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Proceedings of the 2nd SimTech Ph.D. Weekend

“Winged Plans for Visions”

Sollerhaus in Hirschegg, Kleinwalsertal,

16.-18. July, 2010

Ph.D. students and PostDocs of the

Stuttgart Research Centre for Simulation Technologyand Cluster of Excellence “Simulation Technology” (SimTech),

University of Stuttgart

2nd SimTech Ph.D. Weekend University of Stuttgart, 16. – 18. July 2010

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Preface

Prof. Dr. Hans-Jurgen Quadbeck-Seeger, formerly President of the German Chemical Societyand Member of the DFG-Senate, once said:

“Visions are Winged Plans”

Unfortunately, as everybody knows from basic physics, wings which do not have air under-neath them cannot generate a surface force and hence cannot lead to takeoff.

In research, the “air” can be referred to the researchers (here, Ph.D. students and PostDocs)and the “forces” correspond to the research projects. The researchers have to generate theforces required to make the visions come true. One intention of the 2nd SimTech Ph.D. week-end was to support and ensure that all researchers and research projects (“forces”) point inthe same direction. Hence, the Ph.D. weekend’s main goal was the development of

“Winged Plans for Visions”

thereby “filling the visions with life” and making the visions tangible for the young researchersin SimTech.

Editors of the Proceedings / Organizers of the Ph.D. weekend:

Andreas BenzingInstitute of Parallel and Distributed SystemsUniversity of StuttgartEmail: [email protected]

Jan HasenauerInstitute for Systems Theory and Automatic ControlUniversity of StuttgartEmail: [email protected]

Tille Karoline RuppInstitute of Sports and Movement ScienceUniversity of StuttgartEmail: [email protected]

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Contents

1 Intentions and Goals 7

2 Program 9

3 Summaries of the Vision Groups 133.1 From Empirical Material Description towards Computational Material Design 14

3.1.1 Introduction of the Vision . . . . . . . . . . . . . . . . . . . . . . . . . 143.1.2 Research Projects of the Discussion Group . . . . . . . . . . . . . . . 153.1.3 Realization of the Vision . . . . . . . . . . . . . . . . . . . . . . . . . . 173.1.4 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18

3.2 Towards Integrative Virtual Prototyping . . . . . . . . . . . . . . . . . . . . . 193.2.1 Introduction of the Vision . . . . . . . . . . . . . . . . . . . . . . . . . 193.2.2 Research Projects of the Discussion Group . . . . . . . . . . . . . . . 193.2.3 Realization of the Vision . . . . . . . . . . . . . . . . . . . . . . . . . . 223.2.4 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24

3.3 Interactive Environmental Engineering . . . . . . . . . . . . . . . . . . . . . . 253.3.1 Introduction of the Vision . . . . . . . . . . . . . . . . . . . . . . . . . 253.3.2 Research Projects of the Discussion Group . . . . . . . . . . . . . . . 253.3.3 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31

3.4 From Classical Biology to Systems Biology . . . . . . . . . . . . . . . . . . . 323.4.1 Introduction of the Vision . . . . . . . . . . . . . . . . . . . . . . . . . 323.4.2 Research Projects of the Discussion Group . . . . . . . . . . . . . . . 323.4.3 Results of the Vision Discussion . . . . . . . . . . . . . . . . . . . . . 363.4.4 Scenarios: When the Vision becomes Reality . . . . . . . . . . . . . . 393.4.5 Possible SimTech Contributions to Systems Biology . . . . . . . . . . 403.4.6 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42

3.5 From Isolated Biomechanics Towards an Overall Human Model . . . . . . . . 433.5.1 Introduction of the Vision . . . . . . . . . . . . . . . . . . . . . . . . . 433.5.2 Research Projects of the Discussion Group . . . . . . . . . . . . . . . 433.5.3 Results of the Discussion on the Vision . . . . . . . . . . . . . . . . . 473.5.4 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51

4 Evaluation/Resume 53

5 Acknowledgement 55

A List of Participants 57

B List of Contributing Institutions 59

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Chapter 1

Intentions and Goals

In order to describe the intentions and goals of the 2nd Ph.D. weekend, this chapter contains asummary of the “Expose”, that was presented to the Executive Board of Directors of SimTechfor the weekend’s approval.

The following five SimTech visions form the center of the interdisciplinary cooperations withinSimTech:

• The transfer of an empirically dominated material description towards a simulation-based design of new materials with tailored high-end properties.

• A completely virtualised development of prototypes and factories.

• The use of complex and integrative methods in environmental engineering, e. g., withrespect to the handling of greenhouse gases or the global climate change.

• The transfer from the classical descriptive biology towards a systems-biologically dom-inated view on technical and natural systems.

• The summing-up of isolated solutions in the field of biomechanics to an integrativedescription of the human body (overall human model), e. g., with respect to medicaltechnology and crash tests.

The SimTech visions have to be seen as superior goals, and every research project withinthe SimTech Cluster of Excellence contributes to work towards these visions. Due to this –importance of the visions on the one hand, and the contribution of the Ph.D. students viatheir projects on the other hand – this year’s Ph.D. weekend focuses on the SimTech visions.

The goals we pursue with the whole Ph.D. weekend are that:

(1) each and every Ph.D. student is able to relate the own project to the SimTech visionsand identifies him-/herself with the SimTech visions,

(2) everybody recapitulates (with respect to the individual visions) the current state ofthe own project, in which direction one currently is and wants to be going, and whichfuture research project and cooperations are necessary,

(3) cooperation opportunities within SimTech are outlined,

(4) Ph.D. students and PostDocs gain a better overview and further insight in all SimTechvisions (not only the ones they are already contributing to).

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In order to achieve these goals, all participating Ph.D. students assign their own project tothe most appropriate vision in preparation for the Ph.D. weekend. This results in five groups,to which we refer in the following as vision groups or discussion groups. Ideally, also theSimTech-PostDoc who is responsible for the respective SimTech vision participates at thePh.D. weekend and joins to the vision group. During the Ph.D. weekend the vision groupswork on the realization of points (1)–(4). The process is outlined in the subsequent chapter.

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Chapter 2

Program

In order to achieve the goals outlined in Chapter 1 the 2nd SimTech Ph.D. weekend was splitin five parts:

Phase 1: Preparation (before the Ph.D. weekend)

To ensure a fruitful, purposeful and productive discussion during the Ph.D. weekend eachparticipant had to be prepared. Therefore, all Ph.D. students had to write a short summaryof their work and design a short presentation (two slides maximum). The summaries andpresentations had to relate each project to the assigned SimTech visions. Additionally, themost important project goals, with respect to the considered vision, had to be outlined.

As the focus of the weekend are the visions, so far achieved results should not be discussed.

The participating PostDocs prepared a presentation of their vision (see Phase 2) and had toplan work within the vision groups (see Phase 3).

Phase 2: Overview on the SimTech visions

Most Ph.D. students contribute to and are only involved in one of the SimTech visions. For thisreason, most Ph.D. students did not have a clear overview of all other visions before attendingthe Ph.D. weekend. To gain a better insight into all visions, the participating SimTech-PostDocs presented the visions they are responsible for to the whole group of participants onthe first day:

• Filip Sadlo – ”Towards Integrative Virtual Prototyping”

• Sergey Oladyshkin – ”Interactive Environmental Engineering”

• Steffen Waldherr – ”From Classical Biology to Systems Biology”

• Nils Karajan – ”From Isolated Biomechanics Towards an Overall Human Model”

These presentation formed the basis of the discussions within the vision groups. Furthermore,this introduction were seen to inspire the Ph.D. students to identify themselves as part of thegreater whole.

Phase 3: Work within the vision groups

On Day 2, the whole group split up into the five vision groups. Within the vision groups, atfirst each Ph.D. students presented his/her project with respect to each vision thereby using

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the prepared presentations. These short presentation helped the Ph.D. students in gainingmore insight into other projects, afterwards allowing for a more productive discussion andpointing out new links and contact points between different projects.

Based on the short presentations, each vision group jointly worked “on” the respective vi-sion and more precisely elaborated them. Ideas for the realization of the goals resulting fromthe vision were collected. The central points were possible cooperations, starting-points, ap-proaches, and continuative projects during and after the end of the current project phase.The discussion was moderated by the attending SimTech-PostDocs.

After collecting different approaches and ideas, the most promising ones were selected (ac-cording to the perspective of Ph.D. students and SimTech-PostDoc) and analyzed/discussedin greater depth. Storyboards, workflows as well as structures were pinpointed and fictitiousmilestones were defined. Finally, the results of the discussion were preliminarily summarizedand a short presentation was prepared.

Phase 4: Presentation of results

On the third and last day of the Ph.D. weekend, the vision groups had the opportunity topresent the results of the discussion. The objective was to show all participants how the worktowards each vision was approached and rendered more precisely and how all Ph.D. studentscan be integrated in shaping the visions. Furthermore, each vision group obtained feedbackand inspiring ideas/comments from the other groups.

Phase 5: Postprocessing (after the Ph.D. weekend)

To ensure that the results and ideas generated during the weekend are not lost, the visiongroups were asked to prepare a summary. These summaries were written after the weekendand can be found in Chapter 3.

The detailed program of the 2nd SimTech Ph.D. weekend is depicted in Figure 2.1.

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AGENDAFRIDAY SATURDAY SUNDAY

8:00

9:00 Introduction

10:00

11:00 Coffee break Coffee break

12:00

13:00

14:00Departure from University

15:00 Departure from Söllerhaus

16:00 "Fresh up" and Snack

17:00

18:00 Arrival Söllerhaus

19:00 Arrival at University

20:00

21:00

22:00PHASE I: Short presentations (by PhD-students), Discussion and Developing ideas/sheduling towards visionsPHASE II: (i) preparation of presentations for Sunday, (ii) structural developement of written summary

in five pre-arranged vision-groupsall together

Breakfast Breakfast

PHASE I

PHASE I

Lunch, Leisure time (hiking, swimming, both,...)

Presentation of Vision-Groups I

Presentation of Vision-Groups II

Lunch and hike to the Sonna-Alp

Vision-Presentation (by PostDocs)

PHASE II

Dinner

Get-Together

Dinner

Figure 2.1: Program of the Ph.D. weekend.

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Chapter 3

Summaries of the Vision Groups

This chapter contains the collection of manuscripts prepared by the vision groups after thePh.D. weekend. The main issues addressed in these manuscripts are the:

• definition of each vision

• description of all project contributing towards each vision (e. g. containing informationgoals of the projects, how these goals relate to the vision, and ongoing cooperations)

• specification of the vision

• definition of fictitious milestones

• information about existing contributions of SimTech

• description of linking vision and demonstrator

• collection of possible future cooperations, starting-points, approaches, and continuativeprojects.

The preparation of the manuscripts of the individual vision groups was organized by therespective, responsible SimTech-PostDoc.

The manuscripts are intended to build a basis for future discussions and to collect the out-comes of the Ph.D. weekend.

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3.1 From Empirical Material Description towards Computa-tional Material Design

Mostafa Biglari, MAWI, [email protected] Hildebrand, MECHBAU, [email protected] Molnar, MPA, [email protected] Raina, MECHBAU, [email protected] Rommel, THEOCHEM, [email protected] Rosato∗, MECHBAU, [email protected] Rothermel, DLR, [email protected]∗Responsible SimTech-PostDoc.

3.1.1 Introduction of the Vision

For millennia, materials have been developed through the empirical correlation of processingand properties. In the past decade, the numerical implementation of material science princi-ples and the integration of different models on different scales within a multi-scale frameworkhave given rise to the possibility of quantitative conceptual design of materials with thelongterm goal to

• design innovative tailor-made materials with optimized functional properties

• through the construction of knowledge-based virtual test laboratories

• that employ efficient numerical methods to bridge both length and time scales as wellas discrete and continuum approaches.

The basis of the desired knowledge-based virtual test laboratories is formed by the bottom-upmultiscale prediction of macroscopic material behavior. This requires a deep and thoroughunderstanding of the relevant multiphysics effects on all scales, including complex phenomenaon several scales, such as highly non-linear hysteresis effects resulting from phase changes,viscous, frictional, defect and also fracture mechanisms, which are often discrete in nature.Furthermore, it depends on the development and numerical implementation of efficient andversatile frameworks combining both discrete and continuum methods and bridging all rele-vant length and time scales. In this context, a key role is played by homogenization methodsthat constitute reliable frameworks for the bridging of scales.Once the multiscale and multiphysics nature of the considered material is sufficiently under-stood and the macroscopic properties of the material can be efficiently predicted from a givenmicrostructure, this opens the possibility to consider the inverse top-down problem and toidentify microstructures that lead to desired macroscopic properties and to thereby designnew, tailor-made materials. Such inverse design relies heavily on optimization and parameteridentification methods and can be supported by material data-bases.An example for materials whose macroscopic behavior crucially depends on multiscale andmultiphysics effects are multifunctional materials. These materials integrally combine at leasttwo properties, one typically mechanical and the other functional (e. g., thermal, electrical,magnetic, optical). As a result, the macroscopic material behavior is governed by the “tun-able” microstructure of the material. Technically highly relevant examples of such materi-als are shape-memory alloys and ferroelectrics. They are extremely useful for the adaptive

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thermo-electromagneto-mechanical control of structures and the hysteresis that governs theirmacroscopic behavior can be optimized by accordingly modifying their microstructural prop-erties.

3.1.2 Research Projects of the Discussion Group

Project 1: Influences of Residual Stresses and Phase Transformations on Mi-crostructural Development

Mostafa BiglariInstitute for Metal Research

The goals of the PhD project is to investigate the influence of internal and external stresses onphase transformations. During phases transformations stresses can rise due to a volumetricdifference between the two phases, which can influence the transformation rate. This inves-tigation will be done by performing Monte Carlo Simulations in which the atoms can jumpfrom lattice sites of the parent phase on lattice sites of the product phase.

Project 2: Configurational-Force-Driven Self-Adaptive Simulations of Fractureand Phase Transitions in Inelastic Solids

Felix HildebrandInstitute of Applied Mechanics

The microstructure of a material and its evolution have a decisive influence on the macroscopicmaterial properties. A profound understanding of the phenomena on the microscale such asphase transformations and their relation to the behavior on the macroscale is hence decisivefor the predictive modeling and the future tailoring of materials with desired properties. Thegoal of the project is the development of thermodynamically consistent variational methodsfor the self-adaptive computational simulation of microstructure formation and evolution. Theresearch combines the concept of dissipative gradient materials, the regularization of sharpinterfaces, relaxation techniques as well as the framework of configurational mechanics. Goalis a profound understanding of the relation of the formation and evolution of microstructureto the behavior on the macroscale to allow for the predictive design of materials with tailoredproperties.

Project 3: Multiscale Simulations of Metals

David MolnarInstitute for Materials Testing, Materials Science and Strength of Materials

The goal of the project is to develop a sequential coupling procedure for linking relevantmicroscopic scales with the macroscopic scale via mesoscopic length scales for the case of pre-cipitation hardening steels such as bcc -Fe containing Cu- precipitates which coarsen duringthe annealing process. For this reason a number of modelling techniques such as Monte Carlo(MC), Molecular Dynamics (MD), Phase Field (PFM), Dislocation Dynamics (DD) as wellas Finite Element (FEM) methods have to be combined in a sequential way using relevanttransfer parameters such as mean precipitate sizes, precipitate size distributions, strength-ening magnitudes or damage mechanical features. PFM and DD are being implemented inorder to simulate the development of larger precipitates. The challenge of this sequentialapproach is to derive appropriate transfer parameters as input for the next higher modelling

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level. These parameters will play a key role for bridging the relevant length and time scalesallowing for numerous parametric studies to better understand the materials behavior and topredict improved materials properties.

Project 4: Computational Modelling of Phenomena in Dynamic Fracture

Arun RainaInstitute of Applied Mechanics

Dynamic fracture of solids is a physical phenomena which leads to the loss of materialstrength, by the presence of cracks in brittle materials or shear bands in ductile materi-als, eventually leading to material failure. Such a phenomena is further complicated by thebranching of propagating cracks into multiple cracks. Theoretical foundations of solids atfailure show notable discrepancies when compared to experimental results due to the crucialrole of dynamic instabilities. The goal of this project is to increase our understanding of thisphenomena appearing when solids undergo dynamic fracture through the incorporation ofeffects from different length scales into the finite element framework, the numerical schemeplanned to be employed at the macroscale. Within the finite element method, the developingdiscontinuities in the displacement field, so called strong discontinuities, of the failing materialare handled by allowing them to propagate through the individual finite elements resultingfor example in the embedded finite element method. This methodology will serve as thestarting point for the macroscopic realization of the computational modeling of phenomenain dynamic fracture

Project 5: Method Development for Tunneling Rate Calculations in High-Dimensional Systems - Applied to Radical Reactions Catalyzed by GlutamateMutase

Judith RommelInstitute for Theoretical Chemistry

The project links the fields of quantum mechanics with the field of molecular dynamics(QM/MM). In this multi-scale approach the smallest procedures investigated in SimTech arecoupled to next larger scale. The electrons of a few tens of atoms (thus 0.5-2 nanometer) areexplicitly described quantum-mechanically. The remaining approximately ten thousand atoms(thus about 10-20 nanometer) are treated with classical force fields. Numerical mathematicsplays a big role in the development of stable and reliable methods. For gaining and analyzingdata, parallel computing architectures in the field of High-Performance-Computing as well asprograms for interactive visualization are essential. Besides the interdisciplinary collaborationwith the department of numerical mathematics, the project also connects biological aspectswith methods and theories of theoretical chemistry and physics. Geometry optimizations tofind quantum transition states in large, high-dimensional systems based on instanton theoryare developed and performed in interaction with electronic structure and molecular dynam-ics calculations. The groundwork to model the computation of reaction rates is provided bystatistical thermodynamics. The project belongs, like most of those in the field of theoreticalchemistry, to basic research. Radical reactions play a big role in chemical synthesis duringtechnical processes. To improve the needed catalysts, one can learn from natural reactions.The efficiency of radical enzymes is hardly understood. In this project the reaction mecha-nism of such an enzyme (adenosylcobalamin-dependent glutamate mutase) is investigated.Understanding which components of the protein are responsible for its catalytic effects is the

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basis for the development of bio-mimetic catalysts. Similar proceedings with other enzymeslead to new production processes of fine chemicals and drugs.

Project 6: Virtual Characterization of Permeable Materials

Thomas RothermelGerman Aerospace Center, Institute of Structures and Design

To characterize and optimism a permeable material in terms of internal fluid transport andheat exchange mechanisms for active cooling purposes, usually extensive material testing isnecessary. The goal of this project is to use microscopic computer tomography (CT) scans toenable the virtual characterization and optimization of materials. A CFD analysis on meshesderived from CT-images shall provide results in real flow velocities and pressure distributionson the microscopic scale in the material. Determination of a material typical RVE and upscal-ing of the results then will yield macroscopic scale material properties for use in engineeringapproximations like Darcy-Forchheimer’s equation for a flow through a permeable material.The simulations will be validated with experimental data for C/C. With the proposed con-nection between results on the microscopic scale and their effects on the macroscopic scale,further analysis of the effects of tailored geometric material properties (like porosity, specificsurface etc.) on permeability and heat transfer in the form of a virtual laboratory is possi-ble. Together with the ability to tune the microstructure of porous fiber ceramics by use ofdifferent process parameters and raw materials, this shall enable a simulation based materialdevelopment, with materials specifically tailored to meet the operation-imposed boundaryconditions.

3.1.3 Realization of the Vision

As can be seen in Section 3.1.1, the realization of the vision “From Empirical Material De-scription towards Computational Material Design” can be decomposed into two major steps:

1. Bottom-up Prediction: The simulation of the macroscopic material behavior basedon a multiscale coupling of all relevant interacting effects.

2. Top-down Optimization: The identification of microstructures leading to desiredmacroscopic properties based on inverse design.

Note that the second step cannot be started out before the first step has been successfullycarried out.

Bottom-up Prediction

Large progress has been made in the area of physically informed material modeling based onmultiscale approaches. However, more research is necessary to fully understand the couplingof all involved scales. In particular, homogenization frameworks have to be further developedand efficiently implemented to bridge all relevant time and length scales and to combinediscrete and continuum approaches for a robust prediction of the macroscopic material be-havior. The pursuit of this ambitious goal demands strong interdisciplinary collaborationsthat combine knowledge and methods from material science, physics, chemistry, engineeringand mathematics.

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Short-term Goal is the development of robust frameworks for the multiscale simulationof existing materials coupling different scales.

Midterm Goal is the development of robust and efficient frameworks for the multiscaleand multiphysics simulation of existing materials coupling different scales.

Longterm Goal is the development of robust and efficient frameworks for the multiscaleand multiphysics simulation also of virtual materials coupling all scales.

Top-down Optimization

Having set up a robust bottom-up multiscale multiphysics model that predicts macroscopicbehavior based on microstructural properties, one can consider the inverse problem and askwhat microstructures are needed to yield a desired macroscopic behavior. The answer to thisquestion can lead to tailor-made, possibly new materials with yet unmatched material prop-erties. However, it requires the solution of highly nonlinear, extremely complex optimizationproblems, possibly supported by large material data-bases. This will again only be possi-ble through a close collaboration between multiphysics modeling, multiscale homogenizationand and inverse design approaches from material science, physics, chemistry, engineering andmathematics.

Short-term Goal is the fine-tuning of individual macroscopic parameters by the modifi-cation of an existing microstructure.

Midterm Goal is the tuning of single-physics macroscopic material behavior by the opti-mization of its microstructure.

Longterm Goal is the optimization of multiphysics macroscopic material behavior by theidentification of microsctuctures and thereby possibly new materials.

3.1.4 Summary

In summary, the vision “From Empirical Material Description towards Computational Mate-rial Design” crucially depends on the robust and efficient bottom-up multiscale multiphysicsmodeling of existing materials. This then allows the successive top-down microstructure op-timization and the design of new, tailor-made materials.The successful pursuit of this ambitious goal is only possible through an interdisciplinary effortthat combines complex multiphysics modeling, efficient scale bridging frameworks, robustinverse design approaches and knowledge based model-reduction techniques from materialscience, physics, chemistry, engineering and mathematics to set up the desired knowledge-based virtual test laboratories.

References[1] K. Bhattacharya and R. D. James [2005], ”The Material Is the Machine”, Science, 307,

53–54.[2] R. D. James [2000], ”New materials from theory: trends in the development of active

materials”, International Journal of Solids and Structures, 37, 239–250.

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3.2 Towards Integrative Virtual Prototyping

Eckhart Arnold, PHILO, [email protected] Fehr, ITM, [email protected] Fischer, ITM, [email protected] Gilbergs, ITO, [email protected] Ionescu, IKE, [email protected] Muckl, VISUS, [email protected] Sadlo∗, VISUS, [email protected]∗Responsible SimTech-PostDoc.


Research, development, and production have undergone dramatic changes in the last decades.One of the most important changes is the shift from the physical to the virtual domain.But although these fields, and also the operation of the resulting products, have profitedfrom this shift, incoherence in the overall process still limits progress and optimization bothin terms of resources and overall performance. Although, not least due to globalization,there is a trend for generalization in the physical world, e.g., increasing standardizationof interfaces and modularization, this is still far from optimal on the virtual side. There,typically isolated simulation models are developed for each application domain and desiredlevel of detail, leading to redundant work and incompatibilities. Another problem is the lowlevel of integration on this side: design, simulation, and evaluation are typically carried outsequentially, further restricting progress and insight.We identified a twofold vision: a tighter coupling of the physical and virtual worlds through-out the complete product lifetime which we call mixed reality product lifecycle, and a tighterintegration within the two worlds. A key component of the former one could be the virtualdata sheet, i.e., physical components and products are accompanied by a respective simulationmodel as a generalization of traditional product specifications. This enables easy developmentin both the physical and virtual worlds by composition of components, freeing the engineersfrom the need of simulation redesign. Key components in the latter vision are virtual re-ality environments combined with integrated design, simulation, and analysis, allowing fora holistic development process including recycling. In the farther future, we expect a newlevel of industrial production and operation in general when this vision meets the vision ofreconfigurable hardware.


Project 1: Philosophy of Science of Computer Simulations: Application Scenarios,Falsification Criteria, Research Designs

Eckhart ArnoldInstitut fur Philosophie

The project pursues the following four central research questions:

1. Application Scenarios: For what kind of questions can computer simulations be used inscientific research? (And what kind of questions cannot be answered with CS?)

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2. Falsification Criteria: How can the validity of computer simulations be tested?

3. Research Designs and Best Practices: How must one proceed in order to obtain tenableresults with computer simulations?

4. Vision: Development of quality criteria for computer simulations that could imaginablyform the basis for the formulation of industrial norms.

These questions are examined by theoretical reasoning (“top down”) and close examinationof the simulation practice in the sciences (“bottom up”). The project is not related to any ofthe SimTech visions in particular, but various connections could be drawn to all of them.

Project 2: Model Reduction in Flexible Multibody Dynamics

Jorg FehrInstitut fur Technische und Numerische Mechanik

For the development of complex systems, e.g. like cars or wind turbines in the developmentprocess a simulation model is used to predict failure and to optimize the construction process.Elastic Multibody System (EMBS) simulation is nowadays one important tool in the virtualprototyping process of complex mechanical systems. One essential step in the simulation pro-cess of EMBS is the reduction of the linear elastic degrees of freedom, see [1], for fast andaccurate results. In recent years, the authors developed a new pre-processor Morembs, see [1]based on advanced model reduction techniques like Krylov-subspaces and Gramian matrix-based reduction techniques. With these techniques, a totally automated pre-processing of theelastic bodies is possible. Furthermore, a priori error bounds or efficient error estimators areavailable. These features make the method especially attractive for optimization and auto-mated calculation of a simulation model from CAD data because during the reduction noadditional input has to be provided [2].During the research project we worked together within the project network PN 6: “Modelreduction, control and real-time simulation.” With Jun.-Prof. B. Haasdonk (IANS) we im-plemented advanced kernel approximation methods from Reduced Basis methods to improveand speedup the model reduction process in elastic multibody systems [3]. With MichaelReiter (IAAS) and Peter Reimann (IPVS) from the informatics department we try to op-timize the work flow and the data handling of data produced in the simulation process ofelastic multibody systems. Right now we are working for a cooperation project “Import ofFE Structures into MKS-Code” with the Automotive Simulation Center Stuttgart (ASCS),where the focus lies in the transfer of the academic research results to the industry.

Project 3: Model-based Shape-error Compensation of Thin-walled CylindricalWorkpieces in Turning

Achim FischerInstitut fur Technische und Numerische Mechanik

In turning of thin-walled workpieces, the final machined parts often do not exhibit the desiredcylindrical shape. This shape errors are due to chucking and cutting forces deforming theworkpiece elastically during the process. In addition, the achievable machining speed andsurface finish is limited by self-excited vibrations which can even lead to dynamic instability.The goal of the project is to address these problems in their entirety. The static or quasi-static deflections of the elastic workpiece are calculated in advance and used in a feed-forward

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control to eliminate the shape errors. To improve the dynamic behavior, a feedback controlbased on a novel adaptronic turning chisel using a piezo-electric actuator is conceived. Thisallows to enhance both surface quality and speed of operation. To accomplish the projectgoals, an efficient, yet precise model needs to be developed and analyzed. This consists ofseveral parts coupled by a model of the turning process and comprises tool and workpiece aswell as the implemented sensors and actuators.In order to obtain a model that is both precise and efficient in terms of computing time, theuse of modern model order reduction techniques is beneficial. Thus a cooperation with theproject “Model Reduction in Flexible Multibody Dynamics” is established.

Project 4: Model-based Identification and Active Suppression of Static and Dy-namic Aberrations in High Performance Optics by Combination of Mechanicaland Optical Simulation

Holger GilbergsInstitut fur Technische Optik

High performance optical systems have extremely high requirements concerning materialproperties of the optical elements, as well as tolerances of optical surfaces and mechanicalmounts. An optical adjustment during the assembly of the objective is not always sufficient.An active adjustment by mechatronic actuators referring to the operating conditions is de-sirable to cancel negative effects of static and dynamic mechanical and thermal effects onthe optical system. A precise identification of the sources of aberrations is important. Asthe disturbing parameters are not completely accessible by direct measurements, an indirectidentification from image data is necessary. A model based solution to this identification prob-lem based on mechanical and optical simulation techniques is investigated. These techniquescan be also used for the design of concepts for reducing aberrations by actively displacing ordeforming optical elements. As this process relies on the precise model of the system, integra-tive virtual prototyping is a desired vision. Without the opto-mechanical model as ’VirtualDatasheet’ the refinement of the process is hard or even impossible.

Project 5: Improving the Reliability of a Long-Term Operated Decision SupportSimulation System for Disaster Prevention

Tudor B. IonescuInstitut fur Kernenergetik und Energiesysteme

The main purpose of this project is to redesign and simplify the management of simulationworkflows given the fact that computer-aided simulation can be used in decision supportsystems for disaster prevention. As an example, consider the simulation of the atmosphericdispersion of radioactive pollutants in case of an accident at a nuclear power plant. In such anevent, there are strict governmental regulations regarding the steps which need to be takenin order to evacuate the strongly affected regions as quickly as possible. In this sense, thedispersion simulation system provides invaluable real-time decision support when it comesto assessing which of the regions surrounding the accident site needs to be evacuated first,based on meteorological conditions and other factors that play a role in the atmosphericdispersion of radioactive pollutants. Since disaster prevention systems are mission critical,fault-tolerance is an important factor which needs to be taken into consideration during

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design, implementation, and deployment of such systems. We therefore investigate the possi-bility of applying classic feedback control theory for improving the fault tolerance of disasterprevention systems.

Project 6: Coupled Simulation of Light and Sound in Complex Environments

Gregor MucklVisualisierungsinstitut

This project focuses on a coupled simulation of light and sound propagation in complex virtualenvironments. Many effects like accurate multiple reflections on surfaces have to be simulatedto achieve realistic results. Light and sound propagation seem to be very different phenomenaat first sight, but quite similar algorithms are used for their simulation. We investigate thesynergy of a coupled simulation due to these algorithmic similarities, for example by reusingdata structures or intermediate results. The handling of complex environments requires theuse of cluster computers which is to be eased by using a newly developed middleware called“SuperGPU”. It distributes computations dynamically on a hybrid CPU-/GPU-cluster andis developed mainly for interactive applications. A graphical user interface should be providedto the users of the framework to ease development by providing support for workflow-baseddesign of applications using that middleware.

3.2.3 Realization of the Vision

During the SimTech PhD weekend our vision group extensively discussed and specified largeparts of the vision. A main outcome was the mixed reality product lifecycle (Figure 3.1) andthe tighter integration, particularly during the design and operation phases of the product.We summarize our results in a set of challenges and project milestones next.

Design

RecyclingIntegratedSimulation Production

Operation

Opt

imiz

atio

n

Model

ProcessOptimization

FailurePredictionRefin

ement

1

Figure 3.1: Mixed reality product lifecycle.

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Key Challenges

One of the key challenges is the achievement of real-time simulations with concurrent designand visualization to exemplify the vision in the first phase. Thereby, a sufficient proximityto real world problems and contexts is required. Since we target cooperation with industry,it is also mandatory to address current needs and the visions in industry, and to providerealistic and appealing content. Therefore, care has to be taken for appropriate definitionand simplification of the exemplary prototype.

Milestones

Clearly, our vision comprises building blocks that have different time scopes. Whereas someintegration aspects of our vision can be achieved with today’s technology, establishment ofstandards, industrial processes, and integration with reconfigurable hardware can not beachieved in the short term. Therefore, the first phase of the vision focuses on integrative andimmersive aspects, investigated and communicated with a prototype for concurrent design,simulation, and evaluation. Prototypes are particularly well suited for research of our visionsince it focuses on behavior instead of state. We have chosen the field of automotive industrybecause it is nowadays relevant with respect to performance, cost, globalization, consumption,and also pollution and recycling. Furthermore, it is a field where we expect for this centuryintense research and significant progress. Mobility has been playing an important role since theindustrial revolution and the market for vehicles is large and competitive. Finally, Stuttgartis an important position in this field. This provides a basis for cooperation with industry andcan additionally support the vision by locality. We identified the following milestones for thenext 5–10 years.

• A first milestone consists of an integrated real-time simulation and design prototype.We plan to achieve this goal by end of 2011 and to obtain feedback from industry forfurther specification of the vision. The prototype can consist of a simple real-time flowsimulation around a simplified model of a vehicle. It will be implemented and operatedin an immersive powerwall environment on a GPU cluster. Dedicated visualizationtechniques will further integrate the environment. Already at this level of the vision wehope for success stories with industry by means of simple case studies. These can thenprovide additional basis for cooperation in the next phase of the vision.

• The next milestone has a twofold objective: incorporation of the feedback from indus-try into the achieved prototype as well as its extension, and further specification ofthe vision. In particular, one goal is to extend the prototype simulation in terms ofcompleteness and accuracy, and an other goal is the achievement of cooperations withindustry. This can on one hand serve as a basis for technology transfer from academiato companies with the involved valuable feedback, at the same time it is the aim toprovide industry with success stories that could serve as a multiplicator for the vision.

• A next milestone could deal with the mentioned “virtual data sheet” and “mixed realityproduct lifecycle” concepts. Since both are highly dependent on decisions on the sideof the industry, it would probably be a worthless undertaking in defining standards,interfaces, or pure recommendations. It is therefore again over prototypes how we planto transport and evolve our vision. This time, the prototypes could implement thetargeted concepts and building blocks in a even more exemplary fashion due to thecomplexity of industrial interaction. A milestone could be an exemplary industrial study,consisting of several supplying companies that work together in automotive industry.

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Again, in an integrative manner, the interplay of research, development and operationin both the real and virtual domains could be modeled, allowing for insight throughexperimentation.

3.2.4 Summary

The presented vision gained substantial profit from the PhD weekend. Since the investiga-tion and realization started only in 2010, it was a most welcome opportunity for discussionabout the specification as well as components and milestones of the vision. In particular, weidentified the “mixed reality product lifecycle” and “virtual data sheet” concepts and foundthe mentioned possibilities for cooperation.

References[1] Fehr, J.; Eberhard, P.: Improving the Simulation Process in Flexible Multibody Dynamics

by Enhanced Model Order Reduction Techniques. In Proceedings Multibody Dynamics2009 - ECCOMAS Thematic Conferences, Warsaw, Poland, 2009.

[2] Fehr, J.; Tobias, C.; Eberhard, P.: Automated and Error Controlled Model Reductionfor Durability Based Structural Optimization of Mechanical Systems. In Proceeding 5thAsian Conference on Multibody Dynamics ACMD, Kyoto, Japan, 2010.

[3] Fehr, J.; Fischer, M.; Haasdonk, B.; Eberhard, P.: Snapshot-based Approximation ofFrequency-weighted Gramian Matrices for Model Reduction in Multibody Dynamics. InProceeding Model Reduction of Parametrized Systems MoRePaS 09, Munster, Germany,2009.

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3.3 Interactive Environmental Engineering

Andreas Geiges, IWS, [email protected] Hlawatsch, VISUS, [email protected] Kosow, SOWI, [email protected] Leube, IWS, [email protected] Oladyshkin∗, IWS, [email protected] Wirtz, IANS, [email protected]∗Responsible SimTech-PostDoc.


Environmental systems are one of the biggest and most important classes of complex dynamicsystems. A dominant aspect consists in the fact that they are spatially distributed and hetero-geneous in their material descriptions and properties. Lacking information about distributedproperties leads to model uncertainties up to a level where quantification of uncertainties be-comes the dominant question in application tasks. Moreover, many environmental systems aredominated by real-time influences of external driving forces because they are open systems.Most common models used for environmental systems need calibration, prediction, design,predictive control and management. All of these tasks are interactive in the sense that theyrequire quick multi-lateral communication between model, system and user. For environmen-tal systems, none of these tasks can be accomplished without joint uncertainty analysis andconsideration of real-time sensor data. These particularities of environmental systems createadditional difficulties for the development of simulation technology. Major steps to successinclude the development of fast but accurate model concepts and simulation tools, real-timecapability and stochastic approaches. We propose to combine all of these aspects in a singleintegrative, transferable and computationally efficient framework that provides the means forall application tasks named above. As Vision Demonstrator we will consider the environmen-tal problem of large-scale industrial CO2 injection into deep geologic formations, which bearsan inherent risk of leakage back into the atmosphere. The current document contains a shortdescription of the five projects connected to the interdisciplinary SimTech Vision ”InteractiveEnvironmental Engineering”.


Project 1: Reverse Engineering in Optimal Design

Andreas GeigesInstitut of Hydraulic Engineering

Focusing on the lack of given information, stochastic modeling is used to model incompleteknowledge. Collecting of site specific data for improved stochastic model calibration is es-sential to maximize prediction quality. Optimal design methods are used to find the mostefficient data collection schemes. The reverse approach to optimal design, newly introducedin the PhD project, is characterized by low computational cost and the flexibility to solvecomplex and non-linear design problems in large-scale systems.The vision of Interactive Environmental Engineering require a very efficient data acquisitionmethodology. The collected data is the main connection with the real environmental systemwhich is used to calibrate the used model. Specially the liability is heavily influenced by the

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amount of data that is collected. In an interactive system this liability has to meet changingrequirements given by the society and of course policy makers that use the data as a decisionbasis. This flexibility does not allow wast computational optimization schemes which maybe so slow to give updated results in the required time period. In addition Optimal Designalso give the connection between the requested liability and the money which need to spendto ensure this liability. Optionally the increase of liability can be monetized which is mayalso interact as input to models for the society and may be key parameter for the modeledacceptance of the whole project.In the demonstrator case of CO2-Storage the handling of risk plays the most important role.On the one hand risk is the main parameter that influence the acceptance of the projectin the society. So the acceptable risk is somehow given as an external parameter to theoptimization. On the other hand modeled risk itself can be influenced via several alternatives.First additional data acquisition allow to improve the model and it prediction quality. So therisk of a wrong prediction decrease. A second possibility is the decrease of the designedstorage capacity. Obviously a smaller amount of stored CO2 produce less risk of a failure ofthe storage facility. So the modeller need to find the tradeoff between increasing costs dueto data acquisition and the decrease of income due to a reduced storage capacity. Cost forconstruction, data acquisition or insurance and income for the storage of CO2 are simulatedusing other modeling tools which state an other interactive global coupling of different models.Optimal Design provide the tools to either optimize both alternative to be most effective andto come up with a optimal balance between alternatives. In a full interconnected model thataccount for all mutual influences, the optimal design methodology need to be fast enough tonot slow down the whole framework.

Project 2: Visualization for Integrated Simulation Systems

Marcel HlawatschVisualization Research Center (VISUS)

The goal of this project is to provide simulation technology users with new visualizationtechniques embedded into an integrated simulation system, which is developed inside projectnetwork 8 of SimTech. Users should be able to create problem specific visualizations bycombining modular visualization components. The research of new visualization methods isfocused on areas which are relevant for simulation technology. This includes vector and tensorfield visualization, uncertainty visualization and techniques for combined and comparativevisualization of data.Uncertainty visualization is of great interest for this vision of interactive environmental en-gineering. The simulations in this area usually depend on unknown or uncertain input pa-rameters. Uncertainty is therefore also included in the resulting data of these simulations.Visualization of these data must include uncertainty to prevent incorrect interpretation ofthe data by the user.Cooperations exist with Katharina Goerlach and Mirko Sonntag from IAAS regardingworkflow technology for visualization workflows, with Markus Ueffinger and Filip Sadlofrom VISUS regarding flow visualization techniques and with Sergey Oladyshkin regardinguncertainty visualization.

For the vision of an interactive environmental engineering, visualization together with theconnected graphical user interface (GUI) has been identified as simulation user interface(Figure 3.2). The reason for this is that simulations usually generate a huge amount of data.

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SIMULATION Visualization

GUI

Simulation User Interface

Figure 3.2: Relationship between simulation, visualization and the user.

Without visualization, i.e. generating a visual representation of the data, the analysis of thedata is more time-consuming or sometimes almost impossible. Hence, visualization establishesthe connection from the simulation respectively the generated data to the user. Typically,visualization tools have some kind of GUI to control the visualization. This GUI offers thepossibility to provide a connection from the user to the simulation.In this context, two types of interactivity can be identified: interactive exploration of theresulting data and interactive control of the simulation. For an interactive exploration of datafast visualization techniques are required. Interactive control of the simulation is only possibleif results from the simulation can be obtained fast enough. Simulations can be acceleratedwith model reduction techniques, which are developed by other members of this vision group.

When we look at the example of CO2 storage the user is interested in the underground CO2

concentration and how it depends on different uncertain input parameters, like the injectionrate or the well position (Figure 3.3). Instead of recomputing the complete simulation it ispossible to do polynomial approximation of the CO2 concentration. The polynomials areobtained by running the simulation only for a few specific input parameter sets. Resultsfor other input parameter sets can then be reconstructed with these polynomials. Moderngraphics hardware (GPUs) can be used for the reconstruction of the scalar field from thepolynomial approximation. The computational power of GPUs allows us to visualize theCO2 concentration in an interactive manner. It is further possible to interactively change theinput parameter set and directly get an visual feedback. Hence, the user has the possibilityto visually explore not only the data itself for an specific input parameter set, but also toexplore the parameter space of the simulation; both in an interactive manner.

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Project 3: Analysing Social Context Complexity of Environmental Simulations(ACCESS)

Hannah KosowInstitute for Social Sciences, ZIRN Interdisciplinary Research Unit on Risk Governanceand Sustainable Technology Development

The goal of this project is to provide a new method to integrate social sciences’ knowledgeinto environmental simulations. This is relevant, because the results of environmental sim-ulation depend on their assumptions regarding (future) social contexts, such as the effectsof population growth, consumer behaviour, attitudes, technological development and poli-cies. These assumptions are particularly important, if environmental simulations are usedto inform political decision makers and managers. Today’s approaches to deal with socialsciences’ knowledge in the field of environmental simulations (ideal types are ’external pa-rameter’, ’scenario and simulation’, ’integration’) all reveal specific strengths and weaknesses.In sum, environmental simulations do not always meet the complexity, the uncertainty as wellas the qualitative nature of social dimensions.We propose Cross-Impact Balance Analysis (CIB), a qualitative, systematic and formalizedsystems analysis to develop consistent scenarios to select and analyse relevant external social,political, economic and technological (real world) parameters of environmental simulations.This might allow better integration of the uncertainty, complexity and qualitative characterof social contexts. Based on literature review and expert interviews, we develop hypothe-ses which we test empirically via case studies. Finally, we develop procedures and routinesspecifically adapted to CIB applications in the field of environmental simulations.To reach these goals, close exchange within the PN9 is established to guarantee the criticaldiscussion of the projects conceptual base. Within the vision group, expert interviews havebeen and will be conducted to learn about the environmental engineering perspective on theissue and to establish cooperation with further modellers from the field to design case studies.Our project focuses the interaction between society and environmental modelling and morespecifically the interaction between environmental modellers and social scientists. We con-tribute to the vision first by providing environmental simulations with context assumptions ofbetter quality; and second by providing a meta-language for the joint work of social scientists

Figure 3.3: Visualization of the underground CO2 concentration. Uncertainty is mapped totransparency to emphasize areas of high certainty.

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and environmental engineers, allowing deeper common understanding of complex dynamicsystems at the social-environmental interface.For example, the storage of carbon is closely linked to social dimensions as e.g. social demand,technological choices, financial aspects, regulation, public opinion, level of acceptable risketc. An integrative environmental engineering simulation model might analyse (long term)future behaviour of a CS site and might have to answer management or policy questions as”Are our decisions robust with regard to possible future changes of social contexts?” WithCIB we can build consistent scenarios of policy alternatives, technology assessment, differentplausible level of acceptable risk, changes in public opinion etc. to embed the CS simulationin comprehensive real world contexts.

Project 4: Joint Data Compression and Model Reduction for Stochastic Subsur-face Models

Philipp LeubeInstitute of Hydraulic Engineering

Compression techniques bride the gap between the simulation user-interface and data ac-quisition. A suitable compression reaches best effectiveness when the response between dataacquisition and simulation user-interface becomes real-time. This, finally, leads to an realtimeinteraction between the model and the real environmental system.Limited access to intrinsic model parameters is one of the major sources of uncertainty in en-vironmental models. Admitting heterogeneity within the parameter media itself even worsensthe accurate description of model parameters inherent necessary to make reliable predictionsof, e.g., contaminants, floods etc. In order to sharpen the reliability of model predictionsprior model parameters (made by assumptions) are updated by measurements (e.g. from siteinvestigation). In other words: the model is calibrated by real data and the uncertainty inmodel parameters is reduced. Time-dependent data (time-series) are most promising in cali-brating models, since they bear plenty of information about the uncertain model parameters.However, they charge a lot of computational costs. Seeking for real-time interaction betweenmodel prediction and real world requires still more computational costs. Hence, we seek for amethod that overcomes the intolerable burden of calibrating with timeseries by mapping theconditioning problem into a sub domain where time-series are compressed to a low numberof characteristic time features. This will drastically boost the computational efficiency, align-ing the time window of update and prediction cost with real-time elapse. Ultimately, thiswill allow the modeler to intervene, on-the-fly, into the modeling process and, hence, fasterprovides the parameter set for predictions towards real-time interactions.In the demonstrator case, the data compression becomes most beneficial when predicting theleakage of CO2 into the atmosphere. Conditioning reduces the risk of CO2 leakage. Condi-tioning on compressed data, moreover, reduces the computational cost in favor of allowing toincorporate more measurement data (this will reduce the risk of CO2 leakage) and makingpredictions faster available. This provides the framework for real-time interaction betweendata calibration, model predictions and real world monitoring.

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Figure 3.4: Reduced model creation process for real-time response enhancement.

Project 5: Model Order Reduction of Parameterized Dynamical Systems usingKernels

Daniel WirtzInstitute of Applied Analysis and Numerical Simulation (IANS)

Project summary Due to more sophisticated simulation models in natural and engineer-ing sciences advanced reduction methods are moving more and more into the focus of manyresearchers and system modelers. Our project is concerned with tackling the difficulties whenhandling large scale, parametrized nonlinear dynamical systems that occur naturally in bio-chemical settings as cell apoptosis simulation, for example. Accessing both the mathematicaland computer science background of Daniel Wirtz the project roughly be divided into twoparts, which are closely interlocked: Development of a comprehensive software framework us-ing current scientific paradigms that allows to simulate and reduce a broad range of dynamicalsystems as well as its integration/embedding into existing model reduction frameworks likeRBMatlab. The other part aims to derive (a-posteriori) error bounds/estimates that directlyinfluence the algorithm design of KerMor and will ensure the quality of the solutions obtained.For validation and test of the work several cooperations throughout the SimTech networksare being established to give access to current models.

Connection to the global vision In environmental engineering most models are dynam-ical systems which also include parameters of different types. Since full simulations take waytoo long to be included in higher-level applications such as parameter optimization, modelorder reduction techniques are also required in this domain. Moreover, having fast reducedmodels with a certain (controllable!) quality allows for a quick evaluation for given parametersand thus contributes to the real-time response with users demanded by the vision. For ourvision demonstrator CO2-Model we can identify possible parameters, which are then passedinto the offline computation that creates a reduced model that fits to those parameters. Later,fast reduced simulations allow for a real-time response for any initially defined parameter;this process is illustrated in Figure 3.4.Besides the possibilities of an enhanced user interaction through increased simulation speedwe identified more aspects which links our project to the vision. We illustrate these using theCO2 example:

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• (Previously) Main Work: Reduced Model Generation

Transform spatial model to dynamical system (spatial discretization, FEM etc)

Perform model reduction with given parameters

Obtain reduced model with specified error tolerance (allows quick evaluation fornew parameters)

• High-Level Applications: Optimization of/for certain parameters, which may involve

Well positions

CO2 input volumes

Soil permeability

. . .

• User interaction: Playing with parameters

Occurrence of Cracks

Exceeding of a-priori thresholds

• Long-term, large scale application: Storage site selection

Support in task of finding the optimal soil composition when optimizing over per-meability parameters

3.3.3 Summary

The presented document represents the summary where five projects are linked together intoone structure. Project 1 is connecting modeling tools with the real world by assuring reverseengineering in optimal design. Project 2 creates an interface between modelers and modelingtools, providing interactive visualization support. Project 3 deals with environmental simu-lations with respect to their social context and analyzes the influence to the human world.Project 4 contributes to the development of modeling tools by providing means of joint datacompression for stochastic subsurface analysis. Project 5 is concerned with model order re-duction of dynamical systems using kernel techniques, thus ultimately allows a fast modelevaluation and response. In summary, the described projects cover and contribute to differentaspects of the SimTech Vision ”Interactive Environmental Engineering”.

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3.4 From Classical Biology to Systems Biology

Christian Breindl, IST, [email protected] Daub, IADM, [email protected] Falk, VISUS, [email protected] Hasenauer, IST, [email protected] Kramer, IST, [email protected] Offtermatt, ISA, [email protected] Simon Schmidt, IST, [email protected] Spath, HI, [email protected] Waldherr∗, IST, [email protected] Weber, IST, [email protected] Winkel, IANS, [email protected]∗Responsible SimTech-PostDoc.


While classical biology has been very successful in determining the components of livingorganisms and the mechanistic interactions between individual components, a transition tosystems biology is required for the predictive understanding of physiological dynamics, i.e. thedynamical processes that ensure functionality of a biological system. This transition criticallyinvolves the construction of dynamical mathematical models for the biological networks on theindividual scales, and their integration across scales from molecular dynamics to biochemicaldynamics on the organ level. In addition, the efficient simulation and computational analysisof these models requires the development of a new methodological framework based on currentsimulation technology.


Project 1: Modeling and Analysis of Uncertain Gene Regulation Networks

Christian BreindlInstitute for Systems Theory and Automatic Control

Genetic regulatory networks are at the core of almost all cellular decision processes. Trying tounderstanding why and how these systems work is an important goal of systems biology andit is also the central question of my research project. Unfortunately, despite their importance,very little is known about these networks. With the help of knock-out or over-expression ex-periments it is often only possible to generate hypotheses about the involved genes and theirqualitative interactions but the exact reaction kinetics can generally not be measured. Thesame holds true for measurements of protein and mRNA concentrations. Therefore, the focusof my research project is the development of mathematical tools for modeling and analyzinguncertain gene regulation networks. These tools are intended to allow for guaranteed pre-dictions about the considered system while explicitly taking into account large uncertainties.Especially questions like ”can a hypothetical model reproduce an observed behavior” or ”howrobust/good/plausible is the hypothetical model” are addressed. The mathematical methodswhich are applied and further developed in my research project reach from combinatorial orBoolean analysis to optimization methods such as semi-definite programming.

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Project 2: Comparison of Modeling in Systems Biology

Markus DaubInstitute for Analysis, Dynamics and Modeling

Apoptosis is one form of programmed cell death. It involves a series of biochemical processes,namely so-called protease signaling networks. The modeling of such processes has been mainlydone by reaction equations leading to systems of ordinary differential equations. The firstaim is to extend the model for the extrinsic pro-apoptotic signaling pathway to a spatiallydistributed system so that the diffusion of proteases is taken into account. We compare thequalitative behavior of the spatially extended system to the space-independent model.The external stimulus of the extrinsic pro-apoptotic signaling pathway is the clustering ofreceptors on the cell membrane. The second aim of this project is to simulate the receptorclustering and to conduct an analytical study of the time evolution of the clusters on largetime scales.Of particular interest are:

• The mathematical model of

– the extrinsic pro-apoptotic signalling pathway in a spatially extended cell

– the external stimulus, namely the receptor clustering, on the cell membrane

• We are using analytical and numerical methods to reach the goals.

• We are currently developing a collaboration with JP Haasdonk. We intend the applica-tion of model reduction methods to the reaction-diffusion system.

Project 3: Visualization and Mesoscopic Simulation in Systems Biology

Martin FalkVisualization Research Center

This project covers simulation and visualization of signal transduction processes on the cel-lular level. Here, effects of molecular crowding, hindered diffusion, and transport with motorproteins along filaments of the cytoskeleton are subject to research. A simplified cellular modelwas established together with project partners from biology. For the near future it is plannedto make the cellular model adjustable regarding the cellular shape and the cytoskeleton tofit experimental data. Furthermore, the visualization is to be merged with the simulationinto an interactive exploration toolkit. The simulation is to be extended to the three tiers ofthe MAPK pathway and a multi-scale model of tissue consisting of several cells. In a secondproject, the simulation model is applied to diffusion of nutrition in a tumor.

Project 4: Modeling and Analysis of Heterogeneous Cell Populations

Jan HasenauerInstitute for Systems Theory and Automatic Control

In many situations of biological relevance individual cells within a clonal population showdifferent responses upon a common stimulus. One example are populations of cancer cellswhere some cells die upon a death signal and others survive.Within this project populations of tumor cells are studied. These population are often highlyheterogeneous as single cells of this population frequently differ in protein expression levels

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and sensitivities with respect to different drugs. To achieve a better understanding of thesignal transduction in a cell population a multi-scale model is developed.This mathematical model describes signaling in single-cells on the basis of mechanistic ordi-nary differential equations, whereas the population is modeled by an ensemble of individualcells. This leads to a partial differential equation governing the population dynamics. Cell-to-cell variability is introduced by differences in protein synthesis rates among individual cells,interpretable as epigenetic differences. The distribution of the synthesis rates is estimatedfrom data obtained by flow cytometry, Western blots and single-cell microscopy.

Project 5: Parameter Identification

Jonas OfftermattInstitute for Stochastics and Applications

In many models for, e.g. material tissues, biological systems, etc., the values of differentparameters are not directly accessible, but play an important role in the simulation process.The identification of these parameters out of experimental or simulated data is in general ahighly elaborate task and leads to inverse problems for differential equations.The goal of this project is to implement and develop efficient methods for parameter identi-fication in differential equations, where only few and noisy measurements are available andwhere the designated parameter set is known to be sparse.To estimate such sparse solutions, we use adaptive refinement strategies, as well as sparsityenhancing regularization priors. To further employ these strategies, we look at the close con-nection between Bayesian Inversion and Regularization Theory. This is done in cooperationwith Jun. Prof. Nicole Radde and their workgroup, from the Institute for Systems Theoryand Automatic Control.

Project 6: Synchronization of Oscillators

Gerd Simon SchmidtInstitute Systems Theory and Automatic Control

Until recently, the main interest in systems and control theory were the system theoreticproperties of single dynamical systems. The improvements in computational and theoreticalmethods as well as computational power allowed to solve sophisticated analysis and controlproblems for single systems. However, increasing demand for detail lead to larger models,which are computationally hard to tackle. Large models often exhibit a natural decompositioninto smaller systems, thus it is proximate to view large systems as network of small subsystemsinstead of one large system. Even though approaches to utilize the interconnection structurefor analysis and control of large systems exist already for some time, see e.g. [2], the focusstayed on properties of the isolated systems or on properties of single feedback loops. Recentdevelopments showed that it is theoretically and computationally desirable to understand thedynamics of large systems in terms of coupled systems. Such a point of view is supportedby the insight that networks of systems and networks of dynamical systems are prevalent innature and technology, see e.g. [3,4].The overall motif for the investigation of coupled dynamical systems is the same as for theisolated systems: determine or modify the evolution of the system with respect to time ina qualitative way. One particularly important point in our case is the connection of thedynamics of the single subsystems with the dynamics of the large system. Many simulationssuggest that the dynamics of a large system are highly structured.

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My research project focuses on one specific type of structured dynamics, namely synchroniza-tion of dynamical systems, especially synchronization of oscillators. The choice of synchro-nization of oscillators is motivated by the theoretically interesting properties of oscillatingsystems and the relevance of synchronization in areas like control, systems biology and neuro-science. The objective is to improve the system theoretic understanding of coupled dynamicalsystems, especially in networks of nonlinear oscillators.

Project 7: History of Simulation 1945 – 2000

Christiane SpathHistorisches Institut

The project explores the history of simulation, especially in astrophysics. Although the focusis on computer simulations, experimental simulations are also a topic of research as a possiblelink between simulation and experiment. I hope to find out whether experimental simulationfurthered the acceptance of simulation as a research tool or whether it was an independentpath. Today, there are many controversies about the validity of computer simulations, forexample in the field of climate research. One of the interesting questions in this project iswhether there are similar controversies in astrophysics for the use of computers or their resultsand if the arguments for and against were the same in the past as they are today.

Project 8: Modeling Secretory Activity at the Trans-Golgi Network in Mam-malian Cells

Patrick WeberInstitute for Systems Theory and Automatic Control

The secretory pathway of the cell is tightly regulated. Lipid-transfer-proteins movekey-lipids from the Endoplasmic Reticulum to the Golgi-Apparatus. These lipids areprocessed within the Golgi- Apparatus inducing changes in its membrane lipid com-position. The changing of the membrane lipids composition influences the organellessecretory activity. The secretory activity is of high interest since special cell lines areused to excrete therapeutic substances. Many proteins and lipids have already beenidentified to interact with each other via interrelated feedback loops [1]. Since the men-tioned protein interactions are highly connected we address this problem in a holisticway using the tools of systems biology. We plan to establish a predictive single cellODE model of the secretory activity for HEK 293 cells expressing an example protein,namely Horseradish-peroxidase (HRP). This project includes: RNA-based perturbationexperiments with HEK 293 cells, Global optimization for parameter estimation, sen-sitivity and identifiability analysis, relative data handling, experimental design, exactmathematical experiment descriptions and uncertainty analysis as well as validation ofthe final model.

Project 9: Modeling and Simulation of TNF-receptor Clustering

Christian WinkelInstitut fur Angewandte Analysis und Numerische Simulation

This project aims to investigate the first steps in TNF induced signal transduction. Therebythe focus is on the onset of ligand/receptor interaction and subsequent formation of sig-nal clusters on the cell membrane which initiate intracellular signals eventually leading to

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Figure 3.5: Systems biology in 2040

cell death. Enhanced knowledge is expected to be acquired from the mathematical analysisand numerical simulations of the models to be developed, leading to a better understandingof the constraints regulating the differential behavior of the two identified TNF receptorswhen binding the ligand TNF. Currently, research efforts are directed towards deriving or-dinary differential equations models in order to characterize the dynamics of generic clusterconcentrations, while in the further course reaction-diffusion models will be developed fordescribing their spatial distribution. Thereby, the numerical simulations of these models willrely on parameters assessed experimentally by the group of Professor Peter Scheurich.

3.4.3 Results of the Vision Discussion

A Look into the Future of Systems Biology

At the beginning we tried to imagine how the field of systems biology could and should looklike in the year 2040. Therefore we first tried to enumerate the most important fields inwhich systems biology will and needs to make significant progress. A summary of key aspectsis shown in Figure 3.5. Several of these aspects are discussed in more detail in the following.

Scientific Understanding, Scientific Results and Longterm Visions

Systems biology is a highly interdisciplinary field. Under the umbrella of systems biology, re-searchers stemming from various fields such as biology, mathematics, engineering, informaticsand may other disciplines try to find answers to the fundamental questions of live. However,systems biology today is not yet an independent and mature discipline and researchers fromdifferent disciplines mostly still restrict themselves to methods from their respective fields.

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We think that, in contrast to the situation as it is today, systems biology will have evolvedinto a mature research area by the year 2040. It will truly integrate methods and approachesfrom the different disciplines and thus allows a more holistic view on biological systems. Inorder to enable such an integrated approach, we think that a new systems theory for biologicalsystems will be necessary. This new biological systems theory will explicitly take into accountthe nature of biological systems and will provide new approaches to deal with their inherentvariety and complexity.An important step toward such a new theory and the growing together of the individualdisciplines is however the development of a common language. Researchers from the differentdisciplines need to be aware of their capabilities and limitations, and the will have to bewilling to communicate with researchers from different backgrounds and continuously learnnew concepts and terminologies.Systems biology, as we envision is for the year 2040, will offer many new and promisingperspectives. Having a more powerful theory, the focus can be shifted from only small andisolated networks toward complete biological systems and the scientific results achieved byan integrated systems biology will lead to new scientific and medical applications. Whilethe final goal of “understanding the origin of life” will probably not be achieved within thenext 30 years, deeper insights into the development of genetic diseases will be gained. Alsodetailed mechanistic models for diseases will be developed which can facilitate the develop-ment of effective drugs and optimal therapies. Eventually, even artificially designed organsmay be possible. However, also ethically more questionable applications such as an “optimallive design” will become possible. The development of systems biology will thus have to beaccompanied by an ongoing ethical and social discourse.

Public Understanding, Social Impact

Besides the pure scientific discourse within a discipline and with neighboring disciplines, animportant challenge for systems biology is the communication of its results and the under-standing of their impact on society. Similar to other branches of life sciences, Systems Biologyaims directly for results that affect everyday life i.e. also the life of people without scientificbackground. Because of the influence on everyday life and the resulting decisions of policymakers it is important to develop capabilities to communicate the results and their impli-cations on a non-scientific level. Beyond the communication of the immediate results to thepublic it is also required to reflect on the possibilities of a better understanding of biologicalsystems through systems biology. This includes technology assessment as well as a generalethical discourse.

Mathematical Models in Systems Biology

The main task of systems biology is the description of biological processes with mathematicalmodels. The general modeling sequence starts with setting up a chemical reaction systems.This system is in general modeled by a system of ordinary differential equations with thelaw of mass action kinetics. Taking into account spatial and/or stochastic effects yields toa system of partial or stochastic differential equations. From an analysis of these equationsone hopes to gain deeper insights into the behavior of the modeled system. This procedurewill surely not change too much in the near future. But nowadays most of these models, eventhe most comprehensive ones, only model a small detail of the organism under consideration.At the very moment there are a lot of very specialized small models, all of them historicalevolved out of even smaller models, action on one scale, looking at one special behavior.

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(For one possibility to overcome this oceanic amount of models see the paragraph aboutmodeling methods.) In future a systems biological model should and probably will handlemany different behaviors and will be applicable to different organisms. A potential example forthe near future would be to model a complete cell with all its signaling pathways and stimuliat the cell membrane. A second, strongly required task is to transcending the different scales.A future challenge – this is a possible challenge especially for SimTech – is to evolve a modelwhich ranges down from molecular level up to the level of organs, bridging all of the scalesin question. This would involve the modeling of single cells with all the processes proceedingin a cell and the interaction between cells. These are all topics different SimTech Projectsare currently working on. Additionally, better tools for model analysis and visualization areneeded, see the paragraph about simulation tools. Summing up the future direction of researchwill be the design of real comprehensive models of general organisms. In order to reach thesegoals the development of new modeling, simulation and analysis tools to yield deeper insightsinto the biological processes occurring on different scales is necessary.

Modeling Workflow in Systems Biology

Speaking about workflows in systems biology, we have to think about a closed complex multi-step process. As described previously, everything starts out with a goal of understanding andpredicting the behavior of a biological process. Given this goal, the individual steps are:

(0) Goal: Understanding and predicting the behavior of a biological process.

(1) Identification of involved components and interactions.

(2) Development of a dynamic process model.

(3) Analysis of model and generation of hypothesis.

(4) Validation of model predictions by experiments.→ If validation is not possible, return to (1).

This four central steps (1)-(4) all have their own key challenges, which have to be overcometo reduce the time required to obtain a reliable, predictive model.In order to improve (1) the identification of components and interactions involved in a par-ticular process, the availability and accessibility of available data has to be improved. Rightnow, there are many different databases, using different data formats, but only few of themcan be accessed directly using modeling software. To speed up the model development and toensure that no important data are missed, it is crucial to develop easy and efficient interfacesbetween databases and modeling tools.On the other hand, this requires the usage of standardized modeling approaches during (2)the development of a dynamic process model. If the models are not standardized it will becomplicated to allow for an automated integration of data from different data sources. Addi-tionally, the usage of only few alternative modeling approaches may allow for the integrationin a single software package.The standardized modeling approaches will also simplify (3) the analysis of model and gener-ation of hypothesis. As the number of modeling approaches is limited, we only has to developanalysis methods for these modeling approaches. This saves time and furthermore allows forthe definitions of minimal analysis standards, e.g. with respect to the predictive power of themodel.For (4) the validation of model predictions by actual experiments, a close link between mod-elers and experimentalists is required. In particular, the experimentalists have to be able

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to suggest feasible validation experiments, which are then analyzed with respect to thereinformation content.It is obvious, that the modeling workflow is highly complex and contains a lot of dependencies.Nevertheless, SimTech can help achieving this goal. The strong background in simulationtechnology could be employed to start developing standardized modeling approach rangingfrom particle simulation to descriptions employing partial differential equations. Also thedevelopment of methods from different data sources in several model class is within the scopeof the SimTech research. Furthermore, the background in workflow optimization could beemployed to streamline the individual processes, e.g. the data integration from many datasources.

3.4.4 Scenarios: When the Vision becomes Reality

To show which effects the realization of the vision in systems biology could have on peoplein their every-day lives, we developed different scenarios:

• Health care and the doctor/patient interaction

• Health insurance

• Lieschen Muller – systems biology in the average person’s life

• Changes in education

Exemplarily, two of this scenarios are described here.

Scenario 1: Health Care in 2040

Having a look at the possible real world impacts of systems biology, one important aspectis the development of health care. Here the insights gained by research on the developmentof diseases may lead to better adapted procedures in medical treatment as the followinghypothetical scenario of patient/doctor interaction in the year 2040 may illustrate.The first step in the treatment of a sick person will in most cases be a pre-diagnostic one.This involves automated measuring of specific medical factors and taking samples in new pre-diagnostic care centers. Based on these collected data the interaction with an expert systemfor diseases and disease models provides first indications of the patient’s sickness.In a second step an interactive briefing allows a doctor not only to review these results butalso to use them to perform instant analyses and simulations based on the patient specificparameters that can be found on his insurance card and in a patient database. Thereby herelies on profound models developed in systems biology in the last decades that allow beingadapted to individual patients.With these measures taken the physician will be enabled to estimate the necessity of aconsultation as in some cases a personalized treatment plan depending on the individualpatient’s model and history may be sufficient. In the other case further data acquisitionthrough medical imaging may be required to allow performing disease specific diagnostics,online real time analysis of the illness and drug response and with it optimal drug design.Following these therapeutic steps the post-diagnostic stage in both cases consists of a con-stant feedback of therapy progress that can be carried out online via personalized automaticmonitoring being compulsive until a complete recovery of the patient. In addition, all thecollected data will serve to update and optimize the existing patient databases and expertsystems in order to enhance future treatments.

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While the impact of systems biology through in-depth modeling and analysis in every stageof the therapy can be very high it is important to note that throughout the whole process ofthis model/data based therapy it is still a human doctor being aware of the model uncertaintyand its implications that makes the decisions carefully.

Scenario 2: Health Insurance in 2040

The future health insurance structure could be deeply influenced by the achievements ofsystems biology. The scenario is situated in an office where a future citizen is willing to geta health insurance contract from insurance agent. At first the patient is asked whether he iswilling to join the insurances database. There are several patient model classes he may join:full, partial and minimal. A long term cost calculation will be started from his data. Thistakes several parameters into account: a gene analysis, lifestyle and diseases markers. He willbe categorized into a risk group which determines a preliminary price class for his insurance.In the next step he will be scanned for scientific markers of interest. This is related to anexperimental design step. If the patient has interesting markers for diseases or gene defectsthe scientific research value of his data will be predicted. Dependent on how much samples(blood, tissue etc.) he is willing to provide for research his insurance costs can be lowered. Thepatient does not necessarily have to be severely ill to have high information content. E.g. rareminor gene defects or irregularities which are not parameterized and understood yet do notcause observable symptoms. Next the patient is asked what extra packages he wishes for hisinsurance. This may include personalized drug design, spare organ services or parameterizedbiomechanical sport medicine support. Also things which are regarded as ethic critical maycommonly be available in a 2040 extra package like genetic birth optimization, prolongedlife expectancy or cryo conservation. Depending on all factors the price of the insurance isdetermined and the insurance gets access to update the patient’s dynamic body model.Remark: The brainstorming on this scenario caused intensive discussions about ethic issuesand on secrecy obligation in the group. We are fully aware of that but this topic is addressedin other reports, while the focus here is the technical possibilities in 2040.

3.4.5 Possible SimTech Contributions to Systems Biology

The group discussed a timeline for possible contributions of SimTech to the field of systemsbiology, which is shown in Figure 3.6. Some of these contributions are described in moredetail in the following.

Modeling Methods

In the chapter Systems Biology 2040, long term goals of the relatively young science systemsbiology are introduced. In this context, model requirements and their complexity is described.The valuable modeling methods to reach these aims - contributed potentially by the Clusterof Excellence SimTech - to systems biology can be outlined in a short time schedule: Ina first step a systems biology database should be developed. In this database investigatedsignaling networks and measured parameter values are brought together, to help furtherresearchers gain a quick and comprehensive overview of the already existing results. As thisis a more organizational task, the database could be established up to 2013. Of course resultsof the proceeding research can be integrated into the database gradually. In a second stepuntil about 2016, molecular dynamic simulation can be used to determine new parametervalues in the network models. High-performance computing and modern efficient algorithmwill support the complex simulations. Therefore, collaborations with computer science are

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Figure 3.6: Timeline for SimTech contributions to systems biology for the next decade

essential. A further apart task (possible eventually 2019) can be to further specify differentsignaling models, e.g. even for a individual patient, to actually gain profits of the detailedmodeling of system biological processes. An actual long term, but not impossible to reach,vision (possible 2022?) would be to postulate a “Newton’s Law” for Systems Biology, i.e.a simple law describing the complex biological correlations in and between cells. Such anuniversally valid law simplifies the multi-scale modeling of general organisms.

Simulation and Modeling Tool

Simulation and modeling in systems biology are tightly connected as can be seen in the typ-ical systems biology workflow. First, a hypothesis is generated and a model for verification isestablished. This model is used as input for a simulation. Afterward, the simulation results areanalyzed supported by visualization or statistical analysis. Differences between experimentaland simulated results are used to validate and refine the original model closing the workflowcircle. By the coupling of modeling and simulation, parameters can be changed during asimulation run and have immediate effect. Additionally, direct feedback from the simulationcan be used to improve the model or give early hints if the model is not correct. Therefore,it should be possible to adjust or change the underlying model of the simulation in an easymanner. The first steps include the collection of currently used methods and toolboxes insystems biology, for both modeling and simulation. A common interface and a plug-in mech-anism, which are to be developed, make those methods exchangeable. The interface and themethods for multi-scale simulations should be available in one systems biology toolkit by2013. The idea of this toolkit can be extended by using building blocks for both simulationand modeling. This allows the easy creation and modification of new or existing models. Suchbuilding blocks are perfect for being incorporated in the systems biology workflow. Modularsystems will allow the modeling and simulation of coupled systems on different scales, i.e.from atom-atom interactions over cellular interactions to whole organs. In 2016, the firstprototypes for this simulation tool should be available. The next step would be the data

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integration in multi-scale models (until 2019). Data from databases and measurements areused to substantiate the underlying generic model for specific applications, e.g. allowing themodel to adapt to patient characteristics.

Applications

Also on the application side SimTech can contribute in several ways. Already the currentresearch projects cover signaling pathways involved in many different processes and on dif-ferent scales. The current applications range from molecular events occurring at the cellmembrane, over secretion processes, all the way to signaling during stem cell differentiationand programmed cell death.With this broad modeling background and the simulation tools developed in SimTech, thedevelopment of a first holistic single-cell model becomes feasible. Such a first holistic single-cell model could contain the important signaling pathways involved e.g. in proliferation,differentiation, cell cycle and programmed cell death. This would allow the in depth analysisof combinatorial therapy and would be a further step towards insilico therapy and drugdesign. Additionally, the complex problems related to stem cell differentiation control couldbe approached in greater depth.

3.4.6 Summary

The discussion group generally saw systems biology as an emerging research field of highscientific relevance. The developed scenarios show that it has the potential to significantlyaffect society, for example the health care system. Most of these effects have been evaluatedpositively, but it became also clear that public awareness with respect to systems biology isrequired in order to monitor the related changes in society.The SRC SimTech has a good potential to contribute to this emerging field, as simulation andcomputational analysis methods are key answers to the discussed challenges within systemsbiology. As specific possible SimTech contributions to systems biology, we have primarilyidentified the establishment of suitable modeling and simulation tools.

References[1] T. Fugmann, A. Hausser, P. Schoeffler, S. Schmid, K. Pfizenmaier, and M.A. Olayioye.

Regulation of secretory transport by protein kinase D-mediated phosphorylation of theceramide transfer protein. J. Cell Biol., 178:15-22, 2007.

[2] M. Vidyasagar. Input-output analysis of large-scale interconnected systems: decomposi-tion, well-Posedness, and stability. Lecture Notes in Control and Information Sciences,volume 29, Springer, 1981.

[3] S. Strogatz. Exploring complex networks, Nature, 410: 268–276, 2001.[4] A. Barrat, M. Barthelemy, and A. Vespignani. Dynamical processes on complex networks.

Cambridge University Press, 2008.

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3.5 From Isolated Biomechanics Towards an Overall HumanModel

Thomas Heidlauf, MECHBAU, [email protected] Karajan∗, MECHBAU, [email protected] Richter, PHILO, [email protected] Karoline Rupp, INSPO, [email protected] Sprenger, MECHBAU, [email protected]∗Responsible SimTech-PostDoc.


The motivation for the development of an overall human model is based on the desire topredict the interaction of the human body with its environment in order to determine thereaction of the human body to external mechanical and environmental influences. Hence, thevision of the overall human model equally addresses mechanical actions as well as environ-mental factors, which influence the electro-chemical as well as the biological balance of thehuman body. Moreover, it is also possible that electro-chemical, biological and mechanicaleffects are coupled and thus, influence each other. In the case of skeletal muscles, for in-stance, an electric depolarisation resulting from a change in the membrane potentials of thecells is needed in order to trigger a mechanical muscle contraction. Vice versa, the musclesmetabolism may cause muscle fatigue after the muscle contracted several times.Following this, the vision of the overall human model must include and unite already avail-able as well as prospective models in the general fields of biomechanics and mechnobiology,thereby not only bridging several scales of representation but also different physical fields.Consequently, the respective numerical methods, which are applied on each scale, also need tobe connected with methods beyond the classical homogenisation techniques. When this visionis achieved some time in the far future, such an overall human model will also substantiallyhelp to obtain a better understanding of the integral functioning of the human body.Finally, together with a possibility to include patient-specific input data, the overall humanmodel will allow to bring personalised health care to a new level in the future.


In general, all projects within the Project Network PN4 can be clearly associated with thevision of an overall human model, as they are concerned with coupled problems in biomech-anics with application to specific regions of the human body. Unfortunately, not all researchassociates, who are working on the projects of PN4, were able to attend the PhD-weekend.However, in order to present a complete insight into the projects affecting the vision of anoverall human body, all projects are listed with a short description. Note that the researchassociates of projects five and six did not attend the PhD-weekend.

Project 1: Multi-body Simulation of the Human Spine: Calculation of InternalLoads and Multi-scale Coupling with Finite Element Analysis

Tille Karoline RuppInstitute of Sports and Movement Science

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Motivation: One of the major health problems for adults is related to back pain, which isoften caused by a degradation of the intervertebral discs (IVD) or the vertebral bodies of thelumbar spine. Current biomechanical research focuses on the calculation of internal stressesin lumbar vertebrae and IVD as well as on prototyping and testing of spinal implants usingthe finite-element (FE) method. Even though FE models reach a high level of precision, onecrucial inaccuracy still remains. So far, the boundary conditions that represent the loads ofdaily life or accidental movements are usually based on rough estimations.Objective: The goal of this project is to develop a simulation framework such that theregional boundary conditions for isolated FE studies of the lumbar spine can be computedin a holistic fashion. Consequently, a human full body model is used to simulate activities ofdaily life or accidental movements. This will finally allow for an improved investigation ofpathologies as well as an evaluation of different surgery methods and physical therapies.Methods employed: The underlying multi-body model will be designed as a scalable 3-dhuman model based on the 16-segment Hanavan [1] model. Moreover, wobbling masses areintroduced which represent the soft (deformable) parts of a segment, cf. Gruber et al. [2],Schmitt & Gunther [3]. Special attention is drawn on the lumbar spine, which is modelled inmore detail using six rigid bodies for the vertebrae L1 to S1 with novel intervertebral jointsas bushing elements having six degrees of freedom (DOF) in between. In order to obtainrealistic load cases for the detailed FE simulations, it is important that the bushing elementscapture the mechanical behaviour of the IVD in a realistic fashion. Following this, a methodneeds to be sought after which is able to couple the discrete multi-body model with thecontinuous FE model described in Karajan [4]. Furthermore, muscles will be modelled as 1-dmodified Hill-type muscle tendon complexes, cf. Gunther & Schmitt [5] or Schmitt [6]. Inorder to simulate different movement tasks of the human full body model, control algorithmshave to be implemented both for the global model movement and for the local lumbarsegment movement. The isolated movement of the detailed lumbar spine will be controlledwith the λ-model theory, cf. Feldman [7,8]. The rest of the human model is controlled usingvariable joint actuators in the ankle, knee, hip, head-neck joints following the virtual modelcontrol (VMC) theory of Pratt et al. [9].Cooperations in SimTech: There is a strong connection to the biomechanical discussiongroup, which meets on a bi-weekly basis. The members of the biomechanical discussion groupare JP Oliver Rohrle, PhD and his research group Continuum Mechanics and Mechanobiology,Dr. Syn Schmitt and his research group Computer Simulation of Human Movement, andDr. Nils Karajan as the PostDoc who is responsible for the present vision.

Project 2: Using 1D-3D Coupled Skeletal Muscle Models to Realistically Simulatethe Influence of Skeletal Muscle Forces on Lumbar Spine Mechanics

Michael SprengerInstitute of Applied Mechanics (CE)

Motivation: In musculoskeletal models of the (lumber) spine, muscle activity is included– if at all – as a static applied force or a simplified 1-d Hill-type model [10]. So far, full3-d skeletal muscle models have not been included in any spine model. This is mainly dueto the computational complexity of these models, which is needed in order to capture theanatomical and physiological behaviour of muscles using electro-mechanical couplings as itis described in Rohrle [11].Objective: The goal of this project is to overcome this limitation by developing a novelgeometrical multi-scale skeletal muscle model that couples anatomically realistic 3-d models

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with 1-d lumped parameter models. Following this, the challenge will be to derive properand consistent interface conditions between the 1-d and the 3-d structure. Furthermore, the1D-3D model is to be merged with multi-body dynamics simulations to set up a realisticmusculoskeletal model of the lumbar spine.Methods employed: Clearly, a description of the interface that prescribes the couplingbetween the 1-d and 3-d modelling approaches is the key step to achieve the proposed goal.A previous literature review emerged two main philosophies. One approach directly linksphysical quantities that appear in the 1-d and 3-d models by averaging [12]. This can posecomplications for quantities like the displacement or the stress. Another approach is linkedto the conservation of energy [13]. Here, one requires that the energy, which is a product ofthe displacement and the stress, matches at the interface between the models. Alternativeapproaches found in literature are based on the screw theory or domain decompositionprinciples.Cooperations in SimTech: There is a strong connection to the biomechanical discussiongroup, which meets on a bi-weekly basis.

Project 3: The Development of a Micro-structurally-based Skeletal Muscle Modelto Assist in Determining Subject-specific Macro-structural Constitutive Param-eters

Thomas HeidlaufInstitute of Applied Mechanics (CE)

Motivation: Obtaining experimental data to determine the parameters of a computa-tional model is one of the major issues for biomedical engineers or medical practitionersand researchers. In the case of skeletal muscle tissue, it is virtually impossible to carry outin-vitro (or in-vivo) experiments that will provide useful measurements to fit the parametersfor macroscopic constitutive laws which describe the contraction of skeletal muscle tissue.The idea is to use detailed high-resolution computational models of (a few) interconnectedskeletal muscle fibres to compute data that allow the characterisation or specification ofsubject-specific (macroscopic) material parameters, thereby only using in-silico experiments.Due to the enormous discretisation effort on the microstructure, it is important to identifyhow changes of microscopic constitutive parameters (collagen content and distribution)influence macroscopic material behaviour.Objective: The main focus of this project is the development of a micro-structurally-basedskeletal muscle model in order to assist the process of determining subject-specific macro-structural constitutive parameters using in-silico experiments. Subject-specific microstruc-tural data for such high-resolution computational models could come, for instance, frommuscle biopsies. Muscle biopsies are minimally invasive and provide a sample for subject-specific microstructural information on the muscle fibre composition and on the state of theextracellular network of collagen. The inter- and extramuscular connective tissues play a sig-nificant role with respect to the force transduction, cf. e. g., Huijing [14], and hence to activecontractile properties.Methods employed: The proposed research aims to develop a 3-d continuum-mechanicalFE model of a few inter-connected skeletal muscle fibres that represent the microstructureof a mouse muscle. In this context, the pathways of force transmission on the macroscopictissue level will be investigated. The first step in achieving the proposed microstructuralmodel is to generate a continuum-mechanical model of a single skeletal muscle fibre. On itssmallest scale, the 3-d skeletal muscle fibre model is based on phenomenological descriptions

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of mechanical and electro-physiological principles of single sarcomeres and their activationdynamics [15]. The single fibre model is extended to a model of a few interconnected skeletalmuscle fibres based on a high-resolution image data set of an EDL mouse muscle (1µm3 voxelsize). Moreover, the adaptation of a cohesive FE method is proposed to model the relativemovement between the fibres. Thereafter, a sensitivity analysis on different micro-mechanicalquantities will be carried out to improve the understanding of microstructural mechanics.This includes variations in the extracellular collagen distribution and the mechanical prop-erties of the transverse stiffness of the skeletal muscle fibres as well as different activationpatterns. The results of the proposed research will have a direct impact on new and improvedmicrostrucurally based constitutive laws for skeletal muscles. Furthermore, the proposedresearch will provide the basis for many seminal application-driven research projects, such asthe analyses of the impact of muscle dystrophies on exerted muscle force and movement orthe improved understanding of signalling and mechanotransduction, which is often directlylinked to effects such as growth, muscle training, or muscle pain.Cooperations in SimTech: There is a strong connection to the biomechanical discussiongroup, which meets on a bi-weekly basis.

Project 4: Modeling Virtual Realities and Virtual Actualities: Towards the Epis-temic Relevance of Visual Representations in Simulation-based Research

Marianne RichterInstitute for Philosophy

Motivation: Visual representation recently advanced to the position of a central concern ofphilosophy of science, as indicated by Ferguson [16], Perini [17], Carusi [18], Hessler & Mersch[19], and many more. The methodological challenge of defining quality criteria for “intuitiveaccesses” to great amounts of data or information revived philosophical interests in the con-ditions of epistemic relevance for the use of non-verbal symbol systems. However, the centralcategories at stake (such as “epistemic trace” [20]) need to stand the test of contextualisation.Objective: The goal of this project is firstly to revise previous works on the epistemicrelevance of visual representation in the light of case studies, and secondly to lay thegrounds for a better understanding of the conceptual substructure of visual representation,particularly in simulation-based research. The project thereby contributes to the range ofinterdisciplinary research endeavours, which meet the suggestive effects of the ubiquitousscientific image.Methods employed: The project proposes a synopsis of bottom-up (discourse analysis)and top-down (transcendental reflection) approaches towards concrete manifestations of“picture-games” [21]. The methodological constraints bear on the research agenda of theempirical turn, which fosters empirically well-informed and limited-in-scope theorisations ofpractices [22].Cooperations in SimTech: The project is strongly related to the research group ofProf. Thomas Ertl at the Institute of Visualisation and Interactive Systems. In particular,Dipl.-Inf. Martin Falk, Dipl.-Inf. Marcel Hlawatch and Dipl.-Phys. Michael Raschke providedexamples from the field of scientific visualisation. Further discussion associates are Prof. KlausHentschel and Prof. Ortwin Renn and their particular research groups as well as the biome-chanical discussion group which elaborates on the design of an overall human model.

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Project 5: Coupling of Micro and Macro Models for Complex Flow and TransportProcesses in Biological Tissues

Katherina BaberInstitute of Hydraulic Engineering

The present project deals with the coupling of free flow with flow in porous media by focusingon the structure of the processes occurring at the interface. Furthermore, the coupling ofdifferent scales is a key issue, since the interface will be resolved on the microscale, whereasthe surrounding regions are to be described with the help of macro models. The goal is todevelop a multi-scale, multi-physics model which will be a first step towards modelling thecomplex flow and transport processes across the microvascular wall.

Project 6: Growth, Modelling and Remodelling of Biological Tissue

Robert KrauseInstitute of Applied Mechanics (CE)

The project aims at investigating the various stimuli on living biological tissue that initiategrowth, modelling and remodelling and at describing these phenomena on the basis of amacroscopic modelling approach. In this regard, a thermodynamically consistent model forvolumetric growth is developed by recourse to mixture and porous media theories accountingfor the multi-phasic and multi-component nature of living tissues. The growth process isdescribed by distinct mass exchanges between the tissue constituents, where the complexmetabolic mechanisms are governed by non-mechanical quantities.It is expected that the considerations result in a complicated algorithm that has to be includedinto the FE method in order to describe general problems of growth, modelling and remod-elling as well as specific problems like the growth and necrosis of tumours or the metabolismof single organs.

3.5.3 Results of the Discussion on the Vision

Each of the above mentioned isolated projects reveals the necessity to couple distinct methodsstemming from different classical fields of research. In view of an overall human model, thesecouplings need to be extended even further, thereby spanning over several scales of magnitudein length and time. Consequently, these couplings are accompanied by issues addressing thecompatibility of the respective computational methods on each scale. Hence, in order toreduce the gap between isolated models and the overall human model, the focus has to bedrawn on interdisciplinary research that embraces the vast amount of isolated approaches toan integrative multi-scale and multi-physical model network.In the beginning of the discussion about the overall human model at the PhD-weekend, it wasclear to everybody that such a model must include multi-physical routines with a multi-scalecharacter. Quickly, the question emerged what kind of particular couplings are necessaryto fully capture all physical and biological fields in the human body. After compiling somesample applications of selected organs including their respective dependencies concerningthe type of model and its scale, we realised that the classical multi-scale approaches knownin continuum mechanics are not applicable. This is easily illustrated with the example ofbiological processes on the cellular level which often play only a minor role in mechanicalproblems on the organ level. Thus, in view of computational efficiency it is reasonable to onlyinclude the most relevant processes of the small scales that really influence the applications

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on the organ level. But exactly the processes that are omitted for one application may playa major role in answering a different question.Following this, an “overall human model” has to be individually compiled in the contextof its underlying application. A very basic illustration of possible fields that benefit froman overall human model is given in Figure 3.5.3. Herein, general medical questions can beunderstood as the driving field, whereas engineering and sport science are bridging generaltechnical applications with medicine.

Figure 3.7: Possible fields and applications that benefit from an overall human model

As a conclusion, the vision of the overall human model must not be understood as a com-pletely implemented model that bridges all sorts of physical, biological or chemical fields andtheir respective scales. Instead, the vision should be developed in the context of a library orframework that, once established, contains a vast collection of isolated computational modelsas well as information on their possible coupling abilities in terms of input and (homogenised)output data. Such a library will give health professionals, engineers and researchers of relatedfields substantial guidance in the process of compiling a high resolution model that is accus-tomed to their specific needs.

Key Challenges

The key question is what level of detail has to be accomplished in order to answer thequestions which arise from medicine, engineering and sport science or even combinations ofsuch. This will give a first decision whether the mechanical or the mechanobiological aspectsare of primary focus. For instance, the complex influence of the healing mechanisms of a drugin the liver will probably not alter the gross mechanical behaviour of a human body. Hence,one has to understand the overall process that is studied in order to decide what effects haveto be modelled. After this decision is made, it is possible to construct the multi-scale andmulti-physics model such that only the relevant parts are included.Thus, the overall human model is actually a huge library of models, which will give a sugges-

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tion on the models that are required to answer the questions arising from a specific applica-tion. Consequently, the way how the models can interact with each other needs to be definedbeforehand, such that it is possible to directly use them from the library, without havingto define interfaces for data exchange, homogenisation procedures, etc. Moreover, additionalinformation needs to be given on the cross influences of the different scales as well as therelevant quantities on that scale. In the sense of coupled problems between biomechanics andmechanobiology, there is a huge difference in the resulting degrees of freedom. While biologicalmodels are usually applied on the pure cell level, it is obvious that a discretisation of all thechemical processes of a cell in 3-d space on organ level is numerically expensive. Instead, a fewdriving quantities on the cellular level need to be identified which can be discretised in space.

Exemplary Applications

Examples for applications of large-scale (mechanical) models can be found in the ergonomicdesign of tools, workplaces or man-machine interfaces, for instance. Moreover, such modelscan also be used to predict the loads acting on, e. g., hip joints, intervertebral discs, the brain,kidneys or any other organ, during daily life activities or even during traumatic events likean accident, cf. Project 1. The drawback of such large-scale models is that they are usuallynot capable of predicting the damage potential or the consequences of traumatic events onorgans and joints, as the resolution of the model is not sufficient. But in view of computationalcosts, models on large scales benefit from their relatively small number of degrees of freedom(unknowns), as a fine resolution on the smaller scales needs to be discretised, which quicklyleads to an unsolvable system of equations, when applied to large areas of the human body. Inorder to benefit most from the different modelling approaches, they are applied to regions ofthe human body, where they are most suitable. In this context, the generated data from large-scale models is highly valuable for models on smaller scales, which treat organs or joints inan isolated manner, thereby not knowing the correct boundary conditions. By bridging thesescales, it is possible to significantly predict local tissue damage or even design better implantsthat evidentially sustain activities of daily living. Thus, the basic idea of these couplings is touse the results of a coarser model as input quantities for the adjacent finer scale. Vice versa,the coarser models can benefit from homogenised results, which are obtained on the smallerscales in order to represent the integral behaviour of a driving quantity.In addition, such large-scale mechanical models may also benefit from multi-physical ap-proaches. The mechanical joint behaviour of two bones, for instance, is often dominated bythe behaviour of the contacting cartilage layers in between. In this regard, the mechanicaljoint behaviour may be influenced by hydration factors as well as electro-chemical effects, be-cause cartilage is classified as a charged, hydrated porous material which exhibits swelling andshrinking phenomena that alter its mechanical characteristic. Following this, the gross me-chanical behaviour needed in the discrete multi-body model can be bridged with hydrationvalues, for instance, using homogenised results stemming from electro-chemically coupledmulti-phasic FE simulations. Moreover, Projects 2 and 3 reveal how electro-physiologicalprinciples and their activation dynamics influence muscle contraction and thus, the muscleactivities implemented in the discrete multi-body model of Project 1. Coupling Projects 1 to3 together would already lead to a sophisticated human model that demonstrates the basicidea of the vision of the overall human model. Among many other clinically relevant ques-tions, such a demonstrator could, for instance, help to investigate the impact of lumbar spineimplants on the overall posture compared to the “healthy” case without the implant.Another exemplary application, where mechanics is coupled to the electrophysiology of cellsand even to the very small scale of genes is found in heart models. Herein, the muscle con-

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traction is triggered by a cell model that captures the electric depolarisation resulting froma change in the membrane potentials. During a single heart beat, there is a depolarisationwave going through the heart, which causes the heart muscle to contract such that blood ispumped into the cardiovascular system. Obviously, the temporal and spatial distribution ofthat depolarisation wave strongly influences the pumping capacity of the heart. Furthermore,the distribution of the wave over the heart muscle depends on the activity of the respectiveion pumps in the cell walls, which may be hyperactive or inhibited due to certain gene defects.Thus, with the aid of such an integral model of the heart, it is possible to investigate theinfluence of gene defects, which are known sources for a malfunction of the ion pumps, ondiseases like a heart attack or ventricular fibrillation.On the cellular level, the overall human model includes approaches from systems biology.Herein, the chemical reaction system of a cell is usually described with a system of coupledordinary differential equations at a single point in space. This 0-d approach can be spatiallydistributed leading to a huge system of coupled partial differential equations, as it was alreadymentioned in the context of the underlying model to describe the depolarisation wave in mus-cles. However, as the depolarisation wave depends on the ion fluxes through the membrane,“only” information on the electrophysiology is upscaled. Hence, a straight forward up-scalingof the complete mechanisms on the cellular level to the organ level is directly connected toextremely high computational costs. One solution to this problem is the pre-computation ofthese pathways, such that they enter the spatially distributed simulations in an approximatedand, thus, numerically cheaper fashion. On the other hand, a spatial distribution of all sig-nalling pathways within a cell is probably not of interest, as not all have a major influenceon the macroscopic behaviour one is looking at. Following this, it is important to identifythe driving quantities and reduce their spatial distribution using homogenised values. Thesame procedure is applied, when the chemical reaction system of a cell is defined. Obviously,the resulting ordinary differential equations are also approximations of the exact simulationsunderneath using molecular dynamics or even quantum-mechanical approaches.An application, where mechanical transport processes are coupled to systems biology is foundin drug delivery problems and the associated healing process in the organ. Imagine a drug isinjected in a blood vessel. Then, a macroscopic model is needed that describes the advectivetransport of the drug in the cardiovascular system, which must be coupled to Project 5, wherethe interface between the free blood flow and the microvascular wall is defined. Thus, Project5 provides the link how the drug enters an organ like the brain, for instance. From there on,diffusion proceses govern the distribution of the drug in the soft-tissue, until it finally reachesan area that needs to be healed. On this level, systems biology will take over in order todescribe the influence of a certain drug concentration on a cancer cell, for instance.

Milestones

In order to succeed in the development of an overall human model in future, a frameworkneeds to be created, which is able to host the isolated but modularised models as well as avast amount of data. Due to the strong interdisciplinarity of the vision, researchers must beable to upload the results of their projects including the respective computational models aswell as the possible linkage to already existing modules. Besides the complexity in providingthe hardware resources for enough computational power, this is also a challenging task for aworkflow development. Herein, several different codes need to be unified, such that the usersof the overall human model can easily withdraw their fully coupled multi-scale model andwork with it.Within SimTech, only a relatively small application will be created in form of a demonstrator

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of the overall human model. In particular, this will include the linkage of Projects 1 to 3.

Essential Interdisciplinary Research Projects

Due to the interdisciplinary nature of the vision, almost every project network (PN) andresearch area (RA) within SimTech can directly, or at least indirectly, contribute to the visionof the overall human model. Starting with PN8 “Integrated data management, workflow andvisualisation to enable an integrative systems science” and its associated RA E and F, whoseexpertise will be needed in creating the overall framework. In addition, besides the importantrole of the visualisation group in processing patient specific input data resulting from MRI orCT scans, there is also a need for novel visualisation techniques of the created computationalresults.After the framework is created, it needs to be enriched with modularised computationalmodels as well as the corresponding generated (homogenised) data. Obviously, this directlyaddresses PN4 “Coupled problems in biomechanics and systems biology” and thus, RA B, Cand D. Due to the multi-scale character of the overall human model, methods stemming fromPN1 “Multiscale simulation of solids” and PN3 “Simulation of microstructure evolution”must also be included, thereby embracing RA A, B and D. The same holds for PN5 “Multi-phase and multi-physics modelling” (RA B, D, E and F). Moreover, as already mentionedpreviously, PN7 “Dynamics and modelling in systems biology” is also included, wheneversignalling pathways on the cellular level are of interest.In order to connect all these multi-scale, multi-physics and multi-phase models into an inte-grative model, PN6 “Model reduction, control and real-time simulation” will play a majorrole. Herein, numerically expensive computations are pre-computed and the result approxi-mated using a parametrisation with reduced basis methods or similar approaches. Followingthis, the knowledge of PN6 will help to drastically reduce the numerical effort of the overallhuman model.Finally, PN9 “Foundations for the integrative reflection and evaluation of simulations” isaddressed in order to judge the outcome of the simulations of the overall human model withrespect to their uncertainty. Moreover, ethical concerns may appear due to the nature of thevision, which need to be quantified and judged.

3.5.4 Summary

The PhD-weekend played a major role in creating a better understanding of the presentedvision as well as of the other four visions within SimTech. Due to the interdisciplinary natureof the present vision, all participants of the PhD-weekend appreciated the discussions andthe accompanied presentations of the five visions. It was of particular importance to see howresearchers work in other fields and how the research of a single participant can contributeto create something bigger in the context of the five visions within SimTech.Additionally, the present discussion about the vision of an overall human model clearly revealsthe necessity for interdisciplinary research, thereby underlining the importance of a connectedresearch centre like the SRC SimTech.

References[1] Hanavan, E. P.: A mathematical model of the human body. Technical Report, Aerospace

Medical Division, Ohio 1964.[2] Gruber, K.; Ruder, H.; Denoth, J. & Schneider, K.: A comparative study of impact

dynamics: wobbling mass model versus rigid body models. Journal of Biomechanics 31(1998), 439–444.

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[3] Schmitt, S. & Gunther, M.: Human leg impact: energy dissipation of wobbling masses.Archive of Applied Mechanics EPub (2010), DOI: 10.1007/s00419-010-0458-z.

[4] Karajan, N.: An Extended Biphasic Description of the Inhomogeneous and AnisotropicIntervertebral Disc. Dissertation, Bericht Nr. II-19 aus dem Institut fur Mechanik (Bau-wesen), Universitat Stuttgart 2009.

[5] Gunther, M. & Schmitt, S.: A macroscopic ansatz to deduce the Hill relation. Journal ofTheoretical Biology 263 (2010), 407–418.

[6] Schmitt, S.: Uber die Anwendung und Modifikation des Hill’schen Muskelmodells in derBiomechanik. Dissertation, Universitat Tubingen 2006.

[7] Feldman, A. G.: Functional tuning of the nervous system with control of movement andmaintenance of a steady posture. II: Controllable parameters of the muscles. Biophysics11 (1966), 565–578.

[8] Feldman, A. G.: Once more on the equilibrium-point hypothesis (λ model) for motorcontrol. Journal of Motor Behavior 18 (1986), 17–54.

[9] Pratt, J.; Chew, C.-M.; Torres, A.; Dilworth, P. & Pratt, G.: Virtual model control: Anintuitive approach for bipedal locomotion. International Journal of Robotics Research 20(2001), 129–143.

[10] Quaaid, Z. E.; Arjmand, N.; Shirazi-Adl, A. & Parnianpour, M.: A novel approach toevaluate abdominal coactivities for optimal spinal stability and compression force inlifting. Computer Methods in Biomechanics and Biomedical Engineering 12 (2009), 735–745.

[11] Rohrle, O.: Simulating the Electro-Mechanical Behavior of Skeletal Muscles. IEEE Com-puting in Science and Engineering (2010), accepted.

[12] Formaggia, L.; Gerbeau, J.; Nobile, F. & Quarteroni, A.: On the coupling of 3d and1d navier-stokes equations for flow problems in compliant vessels. Computer methods inapplied mechanics and engineering 191 (2001), 561–582.

[13] McCune, R. W.; Armstrong, C. G. & Robinson, D. J.: Mixed-dimensional coupling infinite element models. Inter. J. for Num. Methods in Engineering 49 (2000), 725–750.

[14] Huijing, P. A.: Muscle as a collagen fiber reinforced composite: A review of force trans-mission in muscle and whole limb. Journal of Biomechanics 32 (1999), 329–345.

[15] Shorten, P. R.; O’Callaghan, P.; Davidson, J. B. & Soboleva, T. K.: A mathematicalmodel of fatigue in skeletal muscle force contraction. Journal of Muscle Research andCell Motility 28 (2007), 293–313.

[16] Ferguson, E. S.: Engineering and the Mind’s Eye. MIT Press, Cambridge MA/London1992.

[17] Perini, L.: The Truth in Pictures. Philosophy of Science 72 (2005), 262–285.[18] Carusi, A.-M.: Scientific Visualizations and Aesthetic Grounds for Trust. Ethics and

Information Technology 10 (2008), 243–254.[19] Heßler, M. & Mersch D. (eds.): Logik des Bildlichen: Zur Kritik der ikonischen Vernunft.

Bielefeld 2009.[20] Rheinberger, H.-J.; Wahrig-Schmidt, B. & Hagner, M. (eds.): Resume des Wissens:

Reprasentation, Codierung, Spur. Akademie-Verlag, Berlin 1997.[21] Scholz, O.: Bild, Darstellung, Zeichen. Vittorio Klostermann, Frankfurt a. M. 2004.[22] Achterhuis, H. (ed.): American Philosophy of Technology: The Empirical Turn. Indiana

University Press, Bloomington 2001.

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Chapter 4

Evaluation/Resume

In order to be able to evaluate the success and to check whether the goals of the Ph.D.weekend had been reached, an evaluation was prepared and completed. All Ph.D. students andPostDocs completed an evaluation sheet containing several questions related to the SimTechvisions. Selected results of the evaluation of these sheets are shown in Figure 4.1-4.3.

Question 1:“Do you think that you have a good overview about the SimTech visions?”

From Figure 4.1 it becomes evident that only few students could commit on having a goodoverview of the different SimTech visions before attending the Ph.D. weekend. This changedduring the weekend, and in particular the percentage of students who thought of having littleabout the visions decreased tremendously.

Question 2:“Can you identify yourself and your research with the SimTech visions?”

With the level of information about the visions, the identification with the visions improvedduring the weekend, too. After the weekend, most Ph.D. students are now able to directlyidentify themselves with the SimTech visions (see Figure 4.2). Before, due to limited infor-mation this was only possible for few Ph.D. students.The increased identification with the visions hopefully yields an improved participations andan additional intrinsic motivation in contributing towards the visions.

Question 3:“Do you know how your project contributes to the implementation of theSimTech visions?”

Although it is very important that each Ph.D. student can identify her- or himself with avision, the identification alone is not sufficient for productivity. Therefore, the organizerswere glad to see, that the Ph.D. students also gain a better understanding how to contributetowards defined goals. Afterwards, almost 73% of the Ph.D. students were confident to knowhow they are able to contribute (see Figure 4.3). This is seen as a crucial step, i. e. to getgoing and to get closer to an implementation the visions.

In the perspective of the organizers, the Ph.D. weekend was a great success. All goals of theweekend have been reached and all participants see themselves more as a part of the whole.

Still, there are many issues left and it also became obvious that an active participation of thePh.D. students in the development and specification of SimTech visions is crucial.

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Figure 4.1: Distribution of answers to the question: “Do you think that you have a goodoverview about the SimTech visions?”.

Figure 4.2: Distribution of answers to the question: “Can you identify yourself and yourresearch with the SimTech visions?”.

Figure 4.3: Distribution of answers to the question: “Do you know how your project con-tributes to the implementation of the SimTech visions?”.

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Chapter 5

Acknowledgement

The organizers of the Ph.D. weekend would like to thank the Executive Board of Directors ofSimTech for the opportunity of organizing this weekend. Furthermore, we thank the GermanResearch Foundation (DFG) for financial support of Ph.D. weekend within the context ofCluster of Excellence in Simulation Technology (EXC 310/1) at the University of Stuttgart.

Thanks also goes to the SimTech Management Team. In particular Annette Hurst and BirgitUnger, who helped organizing the weekend and contributed to the discussions before andduring the weekend.

Also the SimTech-PostDocs played a central roll during. They presented the visions, moder-ated the discussions in the vision groups, and organized in cooperation with the participants oftheir vision groups the manuscripts. Hence, special thanks belongs to the SimTech-PostDocs.

No Ph.D. weekend can take place without Ph.D. students attending and contributing. Wewould like to thank all participating Ph.D. students for enthusiastic and motivated discus-sions and work input, as well as their attendance.

Finally, we would like to thank all participants. It was a great, fruitful and productive time.We enjoyed the discussion and the get-togethers, simply the whole weekend!

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Appendix A

List of Participants

Nr. Name Institute∗ Email1 Eckhart Arnold PHILO [email protected] Mostafa Biglari MAWI [email protected] Christian Breindl IST [email protected] Markus Daub IADM [email protected] Martin Falk VISUS [email protected] Jorg Fehr ITM [email protected] Achim Fischer ITM [email protected] Andreas Geiges IWS [email protected] Holger Gilbergs ITO [email protected] Jan Hasenauer IST [email protected] Thomas Heidlauf MECHBAU [email protected] Felix Hildebrand MECHBAU [email protected] Marcel Hlawatsch VISUS [email protected] Annette Hurst SimTech [email protected] Tudor Ionescu IKE [email protected] Nils Karajan MECHBAU [email protected] Hannah Kosow SOWI [email protected] Andrei Kramer IST [email protected] Philipp Leube IWS [email protected] David Molnar MPA [email protected] Gregor Muckl VISUS [email protected] Jonas Offtermatt ISA [email protected] Sergey Oladyshkin IWS [email protected] Arun Raina MECHBAU [email protected] Michael Reiter IAAS [email protected] Marianne Richter PHILO [email protected] Judith Rommel THEOCHEM [email protected] Thomas Rothermel DLR [email protected] Tille Karoline Rupp INSPO [email protected] Filip Sadlo VISUS [email protected] Gerd Schmidt IST [email protected] Christiane Spath HI [email protected] Michael Sprenger MECHBAU [email protected] Birgit Unger SimTech [email protected] Steffen Waldherr IST [email protected] Patrick Weber IST [email protected] Christian Winkel IANS [email protected] Daniel Wirtz IANS [email protected]

∗A list containing the full names of the institutes can be found in Appendix B.

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Appendix B

List of Contributing Institutions

Nr. Institute Abbreviation1 German Aerospace Center, Institute of Structures and Design DLR2 Institute for Analysis, Dynamics and Modelling IADM3 Institute for Applied Analysis and Numerical Simulation IANS

4 Institute for Materials Testing, Materials Science and MPAStrength of Materials5 Institute for Metal Research MAWI6 Institute for Social Science SOWI7 Institute for Systems Theory and Automatic Control IST8 Institute for Theoretical Chemistry THEOCHEM9 Institute for Visualization and Interactive Systems VISUS10 Institut fur Kernenergetik und Energiesysteme IKE11 Institute of Engineering and Computational Mechanics ITM12 Institute of Applied Mechanics MECHBAU13 Institute of Applied Optics ITO14 Institute of Architecture of Application Systems IAAS15 Institute of History HI16 Institute of Hydraulic Engineering IWS17 Institute of Philosophy PHILO18 Institute of Sports and Movement Science INSPO19 Institute of Stochastics and Applications ISA20 SimTech Management Team SimTech

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