Sevin, IsIEMS Plenary Talk

7/29/2019 Sevin, IsIEMS Plenary Talk

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Large-Scale Simulations in Structural Mechanics: Retrospective and

Prospective Views

by Dr. Eugene Sevin

Plenary Talk to the ISIEMS 14

Seattle, WA 20 October 2011

ABSTRACT

The development of finite element modeling in structural mechanics is traced from its early

beginnings, through the period of aboveground and underground nuclear testing that gave rise

to the underlying experimental database, and the Cold War military requirements that led to

todays large-scale simulation capabilities. Limitations to that capability are explored by means

of a case study seeking to validate ground shock damage modeling of underground tunnels.

Required improvements to both geologic modeling of discontinuities and mechanics of code

implementation are identified.

I INTRODUCTION & HISTORICAL OVERVIEW

Originally I had in mind titling this talk Weapons and Targets as it reminded me of the old jokeabout the difference between Mechanical and Civil engineers: Mechanical Engineers build

weapons. Civil Engineers build targets. I suppose at the time Civil Engineers were meant to bear

the brunt of the joke, but if so, the tables have turned. The bad guys response to the accuracy

of our precision guided conventional weapons is to go underground wherever they can. Thus,

following the law of unintended consequences, our prowess in weapon accuracy ushered in the

era of hard and deeply buried targets which are difficult-to-impossible to defeat with

conventional weapons.

But thats not the subject of my talk. Rather, my remarks will address (1) the origin of critical

elements in computational structural modeling, (2) the limits in current large-scale modeling

capability as I see them, and (3) my view of what the future may hold for improvements in both

code physics and code mechanics.

But to begin at the modern beginning: the ASCE (American Society of Civil Engineers) held its

first structural engineering conference devoted to electronic calculation in 1958, which I


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attended. The late Professor Nathan Newmark, who both inspired and chaired the conference

and was then Head of the Civil Engineering department at the University of Illinois, said in his

introductory remarks, the high-speed computer is more than a new toolits use involves a

revolution

As I said, I was at the 1958 ASCE conference and presented a paper on dynamic column

buckling. The numerical solutions were done on an IBM 650 computer; I carry more computing

power with me today on my iPhone

in methods, concepts and even in education. Earlier that year (1958), Newmark had

published a paper in Civil Engineering magazine, A Revolution in Design Practice (still worthreading today) In which he argued that the real impact of computers on the engineering

profession will be in the direction of implementing more rigorous analysis, not in simply doing

the old things faster. As in so many other things during his lifetime, Professor Newmark got that

exactly right.

The 2

nd

ASCE conference on Electronic Computation was held two years later in September1960. There, Professor Ray Clough presented his seminal paper The Finite Element Method in

Plane Stress Analysis; while this was not the very first paper on the finite element method,

Clough was the first to give the name Finite Element Method1

From the perspective of

- so today we can belatedly

celebrate its Golden Anniversary. Thus, by 1960 the essential computational modeling tool to

advance the art of computational mechanicswas defined (although the name was yet to be

coined).

our technical community, which relies on the FEM to predict structural

failure, the development of a supporting experimental database is crucial. In fact, this began

almost immediately after World War II, largely in the context of U.S. nuclear weapon testing.

The period from 1945 to 1992 saw a mammoth investment by the U.S. in some 1000 nuclear

tests2, atmospheric and underground. Most of these were for weapon development, but there

were 100 atmospheric weapons effects tests and 67 underground tunnel tests during this

period. That led to a greatly improved understanding of airblast and ground shock loading of

aboveground and shallow buried structures, as well as the ground shock response of deeply

buried structures. Many of these tests were well-instrumented scientific experiments that can

still serve us well today3

1 R.W. Clough, Early history of the finite element method from the view point of a pioneer, INTERNATIONAL

JOURNAL FOR NUMERICAL METHODS IN ENGINEERING, 2004. Other innovators who Clough cites as sharing in the

development of the FEM were John H. Argyris, M. J. Turner and O. C. Zienkeiwicz.

.

2United States Nuclear Tests, July 1945 through September 1992, DOE/NV209 (Rev. 14), December 1994

3DTRA maintains an online library (STARS) that includes a complete collection of nuclear and conventional weapon

test reports, as well as individual guides to the nuclear weapons effects test database. However, much of this

material is limited in distribution to U.S. government agencies and U.S. government contractors.


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Cold War operational requirements, with its emphasis on nuclear weapons, provided the

primary motivation for research in computational simulation of the effects of munitions on

structures. At the same time, force protection developments against conventional weapon

threats were gaining prominence within the military services, particularly the US Army. These

relied heavily on field experimentation, which was practical for conventional high explosiveweapons but not so practical for nuclear weapons. The string of US Embassy bombings In the

mid-70s, and especially the October 1983 bombing of the Marine Barracks in Beruit, led to

increased emphasis on structural protection against terrorist-type HE threats within the

military.

In time, terrorist threats, along with continuing targeting concerns for hard and deeply buried

targets, came to replace Cold War requirements. While those interests exist today, the focus on

research and application has turned almost entirely from nuclear to conventional weapons.

The Defense Nuclear Agency (DNA, remnants of which are now incorporated in the DefenseThreat Reduction Agency, DTRA) was responsible for designing and conducting DoDs nuclear

weapons effects tests. At that time, DNA also was the principal funding agency for related NWE

application research within the Army, Navy and Air Force. Under this general R&D support, the

Services extended the experimental database to include conventional weapons effects and a

broad range of structural modeling and response investigations.

Well, thats a very brief historical overview of how we got to where we are today. So where are

we today and what do we recognize as shortcomings in todays structural simulation

capabilities? I dont claim a global view of present day capabilities but I do have a reasonable

appreciation of what the DTRA-supported R&D community can do in the hard target area.

II PRESENT CAPABILITIES & SHORTCOMINGS

We have codes today that are capable of running very large 3D simulations (with millions of

finite elements) that are advertised as able to predict fracture and structural failure under

dynamic loads. These analyses tend to be major undertakings, requiring the largest

supercomputers, and involving considerable time and expense to set up, execute and interpret.

But what is the basis for estimating the predictive accuracy of such simulations? Verification

and validation of these models and software generally is accepted as being essential; however,these processes seldom are well-documented, nor is accuracy in the face of modeling and input

parameter uncertainty often assessed quantitatively.

These views may sound harsh to you, and there may be exceptions of which Im unaware, but

let me flesh out the argument with a recent case study of underground facilities. I think


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overcoming some of the shortcomings evidenced in this study is of general applicability and

points the way to needed improvements in structural modeling capabilities.

Over the 5-year span from 2003-2007, DTRA conducted an analytical and experimental study to

determine the practicality and predictive accuracy of selected high fidelity (first-principle

physics) codes to model the ground shock response of deeply buried tunnels. Significant

emphasis in the design of this study was placed on software verification and model validation.

As I use these terms, Verification is concerned with correct evaluation of the underlying

mathematical models, while Validation

In the DTRA study, code verification was evaluated on the basis of 20 computational problems

of increasing complexity; these problems focused mainly on modeling of joints and other types

of geologic discontinuities. Validation typically involves comparison of prediction withexperiment. Here, the validation database consisted of two so-called precision tunnel tests in

manufactured jointed limestone at laboratory scale and two 3000 lb high-explosive (HE) field

tests in a limestone quarry, one with and one without tunnels. A larger scale tunnel experiment

was planned but did not take place. Instead, a simulation was performed on a portion of the

DIABLO HAWK Underground Nuclear (UGT) structures test bed, of which more later.

deals with how well the models perform for their

intended purposes. Or more simply put, verification asks if were solving the equations correctly

while validation asks if these are the correct equations.

At the outset of the program DTRA asked me to assemble a Modeling Assessment Group (MAG)

comprised of independent subject matter experts to (1) review the V&V planning effort, (2)

assess the programs success, and (3) recommend confidence and limitations in code

application.

Five experienced modeling teams participated in the study, each using its own codes. Each code

can be described as a general purpose finite element solver embodying non-linear, large

deformation, explicit time domain formulations. One of the codes was Eulerian; the others

were either fully Lagrangian or Arbitrary Lagrangian-Eulerian (ALE) codes. Most had adaptive

mesh refinement. One modeling team used the commercial ABAQUS/Explicit code. All of the

codes included a variety of element and interface properties to model the dynamic response of

structures in a jointed rock mass.

The five teams operated independently, more competitively than cooperatively, although

frequent joint meetings were held with the MAG and with an internal DTRA analysis team to

compare interim results. A considerable effort was made to standardize model definitions and

input parameters, and code outputs to facilitate comparison of results. Still, the modeling

teams largely brought their own expertise to finalizing the simulation models.


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I dont have the time to tell you much about the program, which is well-documented4,5,6

The MAG judged the overall accuracy of the best-performing team to be about 30-40 percent in

terms of peak velocities and tunnel damage. The primary source of uncertainty was in assigning

the physical properties of the rock. Also, the prediction of rock failure by all of the teams was

essentially heuristic in nature and an obvious area for improvement.

and

generally available to most in this audience. The bottom line is that while a number of

important lessons were learned, the program fell short of its original validation goals, both

because of limited experimental results and because the modeling efforts proved to be more

complicated and required more extensive resources than originally envisioned. Thus it was notpossible to assess quantitatively the overall accuracy and predictive uncertainty among the high

fidelity codes. However, it was possible to rank order the performance of the modeling teams

based on the intermediate-scale tunnel test.

It also was clear that the skills of the modeling team and their experience in modeling deepunderground structures were integral to overall code performance and accuracy.

III CASE STUDY: LARGE-SCALE SIMULATION

The large-scale tunnel test was intended as the central validation experiment and its

cancellation was a major setback to realizing the goals of the project. Instead, it was decided to

use the results from an existing underground nuclear test (UGT) as a proxy validation data set.

The DIABLO HAWK UGT was conducted in 1978 at the Nevada Test Site (NTS); it was a test oftunnels of various sizes and reinforcement designs in NTS tuff

7

4Final Report of the Tunnel Target Defeat ACTD Modeling Assessment Group, Northrop Grumman Report,

September 2007

, a weak, nearly saturated

sedimentary rock with a highly faulted, irregular 3D geology. This test was selected fromother tests of tunnels because of its well-documented geology, instrumentation, and range of

damage results. While this experiment does not meet usual conditions for validation as the

experimental results are known in advance, the complexity of the test bed and the different

5 Proceedings from the 78th Shock and Vibration Symposium on CD-ROM, Shock and Vibration Information

Analysis Center, 20086

Supplement to the Final Report of the Tunnel Target Defeat ACTD Modeling Assessment Group:

The DIABLO HAWK Simulation, Northrop Grumman Report, February 20097

The NTS is geologically situated in the Basin and Range province, a region characterized by folded and faulted

Paleozoic sedimentary rocks overlain by Tertiary volcanic tuffs and lavas. NTS Tuff is a weak, nearly saturated rock

(unconfined compression strength of about 0.2 kbars). The majority of the faults within the tunnel complex have

very high dip angles and are nearly parallel to the pre-Tertiary rock ridge.


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levels of damage sustained by the test structures made it a very useful and revealing exercise,

nonetheless.

The DIABLO HAWK simulation was carried out by 3 of the 5 original modeling teams using the

same codes and modeling approaches as for the original test simulations. Their results were

sobering and served to curb somewhat the enthusiasm for earlier findings. These simulations

point up modeling limitations of the codes, the ability to characterize complex geologic sites,

and the ability to represent massively discontinuous media. They also provide insight into

desired future code capabilities.

I am going to show you 3 charts of the DIABLO HAWK event: (1) a plan layout of the test bed

showing the region of the simulation [Chart 1], (2) a cross-section of the geology showing

bedding planes and faults as prescribed for the modelers [Chart 2 ], and (3) posttest

photographs showing large block motions in a test tunnel [Chart 3]. Available time doesnt

allow me to say more about the test here, but a sense of the complexity of the simulation canbe garnered from these charts.

Chart 1 DIABLO HAWK Test Bed Showing Damage Levels


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Block motion along a fault

Dip-slip 2; Strike-slip 10Block motion along a bedding plane

3 laterally and 5-10 longitudinally.

Chart 2 Faults and Beds in the GAMUT Model

Chart 3 DIABLO HAWK Post-Test Block Motions Along Faults


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It was anticipated that the modelers would represent the faults and bedding planes as discrete

surfaces capable of transmitting normal and shear tractions according to a Coulomb friction

law. In fact, none of the modelers did so; rather, faults and bedding planes were modeled as

conventional finite elements with Mohr-Coulomb failure surfaces.

The simulations exhibited features that made it difficult to evaluate overall accuracy in a

consistent manner. For example, one teams rock model was much weaker than the DIABLO

HAWK tuff due to a combination of material and element properties and mesh design choices.

This led to large overestimates of displacements in comparison to both the free field

measurements and high speed camera data.

The Eulerian model used by another team reduced the weakening effect of faults and bedding

planes due to averaging of properties at the element level. Consequently, their rock model was

stronger than the DIABLO HAWK tuff and resembled a more homogeneous medium.

The third teams simulation provided the most faithful representation of the specified DIABLO

HAWK test bed geology; still they underestimated damage to the intermediate range

structures, and showed other features not evident in the data.

The primary lesson learned from the DIABLO HAWK simulations was that the modelers didnt

represent media with extensive discontinuities very well. Discontinuities such as faults and

bedding planes, and how they are modeled, can have a pronounced effect on predicted ground

shock, and can influence ground shock energy flow by channeling, and diversion. Regions with

oriented flaws or fractures behave as anisotropic materials that may give rise to highly

directional stress-wave propagation, and to date cannot be accurately modeled as intactisotropic material with equivalent material properties.

The simulation also highlighted that fact that software validation has meaning only in relatively

narrow bounds of application. The same code and modeling team that performed best in the

intermediate-scale HE tunnel test didnt fare nearly so well in the DIABLO HAWK simulation.

This realty needs to be kept in mind when the notion of validated software is bandied about, as

it may have limited practical relevance.

IV NEEDED MODELING IMPROVEMENTS

The DTRA study, rather than validating available software and modeling capabilities,

demonstrated the need for improvements in modeling technology, both in terms of physics

modeling and code mechanics. Requirements for verification and validation merit separate

consideration as does the manner of quantifying uncertainty.


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For the application Ive been discussing, improvements in physics modeling would include

higher fidelity constitutive models for faults and joints, to include material softening, failure

and fracture modeling, scale and rate effects, and history dependence. Because of the

impracticality of characterizing real world geologic sites to the level of detail required by a

deterministic model, there is a need to be able to selectively model joint-sets interacting withintact material inelastically, either explicitly (as for a few well-defined faults) or implicitly by

means of a homogenized representation of massive jointing. The need for probabilistic

modeling also needs to be considered.

DTRA organized a workshop on Ground Shock in Faulted Media in January 2010 that dealt with

a number of such modeling and code improvements. The proceedings of the workshop, which

involved funded studies, are now available from DTRA.

Looking to the future, I believe we will see a much stronger focus on dealing comprehensively

with uncertainties

8

The DOE weapon laboratories have been pursuing a probability risk assessment methodology

(referred to as Quantification of margins and uncertainty, or QMU) under their Science-based

Stockpile Stewardship Program to predict the reliability, safety and security of nuclear

weapons. The relevancy to our community of this technology to estimate predictive uncertainty

remains to be determined.

in both deterministic and probabilistic formulations. Ground shock is anobvious example where knowledge of faults, joint systems, and other inhomogeneities are

typically unknown at the level of detail required by the simulation.

A committee of the National Academies currently is examining practices for verification andvalidation and uncertainty quantification of large-scale computational simulations in several

research communities. Their report Mathematical Science Verification, Validation, and

Uncertainty Quantification is expected late this yeaar or early next.

A more conventional approach is to characterize predictive uncertainty on the basis of

extensive variation of parameter studies; that is, by repeating a simulation systematically over a

broad range of model assumptions and parameter values in Monte Carlo-like fashion. For large-

scale simulations today this probably is unaffordable except in very high-value instances, but it

is something I think that we need to plan for as a regular event. Key to making this a practical

approach will be the ready availability of greatly increased computing power together with

significant improvements in code mechanics.

8Comprehensive uncertainty includes uncertainties in domain knowledge, validation referent, modeling, user

effects and interpretation of results


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Improving code mechanics means significantly reducing problem setup time, running time and

the time to interpret results. Constructing a three-dimensional finite element model of a

complex system of upward of 2-4 million or more finite elements typically is an extremely labor

intensive trial and error effort, and in the DTRA study was a bottleneck of the model building

process. For example, in the DIABLO HAWK simulation Ive discussed, mesh building absorbedmost of the available resources and impacted adversely on the modeling teams ability to fully

exploit the simulation (e.g., through even modest sensitivity studies) and interpret its results.

The end goal would be to provide an integrated tool-chain (mesh generation to simulation to

post-processing) based on a unified results database that requires no translators or manual

intervention to go from one process to the next. For geologic models this should include a

capability to generate a mesh conforming to the geometry of a fault and to use a sliding

interface algorithm to model arbitrarily large differential tangential motions along the fault

surface. Also, XFEM-like technology should be available to model newly generated fractures.

In summary, my view is that while we are capable today of running very large 3D FEM structural

response simulations, we do not yet have a solid basis for dealing with uncertainty of problem

variables and predictive accuracy. Characterizing failure of geologic and structural materials

under dynamic loads needs to progress beyond heuristic criteria. The investment of time and

effort to set up, execute and interpret large-scale simulations needs to be reduced substantially

to where variation of parameter-type studies become practical and routine.

The formality of the validation process and the role of experimentation are less clear to me, and

I suppose will depend on how high a value is placed on the importance of a particular suite of

simulations. However, I do see a need for development of benchmark-type problems and

supporting proof-of-concept experiments that collectively lend credence to the validity of the

simulation tools.

Sevin, IsIEMS Plenary Talk

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