NAFEMS World Congress 2019 – Paper Submission Get the Data … · 2019-07-12 · NAFEMS World...
Transcript of NAFEMS World Congress 2019 – Paper Submission Get the Data … · 2019-07-12 · NAFEMS World...
NAFEMS World Congress 2019 – Paper Submission
Get the Data Right for Effective Multidisciplinary
SPDM – Making the Case for a Tool-Independent
Unified Data Model
Malcolm Panthaki
VP of Analysis Solutions, Aras Corp., USA
Marc Lind
SVP Strategy, Aras Corp., USA
Abstract
Increasing complexities in product architectures is placing a new level of
emphasis on some traditional SPDM challenges. The complexity of effectively
conducting 3-D simulations for various physics scenarios has been
compounded by the necessity to create and manage mixed-fidelity and
multidisciplinary models, and to rapidly conduct large numbers of simulations
in the overall systems engineering process, including on-board software which
often dynamically controls system behavior.
Today, much of the data required to drive multi-physics, multi-fidelity
simulations are specified in the disparate data formats of each of the underlying
multi-vendor tools. These siloed data must be “integrated” manually by the
engineers, with a severe impact on accuracy and efficiency, limiting the
number of simulations that can be performed. Also, when multiple disciplines
such as mechanical, electronics, and software must be considered to simulate
system behavior, the data and processes are exponentially more complex.
The authors contend that a tool-agnostic, unified, requirements-driven,
systems-centric data model is required to best capture these data for simulation
and SPDM, and that this approach has many advantages over a federated
approach to integrate data. The advantages are further emphasized when you
consider the need for intelligent simulation automation that works across
significant design changes and large numbers of product variants in a product
family, and the need to manage a fully-associative Digital Thread across the
entire product lifecycle with support for versioning and configuration
management.
The authors will present two previously published case studies to illustrate the
benefits of this approach for representing and managing the simulation data
and automating complex multi-fidelity, multidisciplinary simulations that
include dynamic controls software.
Get the Data Right for Effective Multidisciplinary SPDM – Making the Case
for a Tool-Independent Unified Data Model
Case Study 1: Analyzing the Structural Thermal Optical Performance (STOP)
of an optical system is a complex, multidisciplinary, manual, and inefficient
process that is prone to human error. In this session, we will review how
optical system design teams at NASA and The Aerospace Corporation have
used the Aras Comet SPDM Workspace to rapidly perform STOP analyses,
including parametric, “what-if” trade studies of their system designs and active
thermal controls software. The case study will compare the efficiency and
robustness of this automated process to the prior manual process. [1], [2]
Case Study 2: Directed-energy laser weapons are complex precision optical
systems being developed by the US Air Force. In this case study, the Air Force
Research Labs wanted to simulate an early design that was being tested in the
lab and was showing aberrant behavior. The simulation is a mixed-fidelity,
multi-physics simulation. For computational efficiency, it was necessary to
combine lumped-parameter systems models for most of the laser system with
3-D models of certain subsystems that required higher-fidelity, using co-
simulation techniques. The simulation process was automated, significantly
increasing the efficiency of the transient co-simulation trade studies. [3]
1. The status quo cannot meet the exponentially-increasing need for
simulation and effective SPDM
Recently, the bottom-line potential of a range of strategic initiatives including
Digital Thread traceability (PLM), Digital Twins for predictive maintenance
and design improvements, multidisciplinary optimization (MDO) and
uncertainty quantification, generative design, and the quality assessment of
additive products, have galvanized the C-level at major global organizations.
At their core, each of these requires mainstreaming simulation automation and
data management to work robustly across significant and unpredictable design
changes and product families that share common functional architectures.
The status quo, with its inefficient, manual, error-prone and siloed SPDM,
driven by a scarce population of simulation experts, cannot meet this
exponentially increasing demand for rapid, timely, and accurate simulation.
Current approaches lead to simulation lagging behind in the product
development lifecycle as simulation data is not available to the enterprise in a
timely manner for rapid decision-making and predictive maintenance.
Furthermore, the closed landscape and approaches of legacy PLM vendors add
additional constraints to the use of multi-vendor tools and the free flow of
simulation data across the enterprise. These legacy PLM vendors sell their own
CAD and CAE tools and have, understandably, created platforms and data
models that best support their own tools, often providing limited or no support
for competitive tools, in-house tools, and their related data. This, despite the
widely accepted fact that their customers use a wide array of tools from a
diverse set of vendors.
Get the Data Right for Effective Multidisciplinary SPDM – Making the
Case for a Tool-Independent Unified Data Model
The desire to automate simulation processes has existed for decades. The status
quo is often scripting/programming-intensive, without the support of a unified
data model, with unsatisfactory results, limited repeatability, and limited ROI.
The ad hoc nature of this approach has resulted in fragmented solutions that do
not work well across the entire design space, are difficult to comprehend and
maintain, and isolated from other enterprise product data and processes.
Since the 1990’s, optimization (PIDO) tools, have provided “process
integration” to automate simulation steps using a black-box approach to the
model data and results. Design changes, essential for any design space
exploration, rely on automatically editing model files without semantic
knowledge of their content. This limits the design change scope that can be
explored, especially at higher (3-D) levels of model fidelity. When the
geometric design of the product, defined by CAD, changes significantly, this
“process integration” technique starts to break down.
Figure 1: Ramifications of continuing the status quo in SPDM
The ramifications (Figure 1) have been borne by end-user organizations and
will intensify as the need for rapid and accurate simulation and SPDM
exponentially increases.
2. SPDM Challenges
The challenges facing effective SPDM (Figure 2) have been driven by the need
to move towards a predominantly virtual testing environment, to support
Model Based Systems Engineering (MBSE) of increasingly complex and
multidisciplinary products, and a changing business landscape that requires
predictive maintenance. Organizations are struggling to overcome these
challenges using current simulation and SPDM approaches and tools.
Get the Data Right for Effective Multidisciplinary SPDM – Making the Case
for a Tool-Independent Unified Data Model
Figure 2: Challenges facing current simulation and SPDM approaches and tools
3. What’s Needed – Intelligent Simulation Automation and Data
Management in an open, scalable, extensible PLM Platform
The authors contend that more effective enterprise-wide SPDM is foundational
to achieving closed-loop traceability with requirements, test results, and design
data, and to support Intelligent Simulation Automation (ISA).
ISA is a fundamentally different approach that works robustly across
significant design changes and across an entire product family, while
supporting the appropriate level of mixed-fidelity modelling, from 0-D through
3-D, across the various physics. Different from the scripting PIDO approach,
the introduction of a neutral unified data model for SPDM in the PLM platform
provides an abstract model which expands the design scope of the automation
processes, enabling analysts to focus on the engineering of the product.
ISA, with its robust simulation automation technology, becomes a foundational
basis for successful implementation of various corporate strategic initiatives
including Digital Twin analysis for predictive maintenance and design
improvements, multidisciplinary optimization (MDO) and uncertainty
quantification, generative design, predictive maintenance, and the quality
assessment of additive products.
We propose the following foundational requirements for effective SPDM.
Requirement 1: Support for Systems Engineering is central to PLM.
All products are systems—their engineering, from concept to deployment and
maintenance, involves complex data and processes, and multidisciplinary
teams. The authors contend that requirements-driven system models must be
the “connective tissue” that binds the multidisciplinary data, including
Get the Data Right for Effective Multidisciplinary SPDM – Making the
Case for a Tool-Independent Unified Data Model
simulation data and processes, making this a critical foundational element of
SPDM (Figure 3). This approach facilitates and even encourages
multidisciplinary collaboration with potentially significant accuracy and
efficiency gains across a global product development organization.
Figure 3: Systems Engineering as the “connective tissue” for all PLM data
Requirement 2: Comprehensive/extensible, vendor-neutral, unified data model.
The authors contend that a comprehensive, highly-extensible, vendor-and tool-
neutral unified PLM data model is required for effective SPDM across the
entire product organization, including the various product disciplines. This
contention is borne out by the case studies in this paper.
Such a data model captures the following aspects of product data and must be
open and extensible to meet the needs of end-users and other tool vendors:
• Product intent through Functional Requirements that cascade to every
level of the product data and drive design decisions, supported by on-
demand “right-fidelity” simulation and testing.
• System Architecture captured by functional and logical models and
System Parameters that control the product design.
• Physical system models (associated with the functional and logical
system models) that support the following:
o Multiple representations per component to support simulation at
multi-levels of fidelity and physics, using a wide array of tools
o Product variants
o Performance, cost and manufacturing metrics
• Operating conditions, including System Constraints.
• Test data (critical for verification and validation for simulation models).
Get the Data Right for Effective Multidisciplinary SPDM – Making the Case
for a Tool-Independent Unified Data Model
• Simulation data (associated with the physical system models), abstracted
away from the data associated with the CAE tools used for the
simulations. The simulation data model supports the following:
o Engineering objects (component representations that capture
engineering functional abstractions such as joints, welds,
contact conditions, etc.)
o Abstract modelling (supports ISA across a product family by
capturing rules based on the functional architecture rather than
the geometry or topology of the product design)
o Simulation automation process abstractions
o Operating environments
o Simulation results, including reports
The open PLM Platform must support the following functions for all the data:
• Configuration management with multi-BOM associativity for variants.
• Change management and revision control.
• IP protection and access control defined across roles, organizational
groups and projects.
• Extensibility of the data model and processes without adverse
consequences for upgradability to newer versions of the PLM Platform.
Such a data model supports collaboration across the organization, breaking
down silos, supporting the seamless flow and consistency of data between
disciplines, providing traceability and up-to-date simulation data and
discussion forums for rapid and effective decision-making. This is what the
authors define as necessary requirements for effective SPDM.
The key business benefits of this fully-integrated, unified data model approach,
from requirements to system models to simulation and predictive maintenance
(the full Digital Thread) are:
• Nimble and effective product development processes, including the
reduction of physical testing.
• Traceability over the entire Digital Thread.
• Reduction of product defects and warranty issues.
• Reuse of old designs to rapidly create validated variants and new designs.
• Foundational support for predictive maintenance procedures.
• Foundational support for the Model Based Systems Engineering
approach across the enterprise.
4. Case Study 1: Analyzing the Structural Thermal Optical Performance
(STOP) of optical systems – The Aerospace Corporation
Analyzing the Structural Thermal Optical Performance (STOP) of an optical
system is a complex, multidisciplinary, manual, and inefficient process that is
Get the Data Right for Effective Multidisciplinary SPDM – Making the
Case for a Tool-Independent Unified Data Model
prone to human error. In this section, we will review how optical system
design teams at NASA and The Aerospace Corporation have used the Aras
SPDM Workspace to rapidly perform STOP analyses, including parametric,
“what-if” trade studies of their system designs and active thermal controls
software. The case study will compare the efficiency and robustness of this
automated process compared to the prior manual process.
In the traditional design process, many engineering disciplines (mechanical,
structures, thermal, optics, electronics, software) are needed to design and
build modern Electro-Optical (EO) sensors. Separate design models are
normally constructed by each discipline engineer using the CAD/CAE tools
and material properties familiar to each discipline. Design and analysis is
conducted largely in parallel, subject to requirements that have been levied on
each discipline, and technical interaction between the different engineering
disciplines is limited and infrequent. Design reviews are also conducted in a
serial manner, by discipline, using PowerPoint snapshots of design and
analysis status. Access to engineering results is largely limited to discipline
specialists because of the education and experience needed to understand the
technical issues, terminology, and computer tools for each discipline.
Using this traditional method, the discovery of sensor-level design issues tends
to occur late in the design process, often after the hardware has already been
built. A more collaborative environment with a unified view of all the
multidisciplinary data is required.
The STOP Team at The Aerospace Corp chose Aras SPDM (then called Comet
Workspace) to conduct integrated Structural/Thermal Optical Performance
(STOP) calculations on complex, space-borne sensor systems [1], [2].
In this case study, the STOP Team was selected by a satellite team at the
Sandia National Labs to better understand the behavior of a space-borne optical
system being tested in a TVAC (Thermal Vacuum) chamber. The STOP
automation process automates the entire analysis from CAD to thermal,
structural and optics calculations, using multiple tools, at different levels of
model fidelity and across the different physics domains, enforcing the
simulation rules encoded by the experts in the simulation template.
Following all the rules of the experts, the CAD model of the multi-lens system
is automatically meshed by the automation process (Figure 4). The thermal,
structural and optical calculations are also performed by the automation
process, with all the files required for analysis being created automatically.
This allows the engineers to modify various parameters, including geometric
CAD parameters, materials, operating conditions, etc., and rerun the STOP
calculation automatically, in a fraction of the time it used to take.
Get the Data Right for Effective Multidisciplinary SPDM – Making the Case
for a Tool-Independent Unified Data Model
Figure 4: CAD and meshes of the optical system – satisfying all mesh quality metrics
With the traditional approach, with the data being transferred manually, a
single iteration of the STOP analysis could take weeks to complete.. With this
new method, engineers could perform multiple analyses in a day, constrained
only by the amount of time it takes to run the numerical simulations.
Furthermore, the process is enforced consistently each time, the data are
accurately transferred from tool to tool (the workspace deals with unit and
coordinate system transforms automatically) and all the manual, error-prone
steps are removed.
In this case study, the Aerospace STOP Team was able to quickly validate the
simulation models using test data (Figure 5).
Figure 5: Validation of simulation models using TVAC Chamber test data
The validated automation process was then used to run various simulations to
better understand the behavior of this optical system. Thermal soak tests
confirmed that the models were behaving accurately, and these were followed
by more complex calculations, subjecting the optical system to a series of
Get the Data Right for Effective Multidisciplinary SPDM – Making the
Case for a Tool-Independent Unified Data Model
periodically varying thermal cycles designed to simulate the transient
environment that the system would see in orbit. This introduced the need to
add active thermal controls algorithms to the automation process to control the
temperatures at various set points within the optical system.
These integrated STOP analyses, using validated models, provided many
insights into the physical behavior of the system, which in turn allowed the
engineers to program the controls algorithms that are needed to keep the
system working correctly over a wide range of transient thermal operating
conditions during orbit. The engineers concluded [1] that this simulation
automation workspace was used successfully and efficiently to “validate an
unconventional thermal controls approach for maintaining the focus of the
visible channel of a flight payload over its expected thermal environment”
(Figure 6).
Figure 6: Integrated STOP analysis validated focus control effectiveness
Traditional approaches would have taken much longer (going from weeks for a
single iteration to less than a day) and would not have resulted in the accuracy,
consistency and deep physical insights seen using the new automation platform
and approach. The team concluded that “the savings in cost and schedule were
substantial, given that six different integrated STOP analyses were required to
complete the work.”
The unified data model allowed the multidisciplinary team to collaboratively,
within a single environment, view all the data associated with the system,
regardless of the tools used to perform the calculations, promoting “systems
thinking” among the various discipline experts. The expert in a given discipline
had a complete view of the system and the effect of changes to the overall
system performance. This, Dr. David Thomas, a system engineer and the team
leader concluded, was perhaps the most beneficial aspect of working within
this integrated environment—breaking down the silos between the experts,
tools and data, and giving the entire team a much better understanding of the
trade-offs.
Get the Data Right for Effective Multidisciplinary SPDM – Making the Case
for a Tool-Independent Unified Data Model
5. Case Study 2: Understanding the aberrant behavior of Directed-
Energy Laser Systems – Air Force Research Labs.
Directed-Energy laser weapons are complex precision optical systems being
developed by the US Air Force. In this case study, the Air Force Research
Laboratories wanted to simulate an early design that was being tested in the lab
and was showing aberrant behavior, in order to better understand the behavior
and sensitivities of the system.
Modelling laser weapon systems involves combined interactions among
structures, thermal, and optical effects, including ray optics and wave optics,
software controls, atmospheric effects, target interaction, computational fluid
dynamics, and spatiotemporal interactions between the laser light and the
medium. A variety of general-purpose commercial and special-purpose in-
house tools and techniques have been developed that address different parts of
this problem, but these tools are not integrated, and each require their own
experts to operate accurately. Furthermore, working with all these tools to
perform a single simulation results in siloed, disparate mounds of data that
must then be “integrated” by the engineers to extract real knowledge about the
system being simulated.
The simulation is a mixed-fidelity, multi-physics simulation. For
computational efficiency, it was necessary to combine lumped-parameter
systems models for most of the laser system with 3-D models of certain
subsystems that required higher-fidelity, using co-simulation techniques. The
goal was to automate the simulation process, significantly increasing the
efficiency of the transient co-simulation trade studies. Aras SPDM was chosen
to conduct the multi-fidelity calculations on the complex laser systems. [3]
The simulation automation platform, with its single unified data model that is
able to capture component definitions across all the required levels of
fidelity—from lumped-parameter systems models to 3-D CAD/mesh-based
representations—and the existing connectors to the various math and finite
element tools that were needed, was ideally-suited to address these challenging
modelling requirements. The team at Comet Solutions worked closely with
TimeLike Systems to connect their wave optics systems engineering tool,
WaveTrain™, with the automation platform. The goal of the project was to
demonstrate an effective mixed-fidelity Model-Based Systems Engineering
(MBSE) environment for the analysis and design of laser weapons systems. If
successful, the team was tasked with analysing the system that was producing
aberrant behavior in the test laboratory to better understand the physics, so a
more robust design could be achieved.
The laser system was not able to maintain a high-power focused beam on the
target. Instead, the target maximum intensity quickly degraded, defeating the
purpose of the device (Figure 7).
Get the Data Right for Effective Multidisciplinary SPDM – Making the
Case for a Tool-Independent Unified Data Model
Figure 7: Laser System Degradation of the Target Max Intensity
The engineers suspected that an optical element (MATRIX ASE, Figure 8) was
heating up and deforming, resulting in the diffusion of the primary laser beam.
Figure 8: MATRIX ASE optical element
The suspected optical element needed to be simulated in 3-D to compute the
structural deformations due to thermal gradients, while it was adequate to
simulate the rest of the laser system using lumped-parameter systems models.
The results of the simulation were validated by the test observations, predicting
the diffusion of the primary beam on the target surface (Figure 9).
Figure 9: Target Beam Intensity map diffusing over time
The automated simulation process (Figure 10), automatically running multiple
tools such as Thermal Desktop (transient thermal), Nastran (deformation),
CODE V (ray optics) and WaveTrain (wave optics), was able to execute the
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Get the Data Right for Effective Multidisciplinary SPDM – Making the Case
for a Tool-Independent Unified Data Model
co-simulation process efficiently and accurately, capturing the component
representations at the required levels of fidelity, and automatically generating
all the input files required to run the various tools.
Figure 10: Automation template for analysing the laser system
This new approach, with its unified data model and intelligent automation
processes, proved to be effective, rapidly giving the engineers a clear
understanding of the underlying physics processes driving the complex
multidisciplinary laser system. These insights were required to create a more
effective design of the laser system.
6. References
[1] Jason Geis, David Thomas, et.al. (2011), Concurrent Engineering of an
Infrared Telescope System, SPIE Proceedings, August 2011.
[2] Jason Geis, David Thomas, et.al. (2009), Collaborative Design and
Analysis of Electro-Optical Sensors, SPIE Proceedings, August 2009.
[3] Malcolm Panthaki, Steve Coy (2011), Model-Based Engineering for Laser
Weapons Systems, SPIE Proceedings, August 2011.