October 2008 Automation components for simulation-based engineering.
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October 2008 Automation components for simulation-based engineering
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Transcript of October 2008 Automation components for simulation-based engineering.
October 2008
Automation components for
simulation-based engineering
October 2008
simulation-based engineering challenges
Use of physics simulation as an integral part of the design process Use of simulation early and often in
the design process Use of simulation to evaluate design
functional objectives Use of simulation to affect design
decisions
October 2008
simulation-based engineering challenges
Increasing complexity Structural Analysis Thermal Analysis Computational Fluid
Dynamics (CFD) ElectroMagnetic Analysis Radiation Analysis Coupled Physics
MEMS Application specific
Turbo-machinery Power generation Combustion engines Chemical Mixing
. . .
October 2008
simulation-based engineering challenges
objectives of physics simulation in the design process are changing Drive reusable Analysis Data
Models from high level requirements through detailed analysis.
Concept to detail design phases
Provide a means to support robust design, systems engineering, functional design and design space exploration for performance investigations.
Provide Analysis data definition to enable capture and reuse of expertise.
October 2008
simulation-based engineering environment
Simulation Driver Frameworks(Robust Design / Design Exploration / Systems Engineering)
Instance-IndependentSimulation Definition
(Analysis Abstract Model)
Simulation Instance Model
Simulation Results Instance
Data
Data Management Frameworks (PLM/SDM)
Rules, Processes, Templates
Object Definition Instance Data
(CAD, Mesh Based, Concept, Non-Geometric)
Instance Based Simulation Data
(Execution Ready/Results Data)
Simulation Models
Simulation Execution Instance Model
Sol
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October 2008
Simmetrix approach
Provide software components to enable automation to generate accurate simulation results applicable to design decisions directly from the current Design Model instance on a repetitive basis through the design process Abstract Model Simulation Model Direct geometry access Automatic mesh generation Automatic generation of run-ready data Results management Adaptive mesh modification
October 2008
analysis abstract model
Instance Independent Simulation Definition
Made up of Four Major Aspects
Physical Characteristics
Solution Specific Definitions
Conceptual Model
Instantiation Rules and
Processes
October 2008
analysis abstract model
Instance Independent Simulation Definition Physical characteristics
Physical constraints and properties that are instance independent
Material definitions/libraries Physical constraints others
Defined / utilized as attributes assigned to a global / nothing Class in the Conceptual Model
October 2008
analysis abstract model
Instance Independent Simulation Definition Solution Specific Definitions
Definitions of the specific problem to be solved Defined as attributes assigned to data objects
(or global class) in the conceptual model Can be expanded to include definition of
derived results (Performance Requirements) calculation
October 2008
analysis abstract model
Abstract (Conceptual) Model Tag based approach
Tags placed on our associated with Object Definition Data Typically string parameters or attributes Tags can be assigned to Assemblies, subassemblies, parts,
features, and explicit Faces Auxiliary file used for Discrete (mesh) data
Benefits Independent of complexity of problem domain Can work with object definitions from multiple sources
Limitations & Issues Requires creation and maintenance of persistent tag data Difficult to maintain tags at anything less than a feature
(ie a-pillar flange )
Applicable to a limited domain of problems
October 2008
analysis abstract model
Abstract (Conceptual) Model Tag based approach
Simple Heat Exchanger example CFD simulation defined once and alternate designs
instanced first design could be in CAD system A and second design in
CAD system B Boundary Layer meshing and wall boundary conditions applied
to all appropriate faces
October 2008
analysis abstract model
Abstract (Conceptual) Model Abstract Reasoning based approach
Abstract Geometry Component Functions & Filters
Result in Component / Abstract Geometry Can be used to drive complex “rule” like structure (ie matching edge
loop pairs for pin or bolt hole locations) Operations on Components / Abstract Geometry
Relations, Groups, Functions & Filters Result in Component / Abstract Geometry Can be nested to form complex abstract objects
Benefits Independent of object data structure (ie CAD features) Removes need for tags Can work with object definitions from multiple sources Applicable to a broad domain of problems
Limitations & Issues Complexity of problem domain determines complexity of Abstract
Reasoning
October 2008
analysis abstract model
Abstract (Conceptual) Model Abstract Reasoning based approach
Decklid example Decklid analysis starting with existing NASTRAN mesh
models Loads & boundary conditions defined abstractly and located
based on each instance
October 2008
analysis abstract model
Abstract (Conceptual) Model Mixed approach
Abstract Reasoning + Tags Best of both worlds approach
Benefits Leverages knowledge that is available Minimizes need for tags to what is easy & appropriate to tag Reduces complexity of Abstract Reasoning by only using
Abstract Reasoning where tags are not appropriate Independent of object data structure (ie CAD features) Removes need for tags Can work with object definitions from multiple sources Applicable to complete domain of problems
Limitations & Issues Requires planning when & what tags are appropriate
Yet another layer of abstraction
October 2008
analysis abstract model
Instance Independent Simulation Definition Instantiation Rules and Processes
Transformation mappings from various object definition instance representation types to valid simulation instance models (implicit).
Includes instantiation of relations data Includes instantiation of derived data
inverse space for CFD/EM “sliver” feature removal
Different for each object definition instance representation type
Included as part of GeomSim modules for supported object definition representation types
October 2008
simulation model
The Object Definition Instance (“Design Model”) is transformed into a non-manifold topology (Simulation
Model) Solids that touch share common faces and edges at the contact
interface Resulting interface faces may have material on both sides Allows for single or set of mesh entities at the interface Attributes may be used to create duplicate mesh entities at the
interface
Attributes are recast from the Abstract Model to the Simulation Model for the current Design Model instance
October 2008
simulation model
A Unified Topology Model is created independent of geometry source (also works with discrete models – stl/mesh models) Initially generated from Design Model instance Provides interrogations of geometry via direct access of
the Design Model Topological adjacencies, point classification, surface evaluation
(points, derivatives, normals, etc.), closest point queries, etc. Assembly modeling
Represent assembly models as non-manifold model even if the underlying modeling engine does not support this
Allows for creation and modification of simulation related topology Suppression of “small” features Addition of bounding boxes Symmetry planes Recognition of void regions that are not explicitly defined in the
modeling source Simulation Model topology does not have to exactly
match the Design Model topology
October 2008
automatic mesh generation
Mesh control attributes assigned to the Abstract Model are mapped to the appropriate entities in the current Simulation Model instance.
Supports non-manifold topology models embedded vertices, edges, faces
Maintains relationship of mesh entities to Simulation Model topology
Provides ability to put a full or partial mesh on a model entity
October 2008
automatic mesh generation
Fully automatic mesh generation for surfaces and solids Triangular, quadrilateral
and mixed surface meshes Tetrahedral volume
meshes
Courtesy Ford Motor Company
Courtesy Top Systems Ltd
Courtesy Infolytica Corporation
October 2008
automatic mesh generation
Curved mesh generation Supports meshes of higher
order elements that capture geometry
Courtesy Top Systems Ltd.
October 2008
automatic mesh generation
Matched meshes for periodic boundary conditions
Courtesy Infolytica Corporation
October 2008
automatic mesh generation
Boundary Layer meshing with edge blends
October 2008
automatic mesh generation
Extrusion meshing Extrude mesh
between two faces with similar topology
Supports generalized curvature between faces
October 2008
automatic mesh generation
Crack Tip meshing 3D edge blends along crack tip edge
October 2008
automatic mesh generation
Extensive mesh refinement control Specified refinement size
Absolute value on model, model entity or location in space
Relative value on model or model entity Function based on location in space
Boundary layer growth rate Curvature based mesh refinement User defined refinement
October 2008
A unified representation of simulation results data that is independent of the physics solver used
Provide result feedback in terms of design objectives and criteria
Provide improved data for visualization software
Provide high level access and query functions
October 2008
results management
Expressions Are based on operations on one or more fields
Can be created from multiple fields Fields may be on the same or different meshes
Can create new fields Can store what the field represents
Can be evaluated over any part of the domain Can be evaluated over Components or Classes in the
Abstract Model Can be used to express results in terms of design
objectives (e.g. comfort index, bearing force, power drop, …)
October 2008
results management
Supports mapping solutions between meshes Different physics Same physics but different mesh Adaptive mesh modification Solution migration during
repartitioning
October 2008
adaptive mesh modification
October 2008
adaptive mesh modification
Provides refinement and coarsening of existing mesh
Ensures new nodes on boundary are placed correctly on geometry and mesh is valid
Enables anisotropic
target mesh
October 2008
geometry based parallel mesh generation & adaptivity
Growing need to solve larger and larger problems Fluid domain applications (automotive & aerospace) Biomedical applications Environmental applications Electromagnetic applications Coupled applications
October 2008
geometry based parallel mesh generation & adaptivity
Growing need to solve larger and larger problems Tens of millions of elements are becoming prevalent Hundreds of millions of elements are becoming common Applications requiring billions of elements are appearing
October 2008
geometry based parallel mesh generation & adaptivity
Solvers have made significant advances in parallelization Parallel CFD and EM solves are quite common Recent advances have shown excellent scaling results on large
paralleled clusters (ref. Ken Jansen)
Meshing Technology has not kept up with solver technology in the area of Parallel computing Some advances have been made in Parallel mesh adaptation Some work has been done in Parallel mesh generation
A few applications have Distributed memory parallel meshing available A few more applications have Shared memory parallel meshing available Most applications start the parallel meshing with an initial facetted model
Mesh generation for the large scale problems is clearly becoming the bottleneck
October 2008
geometry based parallel mesh generation & adaptivity
Parallel mesh generation brings with it its own set of problems / issues For generalized meshing of arbitrary
geometry the problem is ill formed for parallel computing
An added complexity is that the intent for CFD, EM and other far-field applications is to model the space defining the field volume with one or more complex volumes
Just splitting the workload by parts in an assembly is not appropriate
October 2008
geometry based parallel mesh generation & adaptivity
Accurate solutions require accurate capture of geometry Curvature based refinement is
commonly used in serial mesh generation applications
Small details may be of interest Using a predefined facet model
may not be accurate enough for the critical areas of interest
Accurate capture of geometry requires direct geometry access as part of the parallel mesh generation
Creating a highly accurate facet model is a serial operation and would quickly become the new bottleneck
October 2008
geometry based parallel mesh generation & adaptivity
Shared Memory .vs. Distributed Memory 64 bit processors and multi-core systems have raised the
question of Shared Memory (multi-threaded) or Distributed Memory (MPI) architectures
The answer is basically related to the size of mesh required The limitation on Shared Memory Parallel has moved from the
addressable memory space to the amount of physical memory available
The main issue with Distributed Memory Parallel is to get enough regions to be meshed on each processor to avoid a negative impact from communications
Three groupings of problems can be considered Moderately large – (millions to low tens of millions of elements) –
SMP Large – (low tens of millions to high tens of millions) – either Very Large – (> high tens of millions) - DMP
Simmetrix has developed a set of toolkits for Geometry Based Parallel Mesh generation for both SMP and DMP architectures
October 2008
geometry based parallel mesh generation & adaptivity
A series of rooms with furniture and people (Parasolid model) (courtesy of Transpire , Inc.)
Walls, Furniture, people and space are meshed With BL for CFD type applications Without BL for EM type applications
Results shown for various configurations of Distributed Memory Parallel (DMP) on a small cluster
October 2008
geometry based parallel mesh generation & adaptivity
Cluster configuration used for testing (low end cluster) 6 dual core Suns 2Ghz Opteron processors 2GB Ram per processor Gigabit Ethernet connection
Speedup Test 3 different mesh sizes run with and without Boundary Layers
~ 6 Million mesh regions run on 2, 4, and 8 processors ~ 24 Million mesh regions run on 4 and 8 processors ~ 46 Million mesh regions run on 4, 6, 8,10 and 12 processors
Normalized Speedup = ( t(b) * n(b) ) / ( t(n) * n ) t(b) – meshing time for base (minimum number of processors run) n(b) – number of base processors t(n) – meshing time for n processors n – number of processors used
October 2008
geometry based parallel mesh generation & adaptivity
6 million mesh regions ~1.5 million mesh regions/minute on 2 processors ~2.3 million mesh regions/minute on 4 processors ~3.6 million mesh regions/minute on 8 processors
Meshing time (6 million regions)
0
50
100
150
200
250
300
0 2 4 6 8 10
number of processors
w/out Boundary Layers
w/Boundary Layers
October 2008
geometry based parallel mesh generation & adaptivity
6 Million mesh regions
Processors 2 4 8
w/out Boundary Layers 1.00 0.77 0.61
w/ Boundary Layers 1.00 0.80 0.62
Normalized Speedup (6 million regions)
0
0.2
0.4
0.6
0.8
1
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0 2 4 6 8 10
number of processors
sp
ee
du
p
w/out Boundary Layers
w/Boundary Layers
October 2008
geometry based parallel mesh generation & adaptivity
24 million mesh regions ~ 3 Million mesh regions/minute on 4 processors ~ 4.3 Million mesh regions/minute on 8 processors
Meshing time (24 million regions)
0
100
200
300
400
500
600
0 2 4 6 8 10
number of processors
w/out Boundary Layers
w/Boundary Layers
October 2008
geometry based parallel mesh generation & adaptivity
24 Million mesh regions
Processors 4 8
w/out Boundary Layers 1.00 0.72
w/ Boundary Layers 1.00 0.74
Normalized Speedup (24 million regions)
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0.20
0.40
0.60
0.80
1.00
1.20
0 2 4 6 8 10
number of processors
sp
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du
p
w/out Boundary Layers
w/Boundary Layers
October 2008
geometry based parallel mesh generation & adaptivity
46 million mesh regions ~ 2.7 Million mesh regions/minute on 4 processors ~ 3 Million mesh regions/minute on 6 processors ~ 3.5 Million mesh regions/minute on 8 processors ~ 4.9 Million mesh regions/minute on 10 processors ~ 5.3 Million mesh regions/minute on 10 processors
Meshing Time (46 million regions)
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0 2 4 6 8 10 12 14
number of processors
w/out Boundary Layers
w/Boundary Layers
October 2008
geometry based parallel mesh generation & adaptivity
6 Million mesh regions – normalized speedup
Processors 4 6 8 10 12
w/out Boundary Layers 1.00 0.73 0.68 0.73 0.66
w/ Boundary Layers 1.00 0.76 0.62 0.69 0.65Speedup (46 million regions)
0.00
0.20
0.40
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0.80
1.00
1.20
0 2 4 6 8 10 12 14
number of processors
spee
dup w/out Boundary Layers
w/Boundary Layers
October 2008
geometry based parallel mesh generation & adaptivity
Parallel Mesh adaptivity Supports isotropic and
anisotropic mesh adaptivity
Supports refinement & coarsening
Adapted mesh adheres to original geometry
Supports initial mesh as partitioned or single mesh
