1. October 2008 Automation components for simulation-based engineering

2. 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

3. 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 ...

4. 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.

5. October 2008 simulation-based engineering environment Simulation Driver Frameworks (Robust Design / Design Exploration / Systems Engineering) Instance- Independent Simulation 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 Solve for Frameworks

6. 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

7. 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

8. 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

9. 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

10. 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

11. 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

12. 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

13. 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

14. 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

15. 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

16. 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

17. 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

18. 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

19. 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

20. October 2008 automatic mesh generation  Curved mesh generation  Supports meshes of higher order elements that capture geometry Courtesy Top Systems Ltd.

21. October 2008 automatic mesh generation  Matched meshes for periodic boundary conditions Courtesy Infolytica Corporation

22. October 2008 automatic mesh generation  Boundary Layer meshing with edge blends

23. October 2008 automatic mesh generation  Extrusion meshing  Extrude mesh between two faces with similar topology  Supports generalized curvature between faces

24. October 2008 automatic mesh generation  Crack Tip meshing  3D edge blends along crack tip edge

25. 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

26. 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

27. 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, …)

28. October 2008 results management  Supports mapping solutions between meshes  Different physics  Same physics but different mesh  Adaptive mesh modification  Solution migration during repartitioning

29. October 2008 adaptive mesh modification

30. 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

31. 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

32. 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

33. 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

34. 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

35. 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

36. 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

37. 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

38. 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

39. 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

40. October 2008 geometry based parallel mesh generation & adaptivity  6 Million mesh regions Processors248 w/out Boundary Layers1.000.770.61 w/ Boundary Layers1.000.800.62

41. 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

42. October 2008 geometry based parallel mesh generation & adaptivity  24 Million mesh regions Processors48 w/out Boundary Layers1.000.72 w/ Boundary Layers1.000.74

43. 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

44. October 2008 geometry based parallel mesh generation & adaptivity  6 Million mesh regions – normalized speedup Processors4681012 w/out Boundary Layers1.000.730.680.730.66 w/ Boundary Layers1.000.760.620.690.65

45. 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

46. October 2008 Simmetrix technology is widely used