Automation components for simulation-based engineering

1. Automation components for simulation-based engineering

2. simulation-based engineeringchallenges • 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. simulation-based engineeringchallenges • 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. simulation-based engineeringchallenges • 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. simulation-based engineering environment Simulation Driver Frameworks (Robust Design / Design Exploration / Systems Engineering) Instance-Independent Simulation Definition (Analysis Abstract Model) Simulation Results Instance Data Simulation Execution Instance Model Simulation Instance Model Solve for Frameworks Data Management Frameworks (PLM/SDM) Rules, Processes, Templates Instance Based Simulation Data (Execution Ready/Results Data) Simulation Models Object Definition Instance Data (CAD, Mesh Based, Concept, Non-Geometric)

6. 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. analysis abstract model • Instance Independent Simulation Definition Made up of Four Major Aspects Physical Characteristics Instantiation Rules and Processes Solution Specific Definitions Conceptual Model

8. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. Courtesy Top Systems Ltd Courtesy Ford Motor Company Courtesy Infolytica Corporation automatic mesh generation • Fully automatic mesh generation for surfaces and solids • Triangular, quadrilateral and mixed surface meshes • Tetrahedral volume meshes

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

21. Courtesy Infolytica Corporation automatic mesh generation • Matched meshes for periodic boundary conditions

22. automatic mesh generation • Boundary Layer meshing with edge blends

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

24. automatic mesh generation • Crack Tip meshing • 3D edge blends along crack tip edge

25. 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. 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. 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. results management • Supports mapping solutions between meshes • Different physics • Same physics but different mesh • Adaptive mesh modification • Solution migration during repartitioning

29. adaptive mesh modification

30. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. geometry based parallel mesh generation & adaptivity • 6 Million mesh regions

41. 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. geometry based parallel mesh generation & adaptivity • 24 Million mesh regions

43. 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. geometry based parallel mesh generation & adaptivity • 6 Million mesh regions – normalized speedup

45. 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. Simmetrix technology is widely used