The Multi-Disciplinary challenge: heading towards Global Optimization.

Transcription

1 The Multi-Disciplinary challenge: heading towards Global ptimization. Alexandre Poisson Luca Fuligno Esteco France, presenter EnginSoft SpA

2 Theme of the presentation Synergistical application of simulations and optimization techniques, overcoming the actual limits of computational resources, to achieve a global, reliable, and multi-disciplinary optimum solution MD=Multi-Disciplinary-ptimization

3 MD definition and the global picture (breakthrough tech.) ptimal design of complex engineering systems which requires analysis that accounts for interactions amongst the disciplines (or parts of the system) and which seeks to synergistically exploit these interactions MD definition (NASA) Car aerodynamics Electronics Safety Engine CFD Car Dynamics Structural analysis MD

4 MD and disciplines interactions: engine example (best practice) Engine air intake manifold design Thermo-mechanical analysis (Von Mises 130 C) a a aeeeeeeeeeeeeeeee eeeeeeee Modal analysis (Structural modes freq.s) MD tool Acoustic analysis (Sound pressure) And much more, CST, PRCESS, CFD,

5 Key Points to jump from best practice to the implementation of breakthrough tech.?? Multi bjective ptimization Algorithms? Dedicated post-processing and data mining? Increasing complexity Modeling? Efficient Integration of Response Surface Modeling (RSM) in the MD

6 #1 Multi-disciplinarity needs multi-objective optimization algorithms Weighting factors approach is not adequate: mixing apples with oranges missing the optimum: Consumption X DIFFERENT mono-obj. solutions X X X X??? No solution Cost UNIQUE Multi-bjective Problem Global Solution: Pareto Frontier

7 #1 Multi bjective ptimization Algorithms Experience in multi-objective optimization algorithms needed: MGA, MSA, MGT, NSGA, ARMGA,

8 #2 Multi-bjectivity: efficient post-processing tools From 1 objective to N Pareto Frontier plots and decision process

9 #2 Multi-bjectivity: efficient post-processing tools & data mining Data Clustering in n-dimensional space (useful to sort out high number of variables/objectives in a 2D manner)

10 #3 Clever approach through increasing complexity models MD has the maximum impact if applied upfront in the design process In early phases approximated models (fast!) can be effectively applied

11 #4 Clever approach through deep integration between RSMalgorithms When accurate models are needed (LNG runtimes), optimization can be feasible thanks to deep integration between algorithms and sophisticated Response Surface Models (FAST-MGA*)

12 #4 Clever approach through deep integration between RSMalgorithms Instantaneous adaptive RSMs evaluations replace some CAE software computations: RSMs are automatically updated whenever new reliable CAE data are available, in-the-loop during the optimization (FAST-MGA) Strong time reduction, high accuracy.

13 Summarizing: keys to jump from best practice to breakthrough tech.s are available. Multi bjective ptimization Algorithms Dedicated post-processing and data mining Increasing complexity Modeling Efficient Integration of Response Surface Modeling in the MD

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