Comparing Robust Design Optimization and Reliability Based Optimization Formulations for Practical Aspects of Industry Problems

Paper #:
  • 2015-01-0471

Published:
  • 2015-04-14
DOI:
  • 10.4271/2015-01-0471
Citation:
Gokhale, A., Parashar, S., and Kansara, S., "Comparing Robust Design Optimization and Reliability Based Optimization Formulations for Practical Aspects of Industry Problems," SAE Technical Paper 2015-01-0471, 2015, doi:10.4271/2015-01-0471.
Pages:
8
Abstract:
Need for accounting Robustness and Reliability in engineering design is well understood and being researched. However, the actual practice of applying robustness and reliability methods to high fidelity CAE based simulations, especially during optimization is just starting to gain traction in last few years. Availability of computing power is helping the use of such methods, but, at the same time the demand for modeling stochastic behavior with high fidelity CAE simulations and considering large number of stochastic variables still makes it prohibitive. Typically, Robust Design Optimization (RDO) formulations calculate mean and standard deviation of responses based on sampling. On the other hand Reliability Based Design Optimization (RBDO) formulations have been using methods like First Order Reliability Method (FORM) or Second Order Reliability Method (SORM) which require nested optimization to evaluate joint probability distribution and reliability factor. It is difficult for an industry practitioner with limited expertise in optimization to decide which approach to use when they want to find a robust yet optimal design. This paper will try to evaluate these approaches based on following criteria; 1. Ease of articulating the problem formulation; 2. Effectiveness and flexibility of solution strategy; and 3. Ease of analyzing the data from optimization and picking a final solution. This Paper will also try to highlight the use of multi-objective formulations for both approaches. The paper will focus on industry examples where one has to use commercial tools for optimization and simulation.This paper covers the application and comparison of the above mentioned methods based on practical solution aspects for automotive problems and crash safety. One of the Automotive problem will be from safety application that will use models from MADYMO. All the optimization, uncertainty quantification and automation of CAE analysis tools is performed using commercial optimization software modeFRONTIER. Based on results from various cases, the paper will summarize advantages and disadvantages of both approaches and try to recommend a formulation better suitable for certain problem type.
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