Multi-Disciplinary Design Optimization exploiting the efficiency of ANSA-LSOPT-META coupling
Simple optimization techniques may serve well for the improvement of product performance at early concept design phases. At final phases though, optimization problems become more complex, with many variables and multiple optimum solutions. This leads to the need for the deployment of multi- disciplinary optimization techniques where many different load cases and analysis types, such as for Crash, NVH, CFD etc., are combined to achieve the optimum solution. The combination of ANSA CAE pre-processor, LS-OPT and mETA post-processor, offers an efficient and reliable tool for solving multi- disciplinary optimization problems. In such a process, starting from a common initial model, multiple outputs for different load cases and disciplines can be defined in ANSA. Design Variables that handle model shape and parameters are controlled in a centralized manner by the dedicated Optimization Task tool that is integrated in the core ANSA functionality. Further more, the newly released coupling between LS-OPT and mETA, provides a valuable tool for the definition of multi-disciplinary optimization scenarios, as mETA is able to extract responses from numerous solvers and load cases and feed them to LS-OPT.
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Multi-Disciplinary Design Optimization exploiting the efficiency of ANSA-LSOPT-META coupling
Simple optimization techniques may serve well for the improvement of product performance at early concept design phases. At final phases though, optimization problems become more complex, with many variables and multiple optimum solutions. This leads to the need for the deployment of multi- disciplinary optimization techniques where many different load cases and analysis types, such as for Crash, NVH, CFD etc., are combined to achieve the optimum solution. The combination of ANSA CAE pre-processor, LS-OPT and mETA post-processor, offers an efficient and reliable tool for solving multi- disciplinary optimization problems. In such a process, starting from a common initial model, multiple outputs for different load cases and disciplines can be defined in ANSA. Design Variables that handle model shape and parameters are controlled in a centralized manner by the dedicated Optimization Task tool that is integrated in the core ANSA functionality. Further more, the newly released coupling between LS-OPT and mETA, provides a valuable tool for the definition of multi-disciplinary optimization scenarios, as mETA is able to extract responses from numerous solvers and load cases and feed them to LS-OPT.