Surrogate-based-optimization methods provide a means to achieve high-fidelity design optimization at reduced computational cost by using a high-fidelity model in combination with lower-fidelity models that are less expensive to evaluate. This paper presents a provably convergent trust-region model-management methodology for variableparameterization design models: that is, models for which the design parameters are defined over different spaces. Corrected space mapping is introduced as a method to map between the variable-parameterization design spaces. It is then used with a sequential-quadratic-programming-like trust-region method for two aerospace-related design optimization problems. Results for a wing design problem and a flapping-flight problem show that the method outperforms direct optimization in the high-fidelity space. On the wing design problem, the new method achieves 76% savings in high-fidelity function calls. On a bat-flight design problem, it achieves approximately 45% time savings, although it converges to a different local minimum than did the benchmark.
An approach is presented for determining which parameters defining the features in a CAD model need to be modified, and by what amount, to optimize component performance. It uses sensitivities computed for the parameters to determine the change required in each to optimize the component. Parametric sensitivity is computed by combining a measure of boundary movement due to a parameter perturbation, known as design velocity, and an adjoint sensitivity map over the boundary. The sensitivity map results from an adjoint analysis and approximates the change in objective function (performance) due to a movement of the boundary. Gradient based optimization is used based on the parametric sensitivities.This presented method is significantly less computationally expensive than alternative approaches, and has the advantage that optimization is based on the parameters defining the CAD model, allowing it to be integrated into design workflows. The efficiency of the approach allows all of the parameters in the CAD model to be used as optimization variables, potentially offering better optimization. The work is immune to many of the issues hampering existing approaches.
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