Genetic algorithms + data structures = evolution programs (3rd ed.)
Genetic algorithms + data structures = evolution programs (3rd ed.)
Response Surface Methodology: Process and Product in Optimization Using Designed Experiments
Response Surface Methodology: Process and Product in Optimization Using Designed Experiments
Response Surface Model Building and Multidisciplinary Optimization Using Overdetermined D-Optimal Designs
A survey of multidisciplinary design optimization methods in launch vehicle design
Structural and Multidisciplinary Optimization
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To effectively reduce the computational loads during the optimization process, while maintaining the solution accuracy, a refined response surface method with design space transformation and refined RSM using sub-optimization for the regression model is proposed and implemented for the nose fairing design of a space launcher. Total drag is selected as the objective function, and the surface heat transfer, the fineness ratio, and the internal volume of the nose fairing are considered as design constraints. Sub-optimization for the design space transformation parameters and the iterative regression model construction technique are proposed in order to build response surface with high confidence level using minimum number of experiment points. The derived strategies are implemented to the nose fairing design optimization using the full Navier-Stokes equations. The result shows that an optimum nose fairing shape is obtained with four times less analysis calculations compared with the gradient-based optimization method, and demonstrates the efficiency of the refined response surface method and optimization strategies proposed in this study. The techniques can be directly applied to the multidisciplinary design and optimization problems with many design variables.