Please use this identifier to cite or link to this item: http://hdl.handle.net/1783.1/54290
Surrogate model-based optimization framework: A case study in aerospace design
|Authors|| Mack, Y. |
|Source||Studies in computational intelligence , v. 51, 2007, p. 323-342|
|Summary||Surrogate-based optimization has proven very useful for novel or exploratory design tasks because it offers a global view of the characteristics of the design space, and it enables one to refine the design of experiments, conduct sensitivity analyses, characterize tradeoffs between multiple objectives, and, if necessary, help modify the design space. In this article, a framework is presented for design optimization on problems that involve two or more objectives which may be conflicting in nature. The applicability of the framework is demonstrated using a case study in space propulsion: a response surface-based multi-objective optimization of a radial turbine for an expander cycle-type liquid rocket engine. The surrogate model is combined with a genetic algorithm-based Pareto front construction and can be effective in supporting global sensitivity evaluations. In this case study, due to the lack of established experiences in adopting radial turbines for space propulsion, much of the original design space, generated based on intuitive ideas from the designer, violated established design constraints. Response surfaces were successfully used to define previously unknown feasible design space boundaries. Once a feasible design space was identified, the optimization framework was followed, which led to the construction of the Pareto front using genetic algorithms. The optimization framework was effectively utilized to achieve a substantial performance improvement and to reveal important physics in the design. © Springer-Verlag Berlin Heidelberg 2007.|
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Surrogate modeling uses cheap “surrogates” to represent the response surface of simulation models. It involves several steps, including initial sampling, regression and adaptive sampling. This study evaluates an adaptive surrogate modeling based optimization (ASMO) method on two benchmark problems: the Hartman function and calibration of the SAC-SMA hydrologic model. Our results show that: 1) Gaussian Processes are the best surrogate model construction method. A minimum Interpolation Surface method is the best adaptive sampling method. Low discrepancy Quasi Monte Carlo methods are the most suitable initial sampling designs. Some 15–20 times the dimension of the problem may be the proper initial sample size; 2) The ASMO method is much more efficient than the widely used Shuffled Complex Evolution global optimization method. However, ASMO can provide only approximate optimal solutions, whose precision is limited by surrogate modeling methods and problem-specific features; and 3) The identifiability of model parameters is correlated with parameter sensitivity.