A method for using legacy data for metamodel-based design of large-scale systems |
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Authors: | A. Srivastava K. Hacker K. Lewis T.W. Simpson |
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Affiliation: | (1) Department of Mechanical and Aerospace Engineering, University at Buffalo, 1010 Furnas Hall, Buffalo, NY 14260, USA;(2) Department of Mechanical & Nuclear Engineering and Department of Industrial & Manufacturing Engineering, Pennsylvania State University, 329 Leonhard Building, University Park, PA 16802, USA |
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Abstract: | Despite a steady increase in computing power, the complexity of engineering analyses seems to advance at the same rate. Traditional parametric design analysis is inadequate for the analysis of large-scale engineering systems because of its computational inefficiency; therefore, a departure from the traditional parametric design approach is required. In addition, the existence of legacy data for complex, large-scale systems is commonplace. Approximation techniques may be applied to build computationally inexpensive surrogate models for large-scale systems to replace expensive-to-run computer analysis codes or to develop a model for a set of nonuniform legacy data. Response-surface models are frequently utilized to construct surrogate approximations; however, they may be inefficient for systems having with a large number of design variables. Kriging, an alternative method for creating surrogate models, is applied in this work to construct approximations of legacy data for a large-scale system. Comparisons between response surfaces and kriging are made using the legacy data from the High Speed Civil Transport (HSCT) approximation challenge. Since the analysis points already exist, a modified design-of-experiments technique is needed to select the appropriate sample points. In this paper, a method to handle this problem is presented, and the results are compared against previous work. |
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Keywords: | approximation methods design of experiments kriging response-surface methodology |
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