A method for using legacy data for metamodel-based design of large-scale systems

A. Srivastava, K. Hacker, K. Lewis, T. W. Simpson

Research output: Contribution to journalArticlepeer-review

26 Scopus citations


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; there-fore, 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.

Original languageEnglish (US)
Pages (from-to)146-155
Number of pages10
JournalStructural and Multidisciplinary Optimization
Issue number2-3
StatePublished - Sep 2004

All Science Journal Classification (ASJC) codes

  • Software
  • Control and Systems Engineering
  • Computer Science Applications
  • Computer Graphics and Computer-Aided Design
  • Control and Optimization


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