The integration of design of experiments, surrogate modeling and optimization for thermoscience research

  • Authors:

  • V. Queipo;J. Arévalo;Salvador Pintos

  • Affiliations:

  • Applied Computing Institute, Faculty of Engineering, University of Zulia, Maracaibo, 4005, Zulia, Venezuela;Applied Computing Institute, Faculty of Engineering, University of Zulia, Maracaibo, 4005, Zulia, Venezuela;Applied Computing Institute, Faculty of Engineering, University of Zulia, Maracaibo, 4005, Zulia, Venezuela

  • Venue:
  • Engineering with Computers
  • Year:
  • 2005

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Abstract

This paper presents an integrated approach for the solution of complex optimization problems in thermoscience research. The cited approach is based on the design of computational experiments (DOE), surrogate modeling, and optimization. The DOE/surrogate modeling techniques under consideration include: A-optimal/classical linear regression, Latin hypercube/artificial neural networks, and Latin hypercube/Sugeno-type fuzzy models. These techniques are coupled with both local (modified Newton’s method) and global (genetic algorithms) optimization methods. The proposed approach proved to be an effective, efficient and robust modeling and optimization tool in the context of a case study, and holds promise for use in larger scale optimization problems in thermoscience research.