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Structural and Multidisciplinary Optimization

, Volume 51, Issue 4, pp 919–940 | Cite as

Design optimization using hyper-reduced-order models

  • David AmsallemEmail author
  • Matthew Zahr
  • Youngsoo Choi
  • Charbel Farhat
RESEARCH PAPER

Abstract

Solving large-scale PDE-constrained optimization problems presents computational challenges due to the large dimensional set of underlying equations that have to be handled by the optimizer. Recently, projection-based nonlinear reduced-order models have been proposed to be used in place of high-dimensional models in a design optimization procedure. The dimensionality of the solution space is reduced using a reduced-order basis constructed by Proper Orthogonal Decomposition. In the case of nonlinear equations, however, this is not sufficient to ensure that the cost associated with the optimization procedure does not scale with the high dimension. To achieve that goal, an additional reduction step, hyper-reduction is applied. Then, solving the resulting reduced set of equations only requires a reduced dimensional domain and large speedups can be achieved. In the case of design optimization, it is shown in this paper that an additional approximation of the objective function is required. This is achieved by the construction of a surrogate objective using radial basis functions. The proposed method is illustrated with two applications: the shape optimization of a simplified nozzle inlet model and the design optimization of a chemical reaction.

Keywords

PDE-constrained optimization Surrogate modeling Parametric model reduction Hyper-reduction Discrete empirical interpolation method Shape optimization 

Notes

Acknowledgments

The authors acknowledge partial support by the Army Research Laboratory through the Army High Performance Computing Research Center under Cooperative Agreement W911NF-07-2-0027, and partial support by the Office of Naval Research under grant no. N00014-11-1-0707. This document does not necessarily reflect the position of these institutions, and no official endorsement should be inferred.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  • David Amsallem
    • 1
    Email author
  • Matthew Zahr
    • 2
  • Youngsoo Choi
    • 1
  • Charbel Farhat
    • 1
    • 2
    • 3
  1. 1.Department of Aeronautics and AstronauticsStanford UniversityStanfordUSA
  2. 2.Institute for Computational and Mathematical EngineeringStanford UniversityStanfordUSA
  3. 3.Department of Mechanical EngineeringStanford UniversityStanfordUSA

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