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High-Dimensional Stochastic Design Optimization by Adaptive-Sparse Polynomial Dimensional Decomposition

Conference paper
Part of the Lecture Notes in Computational Science and Engineering book series (LNCSE, volume 109)

Abstract

This paper presents a novel adaptive-sparse polynomial dimensional decomposition (PDD) method for stochastic design optimization of complex systems. The method entails an adaptive-sparse PDD approximation of a high-dimensional stochastic response for statistical moment and reliability analyses; a novel integration of the adaptive-sparse PDD approximation and score functions for estimating the first-order design sensitivities of the statistical moments and failure probability; and standard gradient-based optimization algorithms. New analytical formulae are presented for the design sensitivities that are simultaneously determined along with the moments or the failure probability. Numerical results stemming from mathematical functions indicate that the new method provides more computationally efficient design solutions than the existing methods. Finally, stochastic shape optimization of a jet engine bracket with 79 variables was performed, demonstrating the power of the new method to tackle practical engineering problems.

Keywords

Failure Probability Statistical Moment Design Sensitivity Design Optimization Problem Polynomial Chaos Expansion 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgements

The authors acknowledge financial support from the U.S. National Science Foundation under Grant Nos. CMMI-0969044 and CMMI-1130147.

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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.College of Engineering and Program of Applied Mathematical & Computational SciencesThe University of IowaIowa CityUSA
  2. 2.Department of Mechanical EngineeringGeorgia Southern UniversityStatesboroUSA
  3. 3.Department of Aerospace EngineeringSan Diego State UniversitySan DiegoUSA

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