Book chapter
High-Dimensional Stochastic Design Optimization by Adaptive-Sparse Polynomial Dimensional Decomposition
Sparse Grids and Applications - Stuttgart 2014, pp.247-264
Lecture Notes in Computational Science and Engineering, Springer International Publishing
03/17/2016
DOI: 10.1007/978-3-319-28262-6_10
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.
Details
- Title: Subtitle
- High-Dimensional Stochastic Design Optimization by Adaptive-Sparse Polynomial Dimensional Decomposition
- Creators
- Sharif Rahman - University of IowaVaibhav Yadav - San Diego State University
- Resource Type
- Book chapter
- Publication Details
- Sparse Grids and Applications - Stuttgart 2014, pp.247-264
- Publisher
- Springer International Publishing; Cham
- Series
- Lecture Notes in Computational Science and Engineering
- DOI
- 10.1007/978-3-319-28262-6_10
- eISSN
- 2197-7100
- ISSN
- 1439-7358
- Language
- English
- Date published
- 03/17/2016
- Academic Unit
- Mechanical Engineering
- Record Identifier
- 9984196553802771
Metrics
11 Record Views