Algorithms and analyses for stochastic optimization for turbofan noise reduction using parallel reduced-order modeling

Yang, Huanhuan; Gunzburger, Max

doi:10.1016/j.cma.2017.02.030

Title: Algorithms and analyses for stochastic optimization for turbofan noise reduction using parallel reduced-order modeling

Journal Article · 05 March 2017 · Computer Methods in Applied Mechanics and Engineering

DOI: https://doi.org/10.1016/j.cma.2017.02.030 · OSTI ID:1533650

Yang, Huanhuan ^[1]; Gunzburger, Max ^[2]

Florida State University, Tallahassee, FL (United States); DOE/OSTI
Florida State University, Tallahassee, FL (United States)

Simulation-based optimization of acoustic liner design in a turbofan engine nacelle for noise reduction purposes can dramatically reduce the cost and time needed for experimental designs. Because uncertainties are inevitable in the design process, a stochastic optimization algorithm is posed based on the conditional value-at-risk measure so that an ideal acoustic liner impedance is determined that is robust in the presence of uncertainties. A parallel reduced-order modeling framework is developed that dramatically improves the computational efficiency of the stochastic optimization solver for a realistic nacelle geometry. The reduced stochastic optimization solver takes less than 500 s to execute. Additionally, well-posedness and finite element error analyses of the state system and optimization problem are provided.

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Research Organization:: Florida State University, Tallahassee, FL (United States)

Sponsoring Organization:: Air Force Office of Scientific Research (AFOSR); USDOE; USDOE Office of Science (SC)

Grant/Contract Number:: SC0010678

OSTI ID:: 1533650

Journal Information:: Computer Methods in Applied Mechanics and Engineering, Journal Name: Computer Methods in Applied Mechanics and Engineering Journal Issue: C Vol. 319; ISSN 0045-7825

Publisher:: ElsevierCopyright Statement

Country of Publication:: United States

Language:: English

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Cited By (2)

A compute-bound formulation of Galerkin model reduction for linear time-invariant dynamical systems Rizzi, Francesco; Parish, Eric J.; Blonigan, Patrick J. arXiv https://doi.org/10.48550/arxiv.2009.11742	text	January 2020
Certifiable Risk-Based Engineering Design Optimization Chaudhuri, Anirban; Kramer, Boris; Norton, Matthew arXiv https://doi.org/10.48550/arxiv.2101.05129	preprint	January 2021

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97 MATHEMATICS AND COMPUTING
conditional value at risk
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stochastic Helmholtz equation
turbofan noise reduction

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