Algorithms and analyses for stochastic optimization for turbofan noise reduction using parallel reduced-order modeling
Abstract
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 seconds to execute. In addition, well-posedness and finite element error analyses of the state system and optimization problem are provided.
Cite
@article{arxiv.1611.00671,
title = {Algorithms and analyses for stochastic optimization for turbofan noise reduction using parallel reduced-order modeling},
author = {Huanhuan Yang and Max Gunzburger},
journal= {arXiv preprint arXiv:1611.00671},
year = {2017}
}