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

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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… Click to show full abstract

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 s to execute. In addition, well-posedness and finite element error analyses of the state system and optimization problem are provided.

Keywords: reduced order; stochastic optimization; noise reduction; parallel reduced; optimization; order modeling

Journal Title: Computer Methods in Applied Mechanics and Engineering
Year Published: 2017

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