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

作者：Yang Huanhuan; Gunzburger Max

来源：Computer Methods in Applied Mechanics and Engineering, 2017, 319: 217-239.

DOI：10.1016/j.cma.2017.02.030

• Stochastic Helmholtz equation
• Conditional value at risk
• Proper orthogonal decomposition
• Turbofan noise reduction

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.

• 出版日期2017-6-1