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

TL;DR

This paper introduces a stochastic optimization framework with parallel reduced-order modeling for designing acoustic liners in turbofan engines, significantly reducing computational costs while accounting for uncertainties.

Contribution

It develops a novel parallel reduced-order modeling approach combined with stochastic optimization for robust acoustic liner design in turbofan engines.

Findings

Optimization solver executes in less than 500 seconds.

The framework effectively handles uncertainties in design.

Finite element error analysis supports the method's robustness.

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.