Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
4964006 | Computer Methods in Applied Mechanics and Engineering | 2017 | 25 Pages |
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 500Â s to execute. In addition, well-posedness and finite element error analyses of the state system and optimization problem are provided.
Related Topics
Physical Sciences and Engineering
Computer Science
Computer Science Applications
Authors
Huanhuan Yang, Max Gunzburger,