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Multiobjective muffler shape optimization with hybrid acoustics modelling

机译:基于混合声学模型的多目标消声器形状优化

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摘要

This paper considers the combined use of a hybrid numerical method for the modeling of acoustic mufflers and a genetic algorithm for multiobjective optimization. The hybrid numerical method provides accurate modeling of sound propagation in uniform waveguides with non-uniform obstructions. It is based on coupling a wave based modal solution in the uniform sections of the waveguide to a finite element solution in the non-uniform component. Finite element method provides flexible modeling of complicated geometries, varying material parameters, and boundary conditions, while the wave based solution leads to accurate treatment of non-reflecting boundaries and straightforward computation of the transmission loss (TL) of the muffler. The goal of optimization is to maximize TL at multiple frequency ranges simultaneously by adjusting chosen shape parameters of the muffler. This task is formulated as a multiobjective optimization problem with the objectives depending on the solution of the simulation model. NSGA-II genetic algorithm is used for solving the multiobjective optimization problem. Genetic algorithms can be easily combined with different simulation methods, and they are not sensitive to the smoothness properties of the objective functions. Numerical experiments demonstrate the accuracy and feasibility of the model-based optimization method in muffler design.
机译:本文考虑了将混合数值方法用于消声器建模和遗传算法进行多目标优化的组合使用。混合数值方法提供了具有均匀障碍物的均匀波导中声音传播的准确模型。它基于将波导均匀截面中基于波的模态解耦合到非均匀分量中的有限元解的基础。有限元方法可为复杂的几何形状,变化的材料参数和边界条件提供灵活的建模,而基于波的解决方案可对无反射边界进行精确处理,并直接计算消声器的传输损耗(TL)。优化的目标是通过调整消音器的选定形状参数,同时在多个频率范围内最大化TL。该任务被公式化为一个多目标优化问题,其目标取决于仿真模型的解决方案。 NSGA-II遗传算法用于解决多目标优化问题。遗传算法可以轻松地与不同的仿真方法结合使用,并且它们对目标函数的平滑特性不敏感。数值实验证明了基于模型的优化方法在消声器设计中的准确性和可行性。

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