标题： 远场气动声学形状优化使用大涡模拟 显示英文标题

标题： Far-Field Aeroacoustic Shape Optimization Using Large Eddy Simulation

摘要： 本研究提出了一种气动声学形状优化框架，该框架集成了通量重构（FR）空间离散化、大涡模拟（LES）、Ffowcs-Williams和Hawkings（FW-H）公式以及无梯度的网格自适应直接搜索（MADS）优化算法。 气动声学求解器采用时域FW-H公式处理运动介质问题，通过解析测试用例进行了全面验证，并使用高阶非结构化求解器对无粘和粘性流动进行了验证。 随后，我们强调了数据表面复制对于准确预测展向周期性问题远场噪声的必要性。 所提出的远场气动声学优化框架采用并行实现，确保每次优化迭代的运行时间一致，无论设计参数的数量如何，解决了某些无梯度算法的一个关键局限性。 目标是在远场观察者处最小化总声压级（OASPL），同时保持升力系数的约束，并施加惩罚以防止阻力系数增加，优先考虑降噪同时保持气动性能。 在NACA 4位翼型上评估此框架表明，OASPL减少了$14.4~dB$，平均阻力系数减少了超过$14\%$，同时保持了平均升力系数。 这些发现证明了我们的方法在实际应用中远场气动声学形状优化的可行性和有效性。 显示英文摘要

摘要： This study presents an aeroacoustic shape optimization framework that integrates a Flux Reconstruction (FR) spatial discretization, Large Eddy Simulation (LES), Ffowcs-Williams and Hawkings (FW-H) formulation, and the gradient-free Mesh Adaptive Direct Search (MADS) optimization algorithm. The aeroacoustic solver, employing the FW-H formulation in the time domain for moving medium problems, undergoes thorough verification with analytical test cases and validation using a high-order unstructured solver for both inviscid and viscous flows. We then highlight the necessity of data surface duplication for accurate far-field noise prediction of spanwise periodic problems. The proposed far-field aeroacoustic optimization framework, implemented in parallel, ensures consistent runtime for each optimization iteration, regardless of the number of design parameters, addressing a key limitation of some gradient-free algorithms. The objective is to minimize the Overall Sound Pressure Level (OASPL) at a far-field observer, with a constraint to maintain the lift coefficient and a penalty to prevent any increase in drag coefficient, prioritizing noise reduction while preserving aerodynamic performance. Evaluating this framework on NACA 4-digit airfoils demonstrates a notable OASPL reduction by $14.4~dB$ and over $14\%$ decrease in the mean drag coefficient while maintaining the mean lift coefficient. These findings underscores the feasibility and effectiveness of our approach for far-field aeroacoustic shape optimization in practical applications.