The computational aero-acoustic study of an isolated passenger car tire is carried out to understand the effect of dimensions of longitudinal tire grooves and operational parameters (velocity and temperature) on tire noise. The computational fluid dynamics and acoustic models are used to obtain aero-acoustic tire noise at near-field and far-field receivers around the tire and artificial neural networks-based regression are used to study the highly non-linear and interactive causal relationships in the system. Unsteady Reynolds-Averaged Navier-Stokes based realizable k-epsilon model is used to solve the flow field in the computational domain. The Ffowcs Williams and Hawkings model is used to obtain aero-acoustic tire noise at far-field positions. Spectral analysis is used to convert the output time domain to frequency domain and to obtain A-weighted sound pressure level. Artificial neural network–based response surface regression is conducted to understand casual relationships between A-weighted sound pressure level and control variables (Groove depth, Groove width, Temperature and velocity). Maximum A-weighted sound pressure level is observed in the wake region of the tire model. The interaction study indicates that ∼10% reduction in the aero-acoustic emissions is possible by selecting appropriate combinations of groove width and groove depth. The interaction of velocity with width is found to be most significant with respect to A-weighted sound pressure level at all receivers surrounding the tire. The interaction of operational parameters, that is, velocity and temperature are found to be significant with respect to A-weighted sound pressure level at wake and front receivers. Therefore, the regional speed limits and seasonal temperatures need to be considered while designing the tire to achieve minimum aero-acoustic emissions.

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神经网络辅助孤立轮胎的计算气动声学分析

对孤立的乘用车轮胎进行计算气动声学研究，以了解纵向轮胎凹槽尺寸和操作参数（速度和温度）对轮胎噪声的影响。计算流体动力学和声学模型用于获得轮胎周围近场和远场接收器的气动声学轮胎噪声，并使用基于人工神经网络的回归来研究高度非线性和交互的因果关系。系统。基于非定常雷诺平均纳维-斯托克斯的可实现k-epsilon模型用于求解计算域中的流场。Ffowcs Williams and Hawkings 模型用于获得远场位置的气动声学轮胎噪声。频谱分析用于将输出时域转换为频域并获得 A 加权声压级。进行基于人工神经网络的响应面回归，以了解 A 加权声压级与控制变量（凹槽深度、凹槽宽度、温度和速度）之间的偶然关系。在轮胎模型的尾流区域观察到最大 A 加权声压级。相互作用研究表明，通过选择适当的凹槽宽度和凹槽深度组合，可以减少约 10% 的气动声学排放。发现速度与宽度的相互作用对于围绕轮胎的所有接收器的 A 加权声压级最为重要。操作参数的相互作用，即 发现速度和温度对于尾流和前端接收器的 A 加权声压级很重要。因此，在设计轮胎时需要考虑区域速度限制和季节性温度，以实现最小的气动声学排放。