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Artificial intelligence-based Monte-Carlo numerical simulation of aerodynamics of tire grooves using computational fluid dynamics

机译:基于人工智能的蒙特卡洛轮胎凹槽空气动力学数值模拟,采用计算流体力学

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In the current work, the effects of design (groove depth and groove width) and operational (temperature and velocity) parameters on aerodynamic performance parameters (coefficient of drag and coefficient of lift) of an isolated passenger car tire have been investigated. The study is conducted by using neural network-based Monte-Carlo analysis on computational fluid dynamics (CFD). The computer experiments are designed to obtain the causal relationship between tire design, operational, and aerodynamic performance parameters. The Reynolds-averaged Navier-Stokes equations-based Realizable K-epsilon model has been employed to analyze the variations in flow patterns around an isolated tire. The design parameters are varied over wide range and full factorial design, while considering temperature and velocity is completely explored to draw conclusive results. The multi-layer perceptron type neural network with the back-propagation algorithm is trained to map any non-linearity in causal relationships. The sensitivity analysis is performed to find the relationship between control variables and performance indicators. The importance of control variable is determined by both sensitivity and significance analyses and the paired interaction analysis is performed between selected control variables to find the interactive behavior of corresponding variables. The design parameter of groove width with 6.8% and 41% reduction in drag and lift coefficient, respectively, and conventionally overlooked operational parameter of velocity with 4% and 35% impact on drag and lift coefficient, respectively, are found to be the most significant variables. The air trapped between the longitudinal grooves and the road is found to follow the beam theory. The interaction of the groove depth and width is found to be significant with respect to coefficient of lift based on the air beam concept. The interaction of groove width and velocity is found to be significant with respect to both coefficients of lifts and drag.
机译:在当前的工作中,已经研究了设计(凹槽深度和凹槽宽度)和操作(温度和速度)参数对隔离乘用车轮胎的空气动力学性能参数(阻力系数和升力系数)的影响。该研究是通过使用基于神经网络的蒙特卡洛分析对计算流体动力学(CFD)进行的。设计计算机实验是为了获得轮胎设计,操作和空气动力学性能参数之间的因果关系。基于雷诺平均Navier-Stokes方程的可实现K-ε模型已用于分析隔离轮胎周围的流型变化。设计参数在宽范围内和全因子设计中变化,同时充分考虑温度和速度以得出结论性结果。带有反向传播算法的多层感知器型神经网络经过训练,可以映射因果关系中的任何非线性。执行灵敏度分析以找到控制变量和性能指标之间的关系。控制变量的重要性通过敏感性和重要性分析来确定,并且在选定的控制变量之间执行配对的交互分析,以找到相应变量的交互行为。发现槽宽的设计参数分别是阻力和升力系数分别降低6.8%和41%以及传统上被忽略的速度操作参数(分别对阻力和升力系数有4%和35%的影响)的设计参数变量。发现被困在纵向凹槽和道路之间的空气遵循梁理论。基于空气束概念,发现凹槽深度和宽度的相互作用对于升力系数很重要。已发现,相对于升力和阻力系数,凹槽宽度和速度之间的相互作用非常重要。

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