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

机译:基于人工智能的基于智能的蒙特卡罗使用计算流体动力学空气动力学空气动力学的数值模拟

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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(凹槽深度和沟槽宽度)的影响(沟槽深度和沟槽宽度)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(升降系数和系数的系数)。该研究是通过对计算流体动力学(CFD)的神经网络的蒙特卡罗分析进行。计算机实验旨在获得轮胎设计,操作和空气动力学性能参数之间的因果关系。已经采用了基于雷诺平均的Navier-Stokes方程式的可实现的K-EPSilon模型来分析孤立轮胎周围的流动模式的变化。设计参数在宽范围内和完整的因数设计方面变化,同时考虑温度和速度完全探索,以绘制确凿的结果。具有背部传播算法的多层的Perceptron型神经网络训练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与性能指标之间的关系。控制变量的重要性由灵敏度和意义分析确定,并且在所选控制变量之间执行成对的交互分析,以找到相应变量的交互行为。凹槽宽度的设计参数分别具有6.8%和41%的阻力系数减少,并且分别具有4%和35%的速度的速度普通的速度,分别对阻力和升力系数的影响,是最重要的变量。发现空气捕获在纵向凹槽和道路之间遵循光束理论。对于基于空气束概念的升力系数,发现沟槽深度和宽度的相互作用是显着的。发现沟槽宽度和速度的相互作用对于升降和拖动的两个系数具有重要意义。

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