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MACHINE LEARNING FOR TURBULENCE MODEL DEVELOPMENT USING A HIGH-FIDELITY HPT CASCADE SIMULATION

机译:高精度HPT级联仿真的湍流模型开发机器学习

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The validity of the Boussinesq approximation in the wake behind a high-pressure turbine blade is explored. We probe the mathematical assumptions of such a relationship by employing a least-squares technique. Next, we use an evolutionary algorithm to modify the anisotropy tensor a priori using highly resolved LES data. In the latter case we build a non-linear stress-strain relationship. Results show that the standard eddy-viscosity assumption underpredicts turbulent diffusion and is theoretically invalid. By increasing the coefficient of the linear term, the far-wake prediction shows minor improvement. By using additional non-linear terms in the stress-strain coupling relationship, created by the evolutionary algorithm, the near-wake can also be improved upon. Terms created by the algorithm are scrutinized and the discussion is closed by suggesting a tentative non-linear expression for the Reynolds stress, suitable for the wake behind a high-pressure turbine blade.
机译:探索了高压涡轮机叶片后面的尾流中Boussinesq逼近的有效性。我们通过采用最小二乘法来探究这种关系的数学假设。接下来,我们使用进化算法通过高度解析的LES数据修改各向异性张量。在后一种情况下,我们建立了非线性应力-应变关系。结果表明,标准的涡流粘度假设低估了湍流扩散,在理论上是无效的。通过增加线性项的系数,较远的预测显示出较小的改进。通过在由演化算法创建的应力-应变耦合关系中使用其他非线性项,也可以改善近尾波。对算法创建的术语进行详细审查,并通过建议雷诺应力的暂定非线性表达式来结束讨论,该表达式适用于高压涡轮叶片后面的尾流。

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