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

机译:利用高保真HPT级联模拟机器学习湍流模型开发

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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 farwake 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:探索了高压涡轮叶片后面唤醒的Boussinesq近似的有效性。通过采用最小二乘技术探测这种关系的数学假设。接下来,我们使用高度解析的LES数据来使用进化算法修改各向异性张力的先验。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,标准涡流粘度假设造成湍流扩散,从理论上无效。通过提高线性术语的系数,法浪预测显示了微小的改进。通过在应力 - 应变耦合关系中使用额外的非线性术语,由进化算法产生,也可以改善近似棘爪。通过算法产生的术语被仔细审查,并且通过建议雷诺应力的暂定非线性表达,适用于高压涡轮叶片后面的唤醒来关闭讨论。

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