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arXiv 2404.19035physics.flu-dyn
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用于预测RANS模型中分离起始的改进型压力梯度传感器

Improved pressure-gradient sensor for the prediction of separation onset in RANS models

Kevin Patrick Griffin, Ganesh Vijayakumar, Ashesh Sharma, Michael A. Sprague

AI总结:

本研究改进Menter SST湍流模型的逆压梯度传感器与涡粘性模型,结合数值模拟与RANS校准,提升了风力涡轮机厚翼型的失速预测精度。

摘要:

我们对Menter剪切应力输运(SST)湍流模型的两个关键方面进行了改进:(1)基于局部平均流动方向上的压力梯度强度,提出了一种鲁棒性更强的逆压梯度传感器;(2)提出了两种可替代的涡粘性模型,用于由我们的传感器识别出的逆压梯度区域。研究采用Boeing Gaussian bump的直接数值模拟来确定基准SST模型中需要修正的项,并通过后验雷诺平均Navier-Stokes计算校准系数值,最终得到兼具物理驱动和数据驱动特性的模型。两款搭载该传感器的模型被应用于两款代表现代风力涡轮机应用的厚翼型——FFA-W3-301和DU00-W-212。结果表明,相较于基准SST模型的预测结果,所提出的模型提升了失速(分离起始)的预测效果。

英文摘要:

We improve upon two key aspects of the Menter shear stress transport (SST) turbulence model: (1) We propose a more robust adverse pressure gradient sensor based on the strength of the pressure gradient in the direction of the local mean flow; (2) We propose two alternative eddy viscosity models to be used in the adverse pressure gradient regions identified by our sensor. Direct numerical simulations of the Boeing Gaussian bump are used to identify the terms in the baseline SST model that need correction, and a posteriori Reynolds-averaged Navier-Stokes calculations are used to calibrate coefficient values, leading to a model that is both physics driven and data informed. The two sensor-equipped models are applied to two thick airfoils representative of modern wind turbine applications, the FFA-W3-301 and the DU00-W-212. The proposed models improve the prediction of stall (onset of separation) with respect to the prediction of the baseline SST model.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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