加速CFD模拟器的DNN精度改进技术
Accuracy Improvement Technique of DNN for Accelerating CFD Simulator
AI总结：
本文提出两种DNN技术，通过翼型几何信息推断稳态流场并利用面积加权自定义损失函数修正非均匀网格影响，在不损失精度前提下将CFD模拟整体加速至约3.9倍。
摘要：
存在一种计算流体动力学（CFD）方法，通过引入深度神经网络（DNN）推理来降低计算成本。这种降低是通过用DNN推理替代部分计算实现的。成本降低取决于DNN的实现方式以及DNN推理的精度。因此，我们提出了两种适用于在CFD网格上推断流场的技术。第一种技术是从翼型形状推断稳态流场。我们使用网格点坐标位置以及距翼型表面的距离作为DNN的输入信息。第二种方法使用自定义的均方误差作为损失函数。每个网格点所关联区域的面积大小被乘以平方误差。该方法补偿了由于网格点非均匀分布所导致的关联区域面积大小不同而产生的影响。评估结果表明，采用第一种技术的CFD相较于未使用DNN的CFD实现了1.7倍的加速，同时保持了等效的结果质量。通过实现第二种技术，该CFD相较于仅使用第一种技术的CFD进一步实现了2.3倍的加速。
英文摘要：
There is a Computational fluid dynamics (CFD) method of incorporating the DNN inference to reduce the computational cost. The reduction is realized by replacing some calculations by DNN inference. The cost reduction depends on the implementation method of the DNN and the accuracy of the DNN inference. Thus, we propose two techniques suitable to infer flow field on the CFD grid. The first technique is to infer the flow field of the steady state from the airfoil shape. We use the position on the coordinates of the grid point and the distance from the surface of the airfoil as input information for the DNN. The second method uses the customized mean square error as a loss function. The size of the associated area for each grid point was multiplied by the square error. This method compensates for the effect caused by the size of the associated area of nonuniform allocation of grid points. The evaluation results show that the CFD incorporated first technique achieves 1.7x speedup against the CFD without DNN, while maintaining equivalent result quality. By implementing the second technique, the CFD realized further 2.3x speed up against the CFD with first technique only.
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。
