对于围绕墙壁的流的大模拟的神经网络:数值实验和挑战
Mark Benjamin1, Stefan P Domino2, Gianluca Iaccarino3
1Center for Turbulence Research, Stanford University, Stanford, CA, USA. markben@stanford.edu.
The European physical journal. E, Soft matter
|July 17, 2023
概括
神经网络显示了改善流的大模拟 (LES) 的前景. 然而,在准确地捕捉子网格尺度和计算前预测中的数值错误方面,仍然存在挑战.
科学领域:
- 流体动力学 流体动力学
- 计算科学 计算科学
- 人工智能的人工智能
背景情况:
- 大模拟 (LES) 对于模拟流非常重要.
- 准确的分网尺度建模对于 LES 忠实性至关重要.
- 传统的基于物理的子网封闭有其局限性.
研究的目的:
- 研究基于神经网络的方法来提高 LES 的准确性.
- 评估神经网络在捕获子网尺度方面的表现.
- 识别应用神经网络到流模拟中的挑战.
主要方法:
- 训练神经网络将流动特征映射到子网尺度上.
- 将神经网络性能与传统的子网关闭进行比较.
- 在先验和后验测试中评估模型.
- 调查数值和离谱化错误的影响.
主要成果:
- 简单的神经网络模型与直接数值模拟数据的相关性很差.
- 准确的先验模型在前期计算中由于数值错误而变得不准确.
- 在后续测试中,计算离谱化错误的网络被证明是不稳定的.
- 分布转移是不考虑数值错误的网络的一个关键挑战.
结论:
- 神经网络方法在 LES 中面临重大挑战,特别是关于数值错误.
- 记载分类错误是必要的,但会导致不稳定.
- 需要进一步的研究来解决分配转移和提高稳健性.
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