使用深度神经网络预测流体流的小规模动态
Dhawal Buaria1,2, Katepalli R Sreenivasan1,3
1Tandon School of Engineering, New York University, New York, NY 11201.
概括
基于物理的深度学习模型小规模的流动力学. 该框架准确地预测了雷诺兹数的速度梯度统计数据,优于传统方法.
科学领域:
- 流体动力学 流体动力学
- 计算物理 计算物理
- 机器学习 机器学习
背景情况:
- 流表现出复杂的,多尺度的动态,使得高雷诺兹数模拟在计算上不可行.
- 精确建模小规模的流运动是非常重要的,因为它们的普遍特征和高计算成本.
- 传统的建模方法在捕捉全方位的小规模流现象方面面临着挑战.
研究的目的:
- 开发一个基于物理的深度学习框架,用于建模和预测小规模流动力学.
- 通过使用深度神经网络,为压力Hessian和粘性Laplacian术语创建功能关闭.
- 将雷诺兹数依赖性和物理约束纳入深度学习模型,以提高准确性.
主要方法:
- 利用基于物理的深度学习来建模流中的速度梯度张量.
- 开发了深度神经网络,以学习压力赫西亚和粘性拉普拉西亚的功能关闭.
- 在雷诺兹数的两个数量级上使用大型直接数值模拟数据库训练和验证模型.
主要成果:
- 深度学习模型成功捕捉并预测小规模的流动力学.
- 该模型准确地预测了可见和不可见的雷诺兹数的速度梯度统计数据.
- 证明了模型能够解释小规模间歇性和雷诺兹数依赖性的能力.
结论:
- 基于物理的深度学习为传统流建模方法提供了可行且强大的替代方案.
- 开发的框架有效地捕捉了流的基本小规模特征.
- 这种方法对推进模拟和理解高雷诺斯数流量有很大的前景.
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