基于物理学的神经网络模拟热腔流的热腔流
Eric Fowler1, Christopher J McDevitt1, Subrata Roy2
1Applied Physics Research Group, University of Florida, Gainesville, Florida, 32611, United States.
Scientific reports
|July 2, 2024
概括
基于物理学的神经网络 (PINNs) 在没有数据的情况下成功模拟了流体动力学. 对于复杂的模拟,PINN是有前途的,尽管更高的维度增加了计算成本.
科学领域:
- 计算流体动力学的流体动力学.
- 机器学习 机器学习
- 应用物理 应用物理
背景情况:
- 基于物理学的神经网络 (PINNs) 为传统模拟方法提供了替代方案.
- PINNs可以与现有的模拟技术集成.
- 本研究探讨了用于前模拟的PINNs,而不依赖于先前存在的数据.
研究的目的:
- 评估PINNs在执行2D自然对流驱动腔的前向模拟中的有效性.
- 研究PINN处理更高维的参数空间的能力 (跨x,z和雷利数域的3D模拟).
- 为了验证PINN结果与自然对流的既定解决方案相比.
主要方法:
- 使用PINNs进行基于纳维尔-斯托克斯方程的旋流函数公式的模拟.
- 在恒定雷利数 (Ra) 的空间 (x,z) 域进行了2D模拟.
- 在空间 (x,z) 和参数 (Ra) 领域进行了3D模拟,以评估更高维度的学习.
主要成果:
- 两种2D和3DPINN模拟都准确地复制了Ra值的公布结果,范围从10^3到10^6.
- 2D模拟中的较高Ra值需要更多的训练代,这表明非线性流体热合更强.
- 3D模拟趋同,但由于维度的诅咒,需要比2D情况下更多的培训.
结论:
- PINNs被验证为标准流体动力学模拟的可行工具.
- 通过PINN,可以证明在传统方法之外探索更高阶参数空间的可行性.
- 在PINN模拟中增加的维度会导致更高的计算负担.
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