Physics-informed neural networks for heat transfer prediction in two-phase flows
Physics-informed neural networks for heat transfer prediction in two-phase flows
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DOI：
10.1016/j.ijheatmasstransfer.2023.125089
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发表时间：
2024
影响因子：
5.2
通讯作者：
Darioush Jalili;Seohee Jang;M. Jadidi;Giovanni Giustini;A. Keshmiri;Y. Mahmoudi
中科院分区：
文献类型：
--
作者：
Darioush Jalili;Seohee Jang;M. Jadidi;Giovanni Giustini;A. Keshmiri;Y. Mahmoudi
本文介绍了数据驱动的两相流体传热过程的模拟。一个物理信息神经网络（PINN）被应用到捕获的行为相界面在两相流和建模的流体动力学和传热的流动配置代表既定的数值测试用例。开发的PINN方法是在基于物理的计算流体动力学（CFD）模拟和界面捕获的模拟数据上进行训练的。本研究考虑的基本问题，包括跟踪一个单一的气泡在密度较大的流体中的上升，并探索在气泡上升接近加热壁的尾迹中的传热。跟踪不同性质的流体的上升气泡相界面，揭示了在界面边缘处的最大误差仅为5.2%，在质心位置处的最大误差为2.8%。推断（隐变量）流量研究除了一个纯粹的外推逆等温气泡的情况下。当没有提供速度数据时，速度场预测仍然准确。上升的推断与看不见的流体性质的等温气泡被发现产生的最大均方误差为0.28和质量中心误差为1.25%。对于上升的气泡与热壁的情况下，在温度域中使用指定的边界条件的最大误差为6.8%，而气泡的位置分析揭示了3.6%的最大位置误差。这些结果表明，PINN是不可知的几何形状和流体性质时，研究对流和浮力的两相流的组合效应的第一次。这项工作作为一个起点PINN在多相的情况下，涉及在一系列的几何形状的传热。最终，PINN将被用于这种情况下，以提供解决方案的正向，反向和外推的情况。与传统的CFD相比，每一种都代表了计算成本的显著节省。
This paper presents data-driven simulations of two-phase fluid processes with heat transfer. A Physics-Informed Neural Network (PINN) was applied to capture the behaviour of phase interfaces in two-phase flows and model the hydrodynamics and heat transfer of flow configurations representative of established numerical test cases. The developed PINN approach was trained on simulation data derived from physically based Computational Fluid Dynamics (CFD) simulations with interface capturing. The present study considers fundamental problems, including tracking the rise of a single gas bubble in a denser fluid and exploring the heat transfer in the wake of a bubble rising close to a heated wall. Tracking of a rising bubble phase interface of fluids with disparate properties was performed, revealing a maximum error of only 5.2% at the interface edge and a maximum error of 2.8% at the position of the centre of mass. Inferred (hidden variable) flows are studied in addition to a purely extrapolative inverse isothermal bubble case. When no velocity data was supplied, velocity field predictions remained accurate. Rise of an inferred isothermal bubble with unseen fluid properties was found to produce a maximum mean-squared error of 0.28 and centre of mass error of 1.25%. For the case of the rising bubble with a hot wall, the maximum error in the temperature domain using specified boundary conditions was 6.8%, while the bubble position analysis reveals a maximum positional error of 3.6%. These results demonstrate that PINN is agnostic to geometry and fluid properties when studying the combined effects of convection and buoyancy on two-phase flows for the first time. This work serves as a starting point for PINN in multiphase cases involving heat transfer over a range of geometries. Eventually, PINN will be used in such cases to provide solutions for forward, inverse, and extrapolative cases. Each of which represent a dramatic saving in computational cost compared to traditional CFD.