深度强化学习用于控制共轭传热并应用于工件冷却
Deep reinforcement learning for the control of conjugate heat transfer with application to workpiece cooling
摘要：
这项研究评估了深度强化学习（DRL）技术帮助控制由耦合的Navier-Stokes方程和热方程控制的共轭传热系统的能力。它使用了一种新的，“退化”版本的近端策略优化（PPO）算法，旨在用于神经网络学习的最优策略不依赖于状态的情况下，特别是在优化和开环控制问题的情况下。输入到神经网络的数值奖励是使用内部稳定的有限元环境计算的，该环境结合了控制方程的变分多尺度（VMS）建模、浸没体积法和多分量各向异性网格自适应。在二维和三维的自然和强制对流的几个测试用例被用作开发的方法的测试平台。该方法成功地模拟了由侧壁温度分段恒定波动控制的二维差分加热方腔中的自然对流诱导传热增强。它也证明能够提高温度的均匀性，在冲击冷却下的二维和三维热工件的表面。解决了各种情况，其中多个冷空气喷射器的位置相对于固定的工件位置被优化。数值框架的灵活性使其易于解决逆问题，即，以优化相对于固定喷射器分布的工件位置。所得到的结果展示了潜在的黑箱优化方法的实际意义的计算流体动力学（CFD）共轭传热系统。
英文摘要：
This research gauges the ability of deep reinforcement learning (DRL) techniques to assist the control of conjugate heat transfer systems governed by the coupled Navier--Stokes and heat equations. It uses a novel, "degenerate" version of the proximal policy optimization (PPO) algorithm, intended for situations where the optimal policy to be learnt by a neural network does not depend on state, as is notably the case in optimization and open-loop control problems. The numerical reward fed to the neural network is computed with an in-house stabilized finite elements environment combining variational multi-scale (VMS) modeling of the governing equations, immerse volume method, and multi-component anisotropic mesh adaptation. Several test cases of natural and forced convection in two and three dimensions are used as testbed for developing the methodology. The approach successfully alleviates the natural convection induced enhancement of heat transfer in a two-dimensional, differentially heated square cavity controlled by piece-wise constant fluctuations of the sidewall temperature. It also proves capable of improving the homogeneity of temperature across the surface of two and three-dimensional hot workpieces under impingement cooling. Various cases are tackled, in which the position of multiple cold air injectors is optimized relative to a fixed workpiece position. The flexibility of the numerical framework makes it tractable to solve also the inverse problem, i.e., to optimize the workpiece position relative to a fixed injector distribution. The obtained results showcase the potential of the method for black-box optimization of practically meaningful computational fluid dynamics (CFD) conjugate heat transfer systems.
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。
