Cova-PINN：面向复杂几何结构中流固共轭传热的跨域守恒物理信息神经网络
Cova-PINN: Cross-Domain Conservation Physics-Informed Neural Network for Fluid-Solid Conjugate Heat Transfer in Complex Geometries
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中文总结 AI 辅助
本文针对标准多域PINN求解流固共轭传热时能量传递与出口温度计算不准确的问题，提出Cova-PINN框架，通过对齐守恒支撑与热路径优化跨域平衡，在4种TPMS换热器及DualMS设计上较基准取得显著误差降低与精度提升。
中文摘要 AI 辅助
多域物理信息神经网络（PINN）可灵活建模介质特定表示以求解流固共轭传热（CHT）问题。然而，标准多域PINN在单独采样的域支撑上施加控制方程和界面条件，虽能生成看似合理的温度场，但端到端能量传递和出口温度计算不准确。本文提出Cova-PINN，一种多域PINN框架，其将守恒支撑与复杂几何结构中的热相互作用路径对齐。Cova-PINN在局部尺度联合优化跨域复合控制体积平衡，在全局换热器尺度联合优化配对壁面闭合条件。本文在4种三重周期极小曲面（TPMS）换热器及几何结构不同的DualMS设计上，按统一协议，针对CHT专用、面向优化及复杂几何的PINN基准对Cova-PINN进行评估。与最接近的基准MUSA-PINN-CHT相比，Cova-PINN在4种TPMS拓扑上分别将平均出口温度误差和器件级闭合误差降低37.7%和60.2%，同时提升全场和热负荷精度，在DualMS上也取得一致增益。
英文摘要
Multi-domain physics-informed neural networks (PINNs) flexibly model medium-specific representations to solve fluid--solid conjugate heat transfer (CHT). However, standard multi-domain PINNs enforce governing equations and interface conditions on separately sampled domain supports, which can yield plausible temperature fields but inaccurate end-to-end energy transfer and outlet temperatures. We propose Cova-PINN, a multi-domain PINN framework that aligns conservation support with thermal interaction paths in complex geometries. Cova-PINN jointly optimizes cross-domain composite control-volume balances at the local scale and paired-wall closure at the global exchanger scale. We evaluate Cova-PINN on four triply periodic minimal surface (TPMS) heat exchangers and a geometrically distinct DualMS design against CHT-specific, optimization-oriented, and complex-geometry PINN baselines under a common protocol. Relative to the closest baseline, MUSA-PINN-CHT, Cova-PINN reduces average outlet-temperature and device-level closure errors across the four TPMS topologies by $37.7\%$ and $60.2\%$, respectively, while also improving full-field and heat-duty accuracy, with consistent gains on DualMS.
发表机构
- Shandong University（山东大学）
- Tsinghua University（清华大学）
机构由 AI 辅助整理，请以论文原文为准。
