发表机构
Université Grenoble Alpes, CNRS, GIPSA-lab; Université Côte d'Azur, CNRS, I3S（格勒诺布尔阿尔卑斯大学; 蔚蓝海岸大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出一种混合物理信息与数据驱动的框架，用于逆流换热器的建模、辨识与控制，通过分层控制策略实现热流率的有效调节，实验验证拟合度超94%且流量变化减半。
AI 中文摘要
换热器的高级运行在高效可持续多向量能源系统的发展中起着关键作用。本文提出了一种混合物理信息与数据驱动的框架，用于逆流换热器中瞬态传热的分析、辨识与调节。参数化物理模型结合经典传热关系与输运方程，以捕捉对流传播、输运引起的延迟以及与流量变化相关的非线性效应。传热系数从实验数据中辨识，从而能够在宽运行范围内表征与流量相关的传热动力学。随后，利用辨识模型开发了一种分层实时控制策略。内环使用高阶数据驱动线性模型调节体积流量，而外环使用机器学习控制器调节交换热功率，该控制器最初基于物理模型训练，随后用实验数据细化。实验结果表明，在不同热边界条件下，瞬态传热动力学得以准确再现，且热流率调节有效（辨识拟合度在归一化均方根误差中高于94%，与比例积分控制器相比，流量总变化减少一半）。所提出的框架结合了物理可解释性与数据驱动适应性，用于动态传热系统的分析与实时运行。
英文摘要
The advanced operation of heat exchangers plays a critical role in the development of efficient and sustainable multivector energy systems. This paper proposes a hybrid physics-informed and data-driven framework for the analysis, identification, and regulation of transient heat transfer in counter-flow heat exchangers. A parameterized physical model combines classical heat-transfer relations with transport equations to capture convective propagation, transport-induced delays, and nonlinear effects associated with flow-rate variations. The heat-transfer coefficient is identified from experimental data, enabling the characterization of flow-dependent heat-transfer dynamics over a broad operating range. The identified model is then used to develop a hierarchical real-time control strategy. An inner loop regulates the volumetric flow rate using a high-order data-driven linear model, while an outer loop regulates the exchanged heat power using a machine-learning controller initially trained on the physical model and subsequently refined with experimental data. Experimental results demonstrate accurate reproduction of transient heat-transfer dynamics and effective heat-flow-rate regulation under varying thermal boundary conditions (identification fit above 94\% in normalized root-mean-square error and total variation of the flow rate reduced by half compared to a proportional-integral controller). The proposed framework combines physical interpretability with data-driven adaptation for the analysis and real-time operation of dynamic heat-transfer systems.
