Modular deep learning framework for vapor-compression HVAC systems: Scalable, efficient, and physically consistent modeling

doi:10.1016/j.applthermaleng.2026.131100

2026-04-21

PRE

Ali Vaziri

Hongtao Qiao *

Christopher R. Laughman

Huazhen Fang

DOI：10.1016/j.applthermaleng.2026.131100

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摘要

En 中文

• 模块化组件替代品实现跨VCS拓扑的快速重构。• 质量和能量约束确保VCS组件在热流体循环中物理一致的互连。• 尽管组件模型不完美，但实现了稳定且快速的长周期循环模拟。• 单压缩机和双压缩机制热泵系统通过高保真Modelica模型验证。• 稳态下质量和能量守恒。

Keyword:

Modular component surrogates

Vapor-compression systems

Physically consistent modeling

Deep learning framework

Thermofluid cycle simulation

期刊

Applied Thermal Engineering

IF:

6.9

论文数:

2.8W

被引数:

10.6W

机构

mitsubishi electric

学者数:

论文数: 12

被引数: 0

michigan state university

学者数:

3.6W

论文数: 3.2W

被引数: 44

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