Artificial intelligence in thermal-fluid systems: Data-driven modeling, optimization, and intelligent control — A comprehensive review

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Chinese explanation / 中文解读

中文解读待补充：本站会优先为光伏效率、钙钛矿太阳能电池、储能技术、太阳能热利用、BIPV、并网技术等高价值论文补充中文说明。

Original abstract

Artificial intelligence (AI) has become an active tool in thermal-fluid research, with applications in data-driven modeling, surrogate prediction, design optimization, flow-field reconstruction, and intelligent control. This review examines 160 related publications and groups them into four themes: heat-exchanger design, operation, and maintenance; thermal properties and flow characteristics of advanced working fluids; complex fluid dynamics and multiphase flow; and system-level energy management and control. These studies show that AI can reduce repeated CFD calculations, support fast parameter screening, identify design trade-offs, and improve prediction in selected thermal-fluid problems. Many reported gains are still tied to limited datasets, CFD-generated data, or narrow operating ranges, leaving their extrapolation to new fluids, geometries, and real operating conditions uncertain. More reliable use of AI in this field will require physics-informed and gray-box models, uncertainty quantification, reproducible benchmarks, and validation against experiments or high-fidelity simulations. AI mainly supports modeling, optimization, monitoring, and decision-making; physical models and experiments are still needed to explain mechanisms, test extrapolation, and justify design decisions.

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