Nanofluid-Enhanced HVAC&R Systems (2015–2025): Experimental, Numerical, and AI-Driven Insights with a Strategic Roadmap

Aung Myat (), Md Mashiur Rahman and Muhammad Akbar
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Aung Myat: Department of Mechanical and Manufacturing Engineering, Tennessee State University, Nashville, TN 37209, USA
Md Mashiur Rahman: Department of Mechanical and Manufacturing Engineering, Tennessee State University, Nashville, TN 37209, USA
Muhammad Akbar: Department of Mechanical and Manufacturing Engineering, Tennessee State University, Nashville, TN 37209, USA

Sustainability, 2025, vol. 17, issue 16, 1-53

Abstract: Heating, ventilation, air conditioning, and refrigeration (HVAC&R) systems account for a significant share of global energy demand, prompting intensive research into advanced thermal enhancement techniques. Among these, nanofluids—colloidal suspensions of nanoparticles in base fluids—have shown promise in boosting heat transfer performance. This review provides a structured and critical evaluation of nanofluid applications in HVAC&R systems, synthesizing research published from 2015 to 2025. A total of 200 peer-reviewed articles were selected from an initial pool of over 900 through a systematic filtering process. The selected literature was thematically categorized into experimental, numerical, hybrid, and AI/ML-based studies, with further classification by fluid type, performance metrics, and system-level relevance. Unlike prior reviews focused narrowly on thermophysical properties or individual components, this work integrates recent advances in artificial intelligence and hybrid modeling to assess both localized and systemic enhancements. Notably, nanofluids have demonstrated up to a 45% improvement in heat transfer coefficients and up to a 51% increase in the coefficient of performance (COP). However, the review reveals persistent gaps, including limited full-system validation, underexplored real-world integration, and minimal use of AI for holistic optimization. By identifying these knowledge gaps and research imbalances, this review proposes a forward-looking, data-driven roadmap to guide future research and facilitate the scalable adoption of nanofluid-enhanced HVAC&R technologies.

Keywords: nanofluids; HVAC & refrigeration; heat transfer enhancement; AI/ML modeling; energy efficiency (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2025
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