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

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.

1. Introduction

Heating, ventilation, and air conditioning (HVAC) systems account for a substantial portion of global energy consumption, particularly in the residential, commercial, and industrial building sectors. According to the International Energy Agency (IEA) [1], HVAC systems contribute to nearly 40% of total building energy use worldwide, making them a critical focus for energy efficiency improvements. As global temperatures rise and urbanization intensifies, the demand for HVAC systems is projected to increase significantly, further stressing energy infrastructure and contributing to greenhouse gas emissions.

To address these pressing concerns, researchers and engineers have sought to improve the thermal efficiency of HVAC components, particularly through enhancements in heat transfer mechanisms. One of the most effective and widely explored strategies involves upgrading conventional heat transfer fluids—such as water, ethylene glycol, and oils—with advanced working fluids that exhibit superior thermophysical properties [2]. In this context, nanofluids—a class of engineered colloidal suspensions of nanometer-sized particles (e.g., metals, oxides, carbon nanotubes) in a base fluid—have gained significant attention over the past two decades for their potential to revolutionize heat transfer technologies [3].

Nanofluids offer several theoretical advantages over traditional fluids, including anomalously high thermal conductivity (TC) at low nanoparticle concentrations, improved convective heat transfer coefficients, and the potential for microchannel cooling applications [4,5]. These benefits make them attractive candidates for integration into a wide range of HVAC components, such as heat exchangers, radiators, evaporators, and solar collectors. Furthermore, the ability to tailor nanofluid properties through careful selection of the nanoparticle material, size, morphology, and concentration provides unique opportunities for system-level optimization and performance customization [6]. As the field has evolved, a growing body of the literature highlights both the profound promise and the practical challenges—such as long-term stability, increased viscosity, and cost-effectiveness—of using nanofluids in real-world HVAC applications [7]. Despite the growing body of work, existing reviews often lack integration across methodological domains and fall short in addressing the translational gap between laboratory-scale findings and real-world HVAC deployment. Key challenges include the long-term stability of nanofluids under cyclic operation, viscosity-driven pressure penalties, fouling, lifecycle cost, and system compatibility. This review distinguishes itself by not only mapping experimental, numerical, and AI/ML-based methodologies but also by introducing a novel AI-driven system optimization roadmap, offering a strategic synthesis tailored to building energy applications. Unlike prior reviews that focus narrowly on nanofluid thermophysical properties or generalized thermal systems, this work provides an HVAC-specific, application-driven synthesis. It uniquely combines experimental, numerical, and AI/ML-based insights into a unified methodological framework, aligned with building energy performance goals. Additionally, recent studies have employed entropy generation analysis to evaluate the thermodynamic efficiency of nanofluids, especially in hybrid configurations. These approaches offer valuable insight into irreversibility minimization and the optimization of system performance and are discussed in Section 2.

This review aims to critically examine the current state of nanofluid research within HVAC systems by organizing the literature thematically across key application domains. It will synthesize experimental and computational findings, assess performance metrics, and identify future research directions to support the development of high-efficiency, nanofluid-enhanced HVAC technologies.

1.1. Current Gaps in Review of Nanofluids Applications

Over the past five years, several review articles have investigated the intersection of nanofluids and advanced modeling tools, particularly artificial intelligence (AI), machine learning (ML), and numerical simulation techniques. For instance, Riyadi et al. (2024) [8] focused on ML-enhanced porous heat exchangers and sustainable energy systems, offering a computational taxonomy but limited direct applicability to HVAC&R. Basu et al. (2023) [9] and Raza et al. (2024) [10] provided detailed overviews of AI-driven thermophysical property prediction for nanofluids, while Smrity and Yin (2025) [11] emphasized the synergy between experimental data and digital modeling across general heat transfer applications.

Despite their merits, these reviews exhibit several limitations that constrain their practical impact on the HVAC and refrigeration (HVAC&R) sector:

• Lack of HVAC&R-Specific Focus: Prior reviews remain largely disconnected from HVAC&R system architectures. None offers a comprehensive assessment of nanofluid integration into evaporators, condensers, compressors, or performance indicators such as the coefficient of performance (COP), cooling load, or pressure drop under real-world operation.
• Emphasis on Isolated Property Prediction: AI and ML tools have been primarily applied to predict isolated thermophysical properties (e.g., viscosity, thermal conductivity), without bridging the implications of these predictions for full-system modeling, design optimization, or operational energy performance in HVAC settings.
• Absence of Methodological Taxonomy: While some papers classify studies by nanoparticle type or performance metric, a structured methodological synthesis—categorizing experimental, numerical, hybrid, and AI-based investigations—is missing. Such a taxonomy is critical to identify research synergies and implementation pathways in HVAC&R.
• Neglect of Practical and Lifecycle Challenges: Viscosity-induced pressure penalties, long-term nanoparticle stability, fouling, and cost-efficiency trade-offs are acknowledged but not critically analyzed in most prior reviews. For instance, optimization is discussed by Basu et al. (2023) [9], yet lacks connection to lifecycle sustainability or technoeconomic feasibility.
• Missing Integration with Smart HVAC Technologies: None of the cited reviews incorporate nanofluid research within the evolving context of smart HVAC systems, including digital twins, adaptive control, or real-time energy analytics—a direction increasingly relevant to energy-aware building management.

These gaps underscore the need for a domain-focused, methodologically structured, and deployment-oriented review that evaluates nanofluid applications in HVAC&R from both technical and practical lenses. The present review aims to fill this void by offering a layered synthesis that spans material properties, performance metrics, methodological approaches, and AI-driven system integration.

To contextualize the unique contributions of this review, it is essential to benchmark it against the existing literature. While several recent reviews have addressed aspects of nanofluid applications and machine learning techniques, their scopes often remain confined to either predictive modeling or generalized thermal systems without targeted relevance to HVAC and refrigeration.

Table 1. Comparative scope and contribution of recent review articles on nanofluids vs. this study.

Feature/Focus Area	Riyadi et al. (2024) [8]	Basu et al. (2023) [9]	Smrity & Yin (2025) [11]	This Study (Present Review)
Application Domain	Porous heat exchangers, solar systems	General heat transfer/nanofluid systems	General modeling–experiment synergy	HVAC&R-specific (evaporators, condensers, COP)
AI/ML Integration	Partial (heat exchanger optimization)	Yes (thermophysical property prediction)	Yes (surrogate modeling, data fitting)	Yes (component and system-level modeling + taxonomy)
Methodological Classification	No	No	No	Yes (experimental, numerical, hybrid, AI/ML)
System-Level Analysis	No	No	No	Yes (COP, pressure drop, energy efficiency)
Lifecycle and Technoeconomic Considerations	Brief mention	Not addressed	Not addressed	Discussed (stability, cost, viscosity trade-offs)
Focus on Smart HVAC Integration (e.g., digital twins)	No	No	No	Yes (emerging AI-enabled HVAC technologies)
Research Gaps and Future Roadmap	General	General	General	Detailed and domain-specific

presents a comparative assessment of key review articles, highlighting their primary focus areas, methodological breadth, and thematic limitations. This contrast makes clear the distinctive value of the present work, which offers a structured, HVAC-specific synthesis that not only incorporates experimental, numerical, and hybrid studies but also advances the field through integration of lifecycle considerations, technoeconomic insights, and emerging smart HVAC technologies.

To further contextualize the unique contributions of this review, a conceptual Venn diagram, as shown in Figure 1, is presented to highlight the methodological and thematic intersections explored. While the recent literature often focuses on isolated aspects such as predictive modeling or experimental evaluations, this study deliberately bridges those domains with HVAC&R-specific integration and practical deployment insights.

1.2. Motivation for This Work

This review is motivated by the need for a unified, application-focused understanding of nanofluid technologies specifically tailored to HVAC&R systems. Rather than offering another general survey, this work is purposefully structured to bridge experimental advances, AI-driven modeling, and real-world deployment concerns—domains that have remained siloed in prior studies.

Key features of this review include:

• Component-Specific Relevance: Direct mapping of nanofluid performance metrics to HVAC&R components (e.g., evaporators, condensers) and system-level outputs like COP and thermal load reduction.
• Methodologically Balanced Taxonomy: A structured classification across experimental, numerical, hybrid, and AI/ML-based studies that enables comparative analysis of their strengths, limitations, and evolution.
• Actionable Design Guidance: Synthesized findings are organized by nanoparticle type, base fluid, and operating condition to support engineering design and optimization.
• Deployment-Aware Framing: Particular attention is given to practical challenges—pressure drop penalties, sedimentation, economic feasibility, and integration into smart HVAC systems.

Overall, this review serves not only as a scholarly synthesis but also as a practical reference for researchers, designers, and energy system engineers aiming to implement nanofluid-based enhancements in HVAC&R environments.

1.3. Novelty of This Review Paper

The novelty of this review lies in its application-driven, methodologically structured, and HVAC-specific focus—a combination largely absent in the prior literature. Compared to existing reviews that often generalize nanofluids within broader thermal or materials science contexts, this paper offers the following unique contributions:

• HVAC&R Component-Level Focus: This review uniquely concentrates on the integration of nanofluids into specific HVAC&R components—such as condensers, evaporators, compressors, and heat exchangers—linking them to system-level outcomes like coefficient of performance (COP), heat transfer rates, and energy efficiency.
• Structured Methodological Classification: More than 200 peer-reviewed studies are systematically categorized under a comprehensive framework spanning experimental, numerical, theoretical, hybrid, and AI/ML-based methodologies. This taxonomy allows clear benchmarking of research directions and evidence-based insights for future investigations.
• Comparative and Application-Specific Synthesis: The review presents clustered performance comparisons based on nanoparticle types, base fluids, and operational parameters relevant to HVAC&R environments. This structured synthesis aids researchers and engineers in identifying optimal nanofluid configurations for specific use cases.
• Emphasis on Smart HVAC Integration: Beyond conventional heat transfer evaluation, the paper advances the field by incorporating recent developments in AI, digital twins, and intelligent HVAC controls—areas typically underrepresented in past reviews—thus creating a pathway toward smart, adaptive thermal systems.

By bridging methodological rigor with real-world applicability, this review establishes a research foundation that is not only academically comprehensive but also actionable for next-generation HVAC technologies.

1.4. Organization of This Paper

The remaining parts of this review is organized thematically and methodologically to provide a structured and application-oriented synthesis of nanofluid research relevant to HVAC and refrigeration systems. The paper is divided into four main sections following the introduction:

• Section 2 presents the core thematic review of nanofluid applications in HVAC systems, categorized into three subdomains:
• Section 2.1 focuses on Thermophysical Properties, evaluating both numerical (Section 2.1.1) and experimental (Section 2.1.2) studies, followed by a Critical Analysis and Research Gaps subsection (Section 2.1.3).
• Section 2.2 reviews advances in Heat Transfer Enhancement, structured into Experimental Studies (Section 2.2.1), Numerical Studies (Section 2.2.2), and AI/ML-Driven Approaches (Section 2.2.3), culminating in a Critical Analysis and Future Directions (Section 2.2.4).
• Section 2.3 synthesizes the broader Research Methodologies and Analytical Trends across the literature, including Experimental Studies (Section 2.3.1), Numerical Investigations (Section 2.3.2), Hybrid Studies (Section 2.3.3), AI/ML-Based Approaches (Section 2.3.4), and Review-Based Papers (Section 2.3.5). It concludes with a Critical Analysis and Future Directions subsection (Section 2.3.6) that integrates methodological gaps and forward-looking insights.
• Section 3 provides a consolidated Conclusion and Future Directions, summarizing the key insights from the review while proposing actionable research trajectories and system-level recommendations for nanofluid deployment in next-generation HVAC&R technologies.

This organization facilitates both a comprehensive understanding of the current research landscape and a practical roadmap for researchers, engineers, and technology developers aiming to translate nanofluid innovations into real-world HVAC applications.

2. Thematic Review of Nanofluid Applications in HVAC Systems

The successful integration of nanofluids into HVAC systems extends beyond their enhanced thermophysical properties. It depends fundamentally on how these improvements translate into measurable performance gains across diverse HVAC components. From compact heat exchangers to district-scale cooling loops, each subsystem presents distinct thermal, hydraulic, and operational challenges that shape the effectiveness of nanofluid utilization. Therefore, understanding nanofluid performance requires a system-specific lens, accounting for variations in flow regime, operating temperature, material compatibility, and long-term stability.

To ensure a focused and high-quality synthesis of the relevant literature, a structured filtering and categorization process was implemented. An initial search across the Scopus database yielded 907 articles published between 2015 and 2025, using compound Boolean keywords that included HVAC, refrigeration, nanofluids, nanoparticles, and heat transfer enhancement. After applying filters to limit the scope to peer-reviewed journal articles and reviews within the engineering and energy domains and written in English, the dataset was reduced to 347 articles.

These remaining articles were further assessed for thematic relevance. Articles that were unrelated to nanofluid applications in thermal systems or represented marginal research clusters (e.g., <10 articles) were excluded. The final selection contained 200 articles, thematically grouped into three core categories:

• Refrigeration, Air Conditioning, and HVAC Systems (main application theme, n = 133),
• Heat Transfer Enhancement (supporting subtheme 1, n = 46), and
• Thermophysical Properties (supporting subtheme 2, n = 21).

The filtering logic and thematic breakdown are illustrated in Figure 2, which provides a visual summary of the literature search strategy, exclusion criteria, and retained themes. Each theme was identified through the literature clustering and reinforced using targeted keyword searches in Scopus. The number of articles selected per theme reflects both the research volume and relevance after exclusion criteria were applied. These categories inform the structure of the following sections, enabling a targeted and critical analysis of nanofluid performance across HVAC&R applications.

Within each thematic domain, findings from experimental evaluations, numerical simulations, and hybrid approaches are synthesized to assess performance trends, implementation challenges, and research gaps. This framework provides a structured foundation for understanding the practical relevance and research trajectory of nanofluids across HVAC&R systems.

2.1. Thermophysical Properties

The evaluation of thermophysical properties is fundamental to understanding the suitability and performance of nanofluids in HVAC&R systems. These properties, particularly thermal conductivity, viscosity, density, and specific heat, govern how nanofluids interact with heat exchangers, pumps, and flow circuits [12]. Their accurate characterization under real and simulated operating conditions forms the basis for predicting energy transfer efficiency and system behavior.

2.1.1. Numerical Investigations

Numerical studies serve as the backbone for modeling nanofluid behavior under controlled and parametric variations, providing insight into performance outcomes that would be costly or impractical to capture experimentally. In the context of HVAC&R applications, numerical models are widely used to estimate thermal conductivity, effective viscosity, specific heat capacity, and entropy generation based on nanofluid formulation, temperature, and flow conditions.

Several studies leverage finite volume and finite element approaches to solve coupled momentum and energy equations under laminar and turbulent flow conditions. For instance, Ali et al. (2025) [13] modeled entropy generation in nanofluid-saturated porous channels, incorporating slip velocity and thermodiffusion effects. Their simulations showed that entropy generation could be minimized by optimizing nanoparticle concentration and Darcy number, especially under low Reynolds number conditions typical of HVAC&R circulation loops.

Abbas (2025) [10] used a rotating wavy porous channel geometry to investigate heat transport of Al₂O₃ and hybrid nanofluids. The model revealed that rotational motion and porous structure synergistically enhanced thermal dispersion. Similarly, Gao et al. (2024) [14] conducted a numerical assessment of nanofluid ice slurries, concluding that hybrid formulations could improve cold energy storage density by over 15% without significantly increasing flow resistance.

Other studies incorporated hybrid nanoparticles, including Fe₃O₄-CoFe₂O₄ and carbon-based additives. These simulations indicated synergistic thermal benefits, particularly when particle shapes (e.g., cylindrical or platelet-like) and surface treatments were accounted for. Most simulations assume homogeneous dispersion and thermophysical equilibrium, which, while simplifying the problem, introduces limitations when comparing against real-world conditions.

Overall, numerical investigations affirm that thermal conductivity increases nonlinearly with nanoparticle volume fraction, but at the cost of rising viscosity—especially above 1% vol. Several models now include temperature-dependent property correlations, Brownian motion, and thermophoresis to improve predictive accuracy under transient HVAC&R conditions.

2.1.2. Experimental Investigations

Though fewer in number, experimental studies offer indispensable validation for simulation-based predictions and provide real-world insight into nanofluid behavior under operational constraints. Such investigations typically focus on measuring effective thermal conductivity, viscosity, and, less frequently, specific heat capacity and density, using various test setups including guarded hot plate, transient hot wire, and viscometric analyses.

Lakshmi et al. (2025) [15] synthesized Fe₃O₄-CoFe₂O₄ nanofluids and characterized them under porous media flow. Their experiments confirmed up to 20% improvement in thermal conductivity at 0.5 vol.% particle loading, with minimal sedimentation over 72 h, attributed to uniform magnetic particle dispersion. However, a corresponding 15–25% increase in viscosity was observed, which could pose challenges for pumping power and flow rate regulation in HVAC&R systems.

Su et al. (2025) [16] performed both experimental and computational assessments of hybrid nanofluids containing TiO₂ and graphene nanoparticles. Their experimental results confirmed a 12–18% enhancement in thermal conductivity, highlighting the importance of nanoparticle morphology and base fluid compatibility. The study also demonstrated that surfactant selection (e.g., SDS, PVP) had a direct impact on long-term stability and the control of aggregation.

A recurring challenge noted across studies is the tendency for nanoparticles to agglomerate or settle over time, especially in systems subject to cyclic heating and cooling, such as HVAC&R loops. This not only affects the thermophysical consistency of the nanofluid but also undermines heat exchanger performance and operational safety. Moreover, accurate measurement of specific heat and dynamic viscosity at low temperature gradients—a typical condition in HVAC&R applications—remains limited due to instrumentation sensitivity and calibration challenges.

2.1.3. Critical Analysis and Research Gaps

A comparative synthesis of the reviewed numerical and experimental studies confirms broad agreement on the trends associated with nanofluid thermophysical behavior. Thermal conductivity enhancement is consistently reported as the primary motivation for nanofluid use, with most studies showing 10–30% improvement, depending on nanoparticle concentration, temperature, and fluid type. However, this enhancement is frequently accompanied by undesirable increases in viscosity, especially beyond 1% vol. loading [17], which imposes a penalty on pumping power and flow dynamics. Figure 3 summarizes the frequency of thermophysical properties mentioned in the reviewed literature.

Despite the progress made, several gaps and inconsistencies persist between numerical predictions and experimental validation:

• Idealized assumptions in simulations (e.g., uniform dispersion, constant properties) often overlook real-world phenomena such as agglomeration, phase separation, and surfactant degradation.
• Long-term operational studies assessing thermal cycling effects, corrosion, and stability under HVAC&R-like conditions remain scarce.
• Viscosity and specific heat are underexplored in terms of their coupled impact on system performance, especially under low-to-moderate temperature gradients typical of HVAC&R loops.

To provide quantitative insight into material preferences in the literature, Table 2 categorizes reviewed studies based on nanoparticle types, the number of publications, their methodological approach (numerical, experimental, or review), and representative findings. It highlights the research emphasis and identifies trends and gaps in nanofluid thermophysical characterization relevant to HVAC&R applications.

Table 2. Summary of nanoparticles studied in thermophysical property investigations, with study types and representative findings.

Ref.	Nanoparticle Type	Study Type Distribution	Representative Findings
[18,19,20,21,22]	Al2O3, SiO2	Numerical	• Density is a less studied property vs. TC and viscosity.
[13,17,23,24]	CuO	Numerical, Review	• TC ↑ by ~57%; COP ↑ by ~27% (with R134a/R152a). • Minor viscosity increase observed.
[15,23,25,26]	Fe3O4-CoFe2O4	Experimental, Numerical	• Investigated MHD effects on heat transfer properties.
[16,27]	Graphene	Experimental, Experimental/Numerical, Numerical	• ↑ Concentration enhances TC and heat transfer. • ↑ Concentration also increases viscosity and friction.
[20,22,23,24,28]	TiO2	Experimental	• Studied heating effects on viscosity, TC, and absorbance.
[14,29,30]	Hybrid/Other	Numerical, Review	Improving adsorbent TC and heat exchange area for compact system optimization.
[31,32]	Nanolubricant, Refrigerant	Review	Analyzing thermophysical and hydraulic parameters to enhance refrigeration system COP.

Table 2. Summary of nanoparticles studied in thermophysical property investigations, with study types and representative findings.

Ref.	Nanoparticle Type	Study Type Distribution	Representative Findings
[18,19,20,21,22]	Al2O3, SiO2	Numerical	• Density is a less studied property vs. TC and viscosity.
[13,17,23,24]	CuO	Numerical, Review	• TC ↑ by ~57%; COP ↑ by ~27% (with R134a/R152a). • Minor viscosity increase observed.
[15,23,25,26]	Fe3O4-CoFe2O4	Experimental, Numerical	• Investigated MHD effects on heat transfer properties.
[16,27]	Graphene	Experimental, Experimental/Numerical, Numerical	• ↑ Concentration enhances TC and heat transfer. • ↑ Concentration also increases viscosity and friction.
[20,22,23,24,28]	TiO2	Experimental	• Studied heating effects on viscosity, TC, and absorbance.
[14,29,30]	Hybrid/Other	Numerical, Review	Improving adsorbent TC and heat exchange area for compact system optimization.
[31,32]	Nanolubricant, Refrigerant	Review	Analyzing thermophysical and hydraulic parameters to enhance refrigeration system COP.

2.2. Heat Transfer Enhancement

Nanofluids have emerged as highly effective thermal carriers for enhancing heat transfer across various components of HVAC&R systems. Their ability to significantly increase thermal conductivity, improve convective heat transfer coefficients, and reduce the size and weight of heat exchangers has prompted extensive investigation. These enhancements arise from several fundamental mechanisms, including enhanced thermal conductivity from high-conductivity nanoparticles, Brownian motion-induced microscale mixing, thermophoresis under strong temperature gradients, and microconvection generated by particle movement. The relative contribution of these mechanisms depends on the nanoparticle type, base fluid properties, concentration, and flow regime, and they often act synergistically to boost performance.

Nanofluids have emerged as highly effective thermal carriers for enhancing heat transfer across various components of HVAC&R systems. Their ability to significantly increase thermal conductivity, improve convective heat transfer coefficients, and reduce the size and weight of heat exchangers has prompted extensive investigation. The improvement is driven by several underlying physical mechanisms:

• Brownian motion—Random motion of nanoparticles at the microscale promotes enhanced mixing in the fluid, disrupting thermal boundary layers and facilitating heat transport.
• Thermophoresis—Migration of nanoparticles from hotter to cooler regions due to temperature gradients, redistributing thermal energy more effectively.
• Microconvection—Localized eddies generated by moving nanoparticles amplify fluid agitation and improve convective heat transfer rates.
• Enhanced thermal conductivity—Nanoparticles with inherently higher conductivity than the base fluid (e.g., Al₂O₃, CuO, graphene) form efficient thermal pathways, accelerating heat transport through the fluid.
• Field-induced effects (optional in specific designs)—Magnetic or electric fields applied to specialized nanofluids can align particles or induce additional motion, further modifying heat transfer behavior.
• Viscous dissipation and porous media effects—Relevant in some configurations where flow resistance or porous structures influence the local energy balance.

Among these, Brownian motion, thermophoresis, microconvection, and enhanced thermal conductivity are most consistently observed in HVAC&R nanofluid applications and form the basis for interpreting experimental and numerical results in this review.

This section provides a comprehensive thematic synthesis of 46 selected studies focusing on heat transfer enhancement using nanofluids. The analysis is organized into five subsections, each representing a dominant methodological perspective or performance trend:

• Section 2.2.1 Experimental Studies examine lab-scale evaluations of nanofluid heat transfer in compact exchangers, solar systems, and augmented flow channels.
• Section 2.2.2 Numerical Investigations delve into Computational Fluid Dynamics (CFD)-based modeling and simulation of nanofluid behavior under varied boundary conditions and channel geometries.
• Section 2.2.3 AI/ML-Driven Approaches highlight recent advances in artificial intelligence for thermal prediction, surrogate modeling, and optimization.
• Section 2.2.4 Critical Analysis and Future Directions discusses limitations, research gaps, and emerging opportunities.

By integrating these perspectives, this section aims to clarify the current landscape of nanofluid heat transfer enhancement and provide direction for next-generation thermal technologies in HVAC&R systems.

2.2.1. Experimental Studies

Experimental investigations form the cornerstone for validating the potential of nanomaterials in enhancing heat transfer performance within HVAC and refrigeration systems. Such studies not only quantify gains in convective or boiling heat transfer but also evaluate critical system-level metrics like COP improvements, pressure drop penalties, and optimal nanoparticle dosages. The following discussion synthesizes insights from selected experimental works, each focusing on different nanomaterial classes—including Al-based, Al₂O₃, TiO₂, ZnO, Ag, and hybrid formulations—applied across diverse thermal systems, ranging from flow boiling loops to passive building composites and phase change storage. Figure 4 furnishes the distribution of experimental studies by nanoparticle type used in nanofluid-based heat transfer enhancement. The figure highlights the prominence of TiO₂ and CuO in laboratory investigations, reflecting their thermal stability, availability, and cost-effectiveness. Less commonly studied nanoparticles such as Cu-based and Single-Wall Carbon Nanotubes (SWCNTs) are emerging but remain underrepresented in experimental validations.

Al-based nanostructures have garnered significant attention for improving fluid-side heat transfer. For instance, Zhang et al. (2025) [33] examined flow boiling of R134a in EDM-textured aluminum tubes enhanced by Al-based nanoparticles, reporting substantial increases in the Nusselt number and heat transfer coefficient, with indirect suggestions of improved COP. Similarly, a separate investigation on R134a nanofluids inside microfin tubes demonstrated impressive local enhancements, with Nusselt numbers improving by up to 210% and heat transfer coefficients increasing by approximately 120%, while also noting modest pressure drop penalties, often below 13%, thus maintaining hydraulic feasibility.

Incorporating Al₂O₃ nanoparticles, El Hadi Attia et al. (2025) [34] evaluated honeycomb copper tube arrangements using hybrid nanofluids. Their experiments showed a remarkable range of performance gains—up to 92.9% improvement in the Nusselt number, alongside secondary metrics like evaporator surface absorption and convective effectiveness. This underscores Al₂O₃’s continued prominence due to its thermal conductivity stability and chemical inertness in common refrigerants or aqueous systems.

Moving to hybrid configurations, recent work by researchers investigating triply periodic mini-channel structures combined Al-based and Ag nanoparticles, reporting multi-faceted improvements: Nusselt number gains of up to 30%, localized pressure drop increments around 4%, and overall enhancement efficiencies balancing between 12 and 24% across different flow conditions. These studies demonstrate that carefully engineered multi-material nanofluids can exploit synergistic mechanisms—enhanced microconvection from one species and superior thermal conductivity from another—yielding compound benefits.

Silver (Ag) nanoparticles also emerged as a potent agent in phase change thermal management. Abdullah et al. (2024) [35] explored Ag-enhanced PCMs in half-cylindrical solar still configurations, achieving substantial performance jumps, with reported improvements ranging from 55% up to 184% in metrics tied to thermal storage, evaporation rates, and integrated solar efficiency. Such outcomes highlight Ag’s unmatched role in accelerating transient heat storage–discharge cycles, pivotal for hybrid HVAC–solar systems.

On the TiO₂ and ZnO front, Jadhav et al. (2024) [36] introduced nano-composite polycarbonate sheets doped with these oxides (and zeolite) for greenhouse-style climate control. They found that doping concentrations around 0.3% led to cabinet temperature reductions up to 21.5%, with TiO₂ exhibiting superior thermal insulation and UV absorption due to its high refractive index. While ZnO offered slightly lower thermal blocking, it still significantly improved optical shielding compared to undoped polycarbonate.

Further extending TiO₂ applications, Liu et al. (2024) [37] designed wood–plastic composites embedded with carbonyl iron microcapsules and TiO₂, achieving thermal management gains up to 89.2% across combined heat storage and regulated release tests. This underscores how nanomaterials can be embedded in passive construction components to indirectly reduce HVAC loads by flattening thermal swings.

Through key quantitative themes from these diverse experimental efforts, several consistent trends emerge:

• Nusselt number enhancements routinely exceeded 50%, with some specialized microchannel or hybrid geometries pushing improvements beyond 200%.
• COP increases ranged between 5 and 10%, linked to lower compressor work or elevated evaporator effectiveness when using nanofluids.
• Optimal nanoparticle concentrations typically clustered around 0.3–0.4% by weight or volume, balancing enhanced thermal conductivity with manageable viscosity and pressure drop effects.
• In passive systems, cabinet or internal temperature drops of 15–22% were commonly achieved using TiO₂ or ZnO-doped materials.

To further elucidate methodological patterns and material-specific insights, the identified experimental studies were grouped based on their primary nanoparticle type. This classification underscores the diverse thermal enhancement strategies explored, ranging from metal oxides like TiO₂ and Al₂O₃ to metallic nanofluids such as Cu-based suspensions. Table 3 provides a concise summary, cataloging the studies under each nanoparticle category, along with key bibliographic identifiers.

Table 3. Summary of experimental studies on heat transfer enhancement in HVAC&R applications, grouped by nanoparticle type.

Ref.	Nanoparticle Type	Findings
[36,37,38,39]	TiO2	↓ Cabinet temperature up to 21.5% at 125 cm, esp. at 40 °C Optimal at 0.3% doping Provided highest thermal insulation and UV absorption
[34,38,40,41,42,43,44,45]	Al2O3	• ↑ Heat transfer coefficient by 80.5% at 0.4% vol. • ↑ COP by 7.5%, ↓ Power consumption by 5.2% • ↑ Pressure drop by 13.6%, max 25.42 kPa/m
[33,46,47]	Al-based (Hybrid)	• ↑ Nusselt number by 210%, heat transfer coefficient ↑ 120% • Mild pressure drop increase (~13%) • Noted optimal performance around 0.4% concentration
[48]	Cu/CuO	• PEC reached 1.18 (↑ ~18% over baseline) • Most effective at 0.4 wt.% Cu–water nanofluid • ↑ Heat transfer coefficient and pressure drop across flow rates
[36]	ZnO	• Effective UV blocking, better than plain PC • ↓ Refractive index and thermal insulation than TiO2 • Optimal at 0.3% doping for moderate ↓ temperature
[36,42,47]	Ag	• ↑ PCM storage performance by 55–184% across heat storage and evaporation metrics • Accelerated charge/discharge in solar still hybrid systems
[44]	SWCNT	• ↑ Nusselt number by ~25–35% in nanofluid flows • Slight ↑ viscosity, minimal ΔP rise • Stability achieved with surfactant-assisted dispersion
[49]	LCMO manganite	• ↑ Thermal conductivity and heat transfer by ~15–22% • Optimal at 0.1–0.2% volume fraction • Moderate pressure drop, good long-term dispersion stability

Table 3. Summary of experimental studies on heat transfer enhancement in HVAC&R applications, grouped by nanoparticle type.

Ref.	Nanoparticle Type	Findings
[36,37,38,39]	TiO2	↓ Cabinet temperature up to 21.5% at 125 cm, esp. at 40 °C Optimal at 0.3% doping Provided highest thermal insulation and UV absorption
[34,38,40,41,42,43,44,45]	Al2O3	• ↑ Heat transfer coefficient by 80.5% at 0.4% vol. • ↑ COP by 7.5%, ↓ Power consumption by 5.2% • ↑ Pressure drop by 13.6%, max 25.42 kPa/m
[33,46,47]	Al-based (Hybrid)	• ↑ Nusselt number by 210%, heat transfer coefficient ↑ 120% • Mild pressure drop increase (~13%) • Noted optimal performance around 0.4% concentration
[48]	Cu/CuO	• PEC reached 1.18 (↑ ~18% over baseline) • Most effective at 0.4 wt.% Cu–water nanofluid • ↑ Heat transfer coefficient and pressure drop across flow rates
[36]	ZnO	• Effective UV blocking, better than plain PC • ↓ Refractive index and thermal insulation than TiO2 • Optimal at 0.3% doping for moderate ↓ temperature
[36,42,47]	Ag	• ↑ PCM storage performance by 55–184% across heat storage and evaporation metrics • Accelerated charge/discharge in solar still hybrid systems
[44]	SWCNT	• ↑ Nusselt number by ~25–35% in nanofluid flows • Slight ↑ viscosity, minimal ΔP rise • Stability achieved with surfactant-assisted dispersion
[49]	LCMO manganite	• ↑ Thermal conductivity and heat transfer by ~15–22% • Optimal at 0.1–0.2% volume fraction • Moderate pressure drop, good long-term dispersion stability

Altogether, these experimental studies provide robust validation of the technical feasibility and scalability of nanomaterial interventions in HVAC and refrigeration. Whether through direct enhancement of fluid-side heat transfer, passive moderation of indoor climates, or integration into phase change storage, the consistent quantitative outcomes—across Nusselt, COP, and temperature metrics—solidify the role of nanoparticles as a key lever for next-generation energy-efficient thermal systems.

2.2.2. Numerical Studies

In parallel with experimental advancements, numerical studies have emerged as a cornerstone for understanding and predicting the complex thermal and flow behaviors associated with nanofluid-enhanced HVAC and refrigeration systems. Computational approaches, primarily employing CFD simulations, finite element methods, or hybrid analytical–numerical frameworks, allow researchers to investigate parameter sensitivities, optimize system configurations, and predict multiscale phenomena that are often impractical or cost prohibitive to test experimentally. Particularly in the domain of nanofluids, numerical models enable a rigorous evaluation of heat transfer enhancements, entropy generation, pressure drop penalties, and magnetic or electric field effects under diverse operating regimes. These insights are invaluable for accelerating the design and deployment of energy-efficient thermal systems. A closer look at recent studies reveals the breadth of computational methodologies and the diversity of nanomaterials investigated.

Khan et al. (2025) [50] conducted a comprehensive CFD study on TiO₂–Au hybrid nanofluids flowing through microchannels under varying Reynolds numbers. Their simulations highlighted an enhancement in Nusselt number of approximately 26–38% compared to pure water, while also noting a moderate increase in friction factor. This underscores the synergy of metal–oxide combinations in augmenting convective heat transfer. Meng et al. (2025) [51] numerically explored thermoelectric cooling systems incorporating SiO₂–Al₂O₃ nanoparticles dispersed in water. Their model revealed that these hybrid suspensions could reduce localized temperatures by up to 12 °C, thereby extending the cooling range and potential application of TEC modules in low-temperature HVAC environments. Raju et al. (2024) [52] investigated GO–Au–Ag hybrid nanofluids in a wedge-shaped duct configuration using finite volume methods. Their results demonstrated a 32% rise in the average heat transfer coefficient, highlighting the pivotal role of graphene oxide in enhancing thermal pathways when combined with noble metal particles.

Balogun et al. (2025) [53] numerically examined gold and silver hybrid nanofluids under diverse parametric variations, showing optimal performance at nanoparticle concentrations near 0.08 vol%, with noted trade-offs between enhanced heat transfer and increased shear stresses. Bompally et al. (2025) [54] modeled EG + MoS₂ + SiO₂ hybrid nanofluids exposed to inclined magnetic fields, capturing the interplay between magnetic intensities and nanoparticle dispersion. Their simulations revealed up to 28% improvements in heat transfer, accompanied by reductions in entropy generation at optimized field angles. Liang et al. (2025) [55] presented a multi-objective optimization study on thermal interface materials with embedded MWCNTs, focusing on cooling modules for active antenna units. They highlighted how incorporating MWCNT nanostructures improved thermal conductivity and reduced hotspots, offering potential extensions to compact HVAC modules.

Kumar et al. (2025) [56] conducted numerical assessments of CNT-based nanofluids for microelectronic thermal management, observing improved local Nusselt profiles and effective temperature suppression across intricate microchannel configurations. Sreelakshmi et al. (2024) [57] analyzed hybrid Cu-based nanofluids in thin liquid films, noting marked enhancements in transient heat transfer behavior due to the inclusion of copper particles, which intensified conduction at the liquid–solid interface. Benkherbache et al. (2024) [58] performed a detailed natural convection study of hybrid multi-nanoparticle dispersions, demonstrating that incorporating Fe₃O₄, Al₂O₃, and Cu nanoparticles led to substantial increases in heat transfer rates while carefully balancing buoyancy-driven flow characteristics. Farooq et al. (2025) [59] investigated a suite of hybrid nanofluid combinations (e.g., TiO₂ + SiO₂, TiO₂ + MoS₂, TiO₂ + Au) using CFD frameworks, mapping how each hybrid formulation influenced local entropy generation and overall thermal efficiency under solar-assisted flow conditions.

Elboughdiri et al. (2025) [60] examined Cu, TiO₂, and Fe₃O₄ hybrid nanofluid mixtures under variable Stefan blowing and cross-diffusion effects (Soret and Dufour). Their results showcased complex coupled mass-heat transfer enhancements beneficial for evaporative cooling processes. Shinwari et al. (2025) [61] developed a robust computational framework for Al₂O₃ + TiO₂ + SiO₂ trihybrid nanofluid flows, incorporating chemical reaction and thermal activation models. They reported notable gains in heat transfer performance coupled with reduced activation energy requirements.

To visually consolidate the diverse hybrid nanoparticle systems investigated across these numerical studies, a schematic representation is presented in Figure 5. This graphical summary clusters the multi-component nanofluids based on their predominant constituents, effectively illustrating the recurrent combinations employed to enhance heat transfer performance. By mapping out these hybrid formulations alongside their corresponding study counts, the figure offers an intuitive overview of the research landscape, underscoring the strategic pairing of metal oxides, carbides, and noble metals to exploit synergistic thermal transport mechanisms. Such visualization not only highlights the methodological breadth of current investigations but also points toward emerging preferences in hybrid nanoparticle engineering within HVAC and refrigeration contexts.

To further consolidate the diverse numerical investigations into nanoparticle-enhanced heat transfer reviewed in Section 2.2.2, Table 4 summarizes key findings from representative studies. This synthesis highlights the range of nanofluids examined—from simple oxide suspensions to complex multi-metal and carbon-based hybrids—along with the primary enhancements reported in convective heat transfer, entropy reduction, and associated thermophysical behaviors. By considering different nanoparticle configurations and flow phenomena, the table offers a succinct comparative perspective on how numerical methodologies have elucidated the underlying mechanisms and performance trade-offs inherent in these advanced thermal systems.

Table 4. Summary of selected numerical studies on the impact of single and hybrid nanoparticles on heat transfer enhancement.

Ref.	Nanoparticle/Hybrid	Key Findings
[56]	CNT	• Improved microchannel Nusselt and local temperatures
[57]	Al2O3 + Cu	• RKF simulation: hybrid more stable vs. mono; HT ↑ with Pr, Eckert; magnetic field raises skin friction
[58]	Fe3O4 + Al2O3 + Cu/Ag	• Annular cavity: ↑ Ha ↓ temp ↑ velocity ↑ Nusselt; Cu most responsive; fins moderate convection
[59]	TiO2 + SiO2, TiO2 + MoS2, TiO2 + Au	• Solar-assisted flows: ↓ entropy, ↑ efficiency under hybrid blends
[60]	Cu, TiO2, Fe3O4	• Stefan blowing and Soret–Dufour: THNF ↑ HT by 22%, hybrid by 17%, mono by 11%; Yamada–Ota > Hamilton–Crosser
[61]	Al2O3 + TiO2 + SiO2	• Radial magnetic: slip and curvature ↑ temp; activation energy ↑ conc ↓ mass transfer
[62]	Fe3O4 + CoFe2O4	• Magnetic hybrid flows analyzed for entropy and heat transfer
[63]	Ag + Cu + TiO2	• Simulated multi-metal hybrids under laminar regimes
[64]	Al2O3	• Magnetic refrigeration: up to 15% ↑ cooling; small pumping penalty, COP marginal
[65]	ZnO	• Subcooled boiling: ↑ vapor gen, wall Nu with ϕ < 1%; bubble dynamics crucial
[66]	CuO (LiBr)	• FEM: CuO ↑ mass flux, rate, coefficient in LiBr absorption
[67,68]	Cu/CuO	• Barocaloric refrigerator: 10% Cu nanofluid ↑ HT by ~30% • Laminar mixed convection: ↑ ϕ ↑ Nu, rib AR and Ri interplay key for buoyancy vs. forced
[69]	Diamond + Cu	• ANFIS + PSO predicted pool boiling HT of refrigerant–oil with nanoparticles
[55]	MWCNT	• In interface cooling modules ↓ hotspots, ↑ thermal uniformity
[54]	EG + MoS2 + SiO2	• Inclined magnetic ↑ HT by ~28%, ↓ entropy
[52]	GO + Au + Ag	• Wedge boundary layer ↑ HT
[53]	Hybrid (Au–Ag)	• Optimal ~0.08 vol% ↑ HT by ~28–41%, balanced vs. shear
[51]	SiO2, Al2O3	• In TEC, hybrid ratios ↓ local temps, extended cooling
[50]	TiO2 + Au	• Magnetized non-Newtonian flow ↑ Nusselt, sensitive to field and ϕ

Table 4. Summary of selected numerical studies on the impact of single and hybrid nanoparticles on heat transfer enhancement.

Ref.	Nanoparticle/Hybrid	Key Findings
[56]	CNT	• Improved microchannel Nusselt and local temperatures
[57]	Al2O3 + Cu	• RKF simulation: hybrid more stable vs. mono; HT ↑ with Pr, Eckert; magnetic field raises skin friction
[58]	Fe3O4 + Al2O3 + Cu/Ag	• Annular cavity: ↑ Ha ↓ temp ↑ velocity ↑ Nusselt; Cu most responsive; fins moderate convection
[59]	TiO2 + SiO2, TiO2 + MoS2, TiO2 + Au	• Solar-assisted flows: ↓ entropy, ↑ efficiency under hybrid blends
[60]	Cu, TiO2, Fe3O4	• Stefan blowing and Soret–Dufour: THNF ↑ HT by 22%, hybrid by 17%, mono by 11%; Yamada–Ota > Hamilton–Crosser
[61]	Al2O3 + TiO2 + SiO2	• Radial magnetic: slip and curvature ↑ temp; activation energy ↑ conc ↓ mass transfer
[62]	Fe3O4 + CoFe2O4	• Magnetic hybrid flows analyzed for entropy and heat transfer
[63]	Ag + Cu + TiO2	• Simulated multi-metal hybrids under laminar regimes
[64]	Al2O3	• Magnetic refrigeration: up to 15% ↑ cooling; small pumping penalty, COP marginal
[65]	ZnO	• Subcooled boiling: ↑ vapor gen, wall Nu with ϕ < 1%; bubble dynamics crucial
[66]	CuO (LiBr)	• FEM: CuO ↑ mass flux, rate, coefficient in LiBr absorption
[67,68]	Cu/CuO	• Barocaloric refrigerator: 10% Cu nanofluid ↑ HT by ~30% • Laminar mixed convection: ↑ ϕ ↑ Nu, rib AR and Ri interplay key for buoyancy vs. forced
[69]	Diamond + Cu	• ANFIS + PSO predicted pool boiling HT of refrigerant–oil with nanoparticles
[55]	MWCNT	• In interface cooling modules ↓ hotspots, ↑ thermal uniformity
[54]	EG + MoS2 + SiO2	• Inclined magnetic ↑ HT by ~28%, ↓ entropy
[52]	GO + Au + Ag	• Wedge boundary layer ↑ HT
[53]	Hybrid (Au–Ag)	• Optimal ~0.08 vol% ↑ HT by ~28–41%, balanced vs. shear
[51]	SiO2, Al2O3	• In TEC, hybrid ratios ↓ local temps, extended cooling
[50]	TiO2 + Au	• Magnetized non-Newtonian flow ↑ Nusselt, sensitive to field and ϕ

2.2.3. AI/ML-Driven Approaches

The adoption of artificial intelligence (AI) and machine learning (ML) methodologies in the study of nanofluid-enhanced heat transfer has expanded significantly, driven by the need to model and optimize highly nonlinear, multi-parameter thermal systems. These approaches offer robust predictive frameworks that surpass traditional analytical and purely numerical methods, especially in handling the complex interplay of nanoparticle concentration, flow dynamics, thermal conductivity, and entropy generation. This is critically important in HVAC&R applications, where balancing heat transfer enhancement with pressure drop and entropy minimization remains a central engineering challenge.

Among single-component systems, Zolghadri et al. (2023) [70] investigated the application of artificial neural networks (ANNs) with Levenberg–Marquardt (LM) optimization to predict thermal parameters of Al₂O₃ nanofluids. Their study reported high correlation coefficients and very low prediction errors, demonstrating the capability of ANN-LM to effectively capture thermal behavior under varying operational conditions. Furthermore, by employing genetic algorithms (GAs), they identified optimal nanoparticle volume fractions that maximized thermal effectiveness while mitigating additional pumping power demands.

In hybrid systems, Kanti et al. (2025) [71] focused on Al₂O₃ + TiO₂ nanofluids, utilizing Gaussian Process Regression (GPR) with a 5/2 Matern kernel to construct surrogate models that predicted Nusselt numbers and entropy generation rates in microchannel flows. Their GPR approach significantly reduced computational time compared to full CFD simulations while maintaining prediction errors under 3%, illustrating its utility for rapid design iterations.

For more intricate multi-component systems, Ullah et al. (2025) [72] examined nanofluids composed of Al₂O₃, CuO, Al, and Cu nanoparticles. They applied recurrent neural networks (RNNs) enhanced with long short-term memory (LSTM) architectures to capture the temporal evolution of convective heat transfer and entropy generation. Their models effectively identified operating regimes that balanced improved heat transfer with acceptable increases in frictional and entropic losses, providing a nuanced tool for thermal system optimization.

Expanding to hybrid nanoparticle systems that incorporate carbon nanostructures, Almohamadi et al. (2024) [73] analyzed Al₂O₃, SWCNT, and Au nanofluids using quantum-like machine learning (QLM) integrated with bidirectional recurrent neural networks (BRNNs). This advanced hybrid approach successfully predicted enhancements in effective thermal conductivity and convective heat transfer coefficients, demonstrating the synergistic benefits of multi-nanoparticle systems, which are often poorly captured by classical regression or single-output models.

Finally, a particularly rigorous investigation by Qureshi et al. (2024) [74] focused on a ternary nanofluid system comprising Al₂O₃, CuO, and Cu, flowing over a stretchable wavy cylinder. By integrating a Local Mean Field Approximation Neural Network (LMFA NN) within their hybrid computational framework, they achieved exceptionally low mean square errors—on the order of 10⁻¹⁰ to 10⁻¹³—across training, testing, and validation datasets. Their study showcased how ML frameworks can precisely capture complex velocity, temperature, and nanoparticle concentration fields, outperforming conventional numerical solvers, particularly for geometrically intricate domains.

Collectively, these diverse investigations underscore the pivotal role of AI/ML techniques in advancing the understanding and optimization of nanofluid-based heat transfer systems. By leveraging data-driven algorithms tailored to capture multi-parameter dependencies, researchers have demonstrated substantial improvements in predictive accuracy, design optimization speed, and the identification of operational envelopes that maximize thermal performance with controlled hydrodynamic penalties. As the field continues to mature, the integration of AI/ML models with experimental validation and high-fidelity simulations is poised to drive the next generation of energy-efficient HVAC&R systems.

To further elucidate the integration of artificial intelligence and machine learning (AI/ML) techniques with nanofluid-based heat transfer applications, a concept map has been developed, as shown in Figure 6. This graphical representation systematically links the various AI/ML models deployed in the reviewed studies to the types of nanoparticles investigated, thereby highlighting both the methodological diversity and the material scope of recent research. By visually mapping these associations, readers can quickly discern predominant algorithmic strategies, frequently studied nanofluid systems, and overlapping areas where multiple models have been employed to predict or optimize thermal performance. This holistic view facilitates a clearer understanding of how advanced data-driven approaches are progressively shaping the field of nanofluid-enhanced HVAC&R systems.

To provide a consolidated overview of the methodological advancements and key insights discussed in this subsection, Table 5 summarizes the specific types of nanoparticles investigated, the AI/ML algorithms employed, and the principal findings reported across the reviewed studies. This tabular synthesis facilitates a rapid comparative assessment of recent contributions, highlighting both the breadth of data-driven techniques applied and the diverse nanofluid systems explored within the context of heat transfer enhancement for HVAC&R applications.

Table 5. Summary of recent studies applying AI/ML techniques to nanofluid-based heat transfer enhancement.

Ref.	Nanoparticle Type	AI/ML Algorithms	Key Findings
[70,75,76]	Al2O3	ANN, LM, GA	• ANN models predicted nanofluid heat exchanger parameters accurately. • GA optimized volume fraction for max thermal effectiveness.
[71,77]	Al2O3 + TiO2	GPR	• GPR predicted Nusselt number and entropy rates in microchannels. • Achieved surrogate models reducing CFD computation time.
[72,78]	Al2O3, CuO, Al, Cu	RNN + LSTM	• LSTM captured time-dependent heat transfer dynamics. • Predicted optimal conditions balancing heat transfer and pressure drop.
[73,79]	Al2O3, SWCNT, SWCNT, Au, ZnO	CFNN, ET, QLM, BRNN	• Hybrid AI approaches forecasted thermal conductivity enhancements. • Showed multi-nanoparticle blends raised convective HT coefficients.

Table 5. Summary of recent studies applying AI/ML techniques to nanofluid-based heat transfer enhancement.

Ref.	Nanoparticle Type	AI/ML Algorithms	Key Findings
[70,75,76]	Al2O3	ANN, LM, GA	• ANN models predicted nanofluid heat exchanger parameters accurately. • GA optimized volume fraction for max thermal effectiveness.
[71,77]	Al2O3 + TiO2	GPR	• GPR predicted Nusselt number and entropy rates in microchannels. • Achieved surrogate models reducing CFD computation time.
[72,78]	Al2O3, CuO, Al, Cu	RNN + LSTM	• LSTM captured time-dependent heat transfer dynamics. • Predicted optimal conditions balancing heat transfer and pressure drop.
[73,79]	Al2O3, SWCNT, SWCNT, Au, ZnO	CFNN, ET, QLM, BRNN	• Hybrid AI approaches forecasted thermal conductivity enhancements. • Showed multi-nanoparticle blends raised convective HT coefficients.

To complement the thematic synthesis presented in this review, a high-level decision-flow schematic (Figure 7) has been developed to guide readers in the practical selection of nanofluids and associated system design parameters for HVAC&R applications. The flow begins with identifying the application type (e.g., chiller, heat pump, secondary loop), followed by selecting a suitable nanoparticle–base fluid combination based on thermal conductivity, stability, and compatibility criteria. Subsequent decision points address the optimum particle volume fraction, recommended flow velocities for enhanced heat transfer without excessive pressure drop, and suitable channel sizes/geometries to balance performance with manufacturability. This schematic is distilled from the collective insights of experimental, numerical, and AI-driven studies reviewed in Section 2.1, Section 2.2 and Section 2.3 and is intended as a practical visual guide rather than a rigid design formula.

2.2.4. Critical Analysis and Future Directions

A comprehensive review of experimental, numerical, and AI/ML-driven investigations into nanofluid-based heat transfer enhancement reveals both significant promise and persistent challenges that shape the future research landscape. While results across studies consistently demonstrate meaningful gains in thermal performance—frequently exceeding 30–50% in Nusselt number or local heat transfer coefficients—such improvements often come with nuanced trade-offs that necessitate deeper scrutiny.

From an experimental standpoint (Section 2.2.1), investigations on Al₂O₃, TiO₂, ZnO, and Ag nanofluids, along with emerging SWCNT and hybrid systems, have validated the capacity of nanoparticles to boost convective and boiling heat transfer in compact exchangers and passive thermal control structures. However, these studies also underscore recurring issues: viscosity escalation leading to higher pressure drops; challenges in sustaining long-term nanoparticle dispersion; and practical limits on concentration (~0.3–0.4 wt.%), beyond which benefits taper or even reverse due to excessive ΔP or sedimentation risks. Passive building materials doped with TiO₂ or ZnO have shown encouraging internal temperature reductions (15–22%), but such applications are still largely prototype-scale, with questions remaining about durability under cyclic thermal loads.

Numerical analyses (Section 2.2.2) have played a pivotal role in dissecting parametric influences, offering detailed mappings of how hybrid nanoparticle blends—like GO + Au + Ag or Fe₃O₄ + Al₂O₃ + Cu—impact entropy generation, frictional losses, and heat transfer rates under controlled simulations. Yet, many models rely on steady-state assumptions, ideal dispersion, or simplified thermophysical correlations. As a result, real-world complexities such as particle clustering, transient fouling, or multi-phase slip effects are often underrepresented. Even sophisticated multi-objective frameworks that balance entropy minimization with thermal gains rarely incorporate lifecycle cost or maintenance aspects, limiting their immediate translatability to HVAC&R scale systems.

The recent influx of AI/ML-driven studies (Section 2.2.3) signals a transformative methodological shift. Surrogate models like GPR, advanced neural architectures (RNN + LSTM, BRNN), and hybrid algorithms (QLM + CFNN) have achieved high-fidelity predictions with mean errors typically below 3%, dramatically reducing the computational burden compared to exhaustive CFD. These models excel at capturing multi-parametric nonlinearities, as evident in works examining Al₂O₃, TiO₂, SWCNT, and multi-metal hybrids. However, their effectiveness remains fundamentally tied to the depth and quality of training data. Most models are validated on narrowly defined datasets under controlled lab or simulation conditions, raising concerns about generalizability under fluctuating load scenarios, contaminant exposure, or long-duration HVAC operation.

Looking forward, several critical pathways can enhance the robustness and applicability of nanofluid technologies in HVAC&R:

• Integrated multi-objective frameworks: Future studies should unify AI/ML optimization with CFD or experimental data streams to jointly minimize entropy, maintain low pressure drops, and maximize Nusselt gains—bridging energy efficiency with operational reliability.
• Stability and fouling resilience: There is a pressing need for dynamic investigations tracking nanoparticle agglomeration, surface fouling, and re-dispersion under cyclic on–off operation typical in HVAC systems. Coupled magnetic or ultrasonic field techniques may offer novel avenues for in situ stability management.
• Next-generation nanomaterials: Graphene derivatives, MXenes, or bio-inspired hybrid composites hold promise for achieving high thermal conductivities at lower concentrations, potentially decoupling the viscosity trade-off that has constrained many oxide-based systems.
• Lifecycle and techno-economic perspectives: Very few studies holistically assess long-term cost-effectiveness, environmental footprint, or regulatory considerations tied to nanoparticle deployment and disposal. These analyses are vital to guide commercial translation.
• Integration into digital twins and adaptive controls: As smart HVAC infrastructures proliferate, embedding AI-driven nanofluid models into real-time digital twin systems could enable predictive adjustments to flow rates, nanoparticle dosing, or auxiliary magnetic fields—dynamically optimizing thermal performance and prolonging system lifespan.

Collectively, while nanofluids remain a highly promising strategy for elevating heat transfer capabilities in HVAC&R, unlocking their full potential demands a concerted push toward more integrative, multidisciplinary investigations. By coupling rigorous experimental validation, high-fidelity simulations, advanced machine learning, and lifecycle assessments, next-generation systems can move closer to practical, scalable deployments that deliver robust energy savings with engineered thermal precision.

2.3. Research Methodologies and Analytical Trends in Nanofluid-Enhanced HVAC&R Systems

Integrating nanofluids into HVAC&R and thermal management systems has prompted the adoption of diverse research methodologies over the past decade. This section synthesizes the methodological landscape of nanofluid research from 2015 to 2025, based on a thematic categorization of 134 peer-reviewed studies, as visualized in Figure 7.

A detailed alluvial diagram, Figure 7 maps the relationship between research methods—including experimental, numerical, theoretical, hybrid, and AI/ML-based approaches—and the targeted application domains such as refrigeration systems and general HVAC&R applications. The aim is to reveal not only the methodological diversity but also the alignment (or mismatch) between methods and specific application areas.

From this mapping, several dominant trends emerge:

• Experimental studies (n = 69) overwhelmingly dominate the literature and are most commonly applied to refrigeration systems (n = 105) and HVAC systems (n = 23). These studies often focus on evaluating nanofluid-enhanced heat exchangers, condensers, and evaporators under lab or bench-scale conditions.
• Numerical (n = 14) and theoretical investigations (n = 11) are primarily used to model heat transfer, fluid flow, and entropy generation using simulation tools or closed-form analytical methods. These studies complement experimental findings and explore parameter sensitivity under controlled assumptions.
• Hybrid methodologies (n = 5), although less common, serve a vital role in validating numerical models against experimental data. These studies demonstrate the highest methodological robustness but remain underutilized.
• AI/ML-driven research (n = 20) is an emerging area, primarily focused on predictive modeling and performance optimization. These studies show increasing potential for integration into real-time HVAC&R system control and design optimization.
• Review papers (n = 14) synthesize findings across various applications and methodologies, often identifying knowledge gaps or proposing research directions.

To maintain focus and clarity, certain categories were excluded from the detailed thematic analysis due to their limited representation in the literature:

• Heat Exchanger studies (n = 1);
• Thermal management applications (n = 2);
• Magnetic nanofluid studies (n = 1).

While these areas offer promising avenues for nanofluid application, the current volume of work is insufficient to support meaningful synthesis or trend evaluation. However, their presence indicates niche interest and underscores the need for expanded research in these subdomains.

To better visualize the distribution of research efforts, an alluvial diagram (Figure 8) maps the linkage between methodological approaches and their corresponding application domains in the nanofluid-based HVAC literature from 2015 to 2025. This representation highlights dominant pairings, such as experimental studies with refrigeration systems, and reveals underexplored intersections that represent potential research opportunities.

2.3.1. Experimental Studies

This section synthesizes the experimental investigations that explore the integration of nanofluids into HVAC&R systems. These studies employ laboratory-scale prototypes, component-level setups (e.g., chillers, condensers), and thermal loops to quantify improvements in heat transfer, energy efficiency, and system reliability. To structure the discussion, experimental studies have been grouped into three thematic categories:

• Experimental Fluid Characterization for System Integration,
• System-Level Performance Enhancement, and
• Comparative and Application-Specific Analysis.

Each theme captures a distinct experimental focus, ranging from nanofluid preparation and thermophysical profiling to performance validation and benchmarking under real or simulated operating conditions.

Figure 9 provides a visual distribution of experimental studies across these thematic categories and their corresponding application domains (HVAC vs. Refrigeration systems). Studies are grouped by (i) Experimental Fluid Characterization for System Integration, (ii) System-Level Performance Enhancement, and (iii) Comparative and Application-Specific Analysis. The diagram highlights that the majority of studies focus on refrigeration systems, particularly within system-level performance evaluations and nanofluid characterization. The figure reveals several key patterns:

• Experimental Fluid Characterization for System Integration accounts for 26 studies, again with a strong concentration in refrigeration systems (24 articles). These works primarily investigate nanofluid formulation, dispersion stability, and thermophysical properties (e.g., thermal conductivity, viscosity) in application-ready contexts, often under flow or heat load conditions.
• System-Level Performance Enhancement is the most extensively studied category, comprising 31 experimental studies, with the majority (25 articles) focusing on refrigeration systems. These studies commonly aim to validate gains in Coefficient of Performance (COP), energy reduction, or thermal efficiency when nanofluids are used in actual system components, such as evaporators, compressors, or heat exchangers.
• Comparative and Application-Specific Analysis includes 10 studies, split between HVAC&R systems. These studies test multiple nanofluids or operating parameters to evaluate performance differences under controlled comparative conditions.

The clear dominance of refrigeration systems across all three categories emphasizes their continued relevance and practical feasibility in nanofluid research. In contrast, HVAC&R systems, while still represented, have seen fewer experimental evaluations—highlighting a potential research gap and opportunity for future exploration.

This thematic organization ensures that each subgroup of studies can be analyzed in detail, with attention to methodological consistency, performance metrics, and application-specific trends. The following subsections will explore these categories in depth, identifying key findings, technical contributions, and unresolved challenges in each domain.

Experimental Fluid Characterization for System Integration

Experimental fluid characterization is a foundational stage in the deployment of nanofluids in HVAC&R systems. These studies primarily focus on understanding how nanofluid properties—such as thermal conductivity, viscosity, density, and stability—respond to nanoparticle type, concentration, base fluid selection, and flow conditions. A consistent observation across this category is the central role of nanolubricants and hybrid nanofluids in enhancing refrigerant performance, particularly in vapor compression cycles.

Silica- and oxide-based nanolubricants have emerged as promising candidates. Khan and ul Haque (2025) [80] performed a detailed thermophysical evaluation of silica-based nanolubricants in refrigeration systems. Their results demonstrated that the incorporation of 0.1 wt% silica nanoparticles in polyolester (POE) oil significantly improved thermal conductivity and reduced dynamic viscosity, offering better lubrication and energy transfer. This improvement was sustained across various temperature gradients and was accompanied by good dispersion stability, reinforcing its viability for system integration.

Similarly, Kumar et al. (2023) [81] proposed a hybrid nanofluid formulation involving TiO₂ and SiO₂ nanoparticles suspended in POE lubricant. Their study aimed at reducing the environmental footprint of conventional refrigerants. The experimental data showed that the hybrid nanofluid reduced total hydrofluorocarbon (HFC) emissions by improving heat transfer efficiency and reducing compressor workload, demonstrating environmental and performance benefits simultaneously.

Carbon nanotube-enhanced nanofluids also exhibit notable performance traits. Cassim, Hairuddin, and Cut (2024) [82] examined a multiwalled carbon nanotube (MWCNT)–CuO nanofluid blend in a test loop mimicking HVAC coil operation. They found that the hybrid nanofluid enhanced overall system energy absorption while maintaining thermal stability and dispersion. Importantly, their study utilized both empirical testing and visual sedimentation analysis to confirm fluid reliability over extended operation times.

Other investigations explored frictional losses and stability trade-offs. Dağıdır and Bilen (2023) [83] studied the use of Al₂O₃ and TiO₂ nanoparticles blended into POE lubricants in R134a refrigeration systems. They noted a complex interaction between nanoparticle loading and fluid pressure drop—where small concentrations improved thermal response, but higher doses led to increased flow resistance. These findings stress the importance of optimizing particle size and concentration to balance thermal gains against system pressure losses.

From a system optimization standpoint, comparative evaluations also fall into this group when focused on fluid behavior. Ahsan et.al (2025) [84] conducted an experimental comparison of nanolubricants in refrigeration systems using different refrigerant–nanofluid pairs. Though their primary objective was COP benchmarking, their work involved extensive viscosity and thermal conductivity profiling, underscoring the practical importance of fluid characterization in real operating scenarios.

In general, studies in this category confirm that the nanoparticle material, morphology, and concentration, along with surfactant use and mixing technique, play decisive roles in nanofluid performance. However, despite promising short-term gains, few studies explore long-term reliability, biofouling risks, or compatibility with legacy components, suggesting areas for future research.

To facilitate system-level integration, understanding the fluid performance characteristics under varying nanoparticle compositions and refrigerant pairings is crucial. Table 6 presents a consolidated summary of key experimental studies that explored a range of nanofluids—including metal oxides, carbon-based nanomaterials, and hybrid blends—dispersed in conventional compressor oils or refrigerants. The table delineates the specific performance parameters evaluated (e.g., COP, cooling capacity, power reduction), alongside the working fluids and nanoparticles involved. Notably, consistent improvements in COP (ranging from 2.4% to over 45%) and reductions in power consumption were observed, with hybrid and carbon-based nanomaterials often yielding the highest performance gains. These findings underscore the potential of nanofluid-enhanced systems in improving the energy efficiency and operational reliability of refrigeration cycles.

Table 6. Summary of nanoparticle-enhanced nanolubricants and nanorefrigerants used in experimental refrigeration systems, highlighting the evaluated performance parameters, refrigerants employed, and observed enhancements in system metrics such as COP, power consumption, and cooling capacity.

Ref.	Nanoparticle Involved	Evaluated Parameter	Refrigerant	Findings
[85,86,87,88]	Al2O3	COP	R134a, R600a	↑ COP (14 to 45%) Power Reduction (5–9%)
[80,84,88]	SiO2	COP	R600a	↑ COP (15–40%)
[80,81,84,88,89,90,91,92]	(25%) TiO2-(75%) SiO2, ZnO	COP, Compressor Power, Cooling Capacity	POE oil, R600a	↑ COP (14–51%) $\downarrow$Power (8–13%) ↑ Refrigerated Capacity (13%)
[93,94,95,96,97,98,99,100]	CuO, Ag, Cu	COP, Compressor Power, Refrigerating Capacity	R600a, R134a, R290	↑ COP (12.2–35%) $\downarrow$Power (1.39–24%) ↑ Refrigerated Capacity (13%) $\downarrow$Friction 9.9%
[82,83,101]	CNT, MWCNT, Graphene	COP, Compressor Power, Cooling Capacity	POE oil	↑ COP (12.2–35%) $\downarrow$Power (1.39–6.8%)
[102,103,104,105,106]	Ag, Diamond,	COP, Cooling Capacity	R600a, R134a, R410A, R32	↑ COP (2.4–39.5%) $\downarrow$Power (1.39–6.8%) ↑ Refrigeration Capacity (2.4%)

Table 6. Summary of nanoparticle-enhanced nanolubricants and nanorefrigerants used in experimental refrigeration systems, highlighting the evaluated performance parameters, refrigerants employed, and observed enhancements in system metrics such as COP, power consumption, and cooling capacity.

Ref.	Nanoparticle Involved	Evaluated Parameter	Refrigerant	Findings
[85,86,87,88]	Al2O3	COP	R134a, R600a	↑ COP (14 to 45%) Power Reduction (5–9%)
[80,84,88]	SiO2	COP	R600a	↑ COP (15–40%)
[80,81,84,88,89,90,91,92]	(25%) TiO2-(75%) SiO2, ZnO	COP, Compressor Power, Cooling Capacity	POE oil, R600a	↑ COP (14–51%) $\downarrow$Power (8–13%) ↑ Refrigerated Capacity (13%)
[93,94,95,96,97,98,99,100]	CuO, Ag, Cu	COP, Compressor Power, Refrigerating Capacity	R600a, R134a, R290	↑ COP (12.2–35%) $\downarrow$Power (1.39–24%) ↑ Refrigerated Capacity (13%) $\downarrow$Friction 9.9%
[82,83,101]	CNT, MWCNT, Graphene	COP, Compressor Power, Cooling Capacity	POE oil	↑ COP (12.2–35%) $\downarrow$Power (1.39–6.8%)
[102,103,104,105,106]	Ag, Diamond,	COP, Cooling Capacity	R600a, R134a, R410A, R32	↑ COP (2.4–39.5%) $\downarrow$Power (1.39–6.8%) ↑ Refrigeration Capacity (2.4%)

System-Level Performance Enhancement

While previous section emphasized fluid-level thermophysical characterization, such as thermal conductivity, viscosity, and nanoparticle concentration effects, the actual impact of nanofluids on system performance must be validated under integrated operating conditions. These system-level studies account for component interactions, dynamic load variations, and long-term operational stability—factors essential to real-world HVAC&R applications. Accordingly, this subsection focuses on experimental research that evaluates how nanofluid incorporation affects the overall energy performance, cooling capacity, compressor behavior, and environmental efficiency of complete systems.

Ikpe and Udofia (2024) [107] investigated the application of a hybrid nanofluid blend of Al₂O₃ and CuO in a domestic vapor compression refrigeration system. Their study highlighted significant improvements in thermal conductivity and heat absorption characteristics, which translated into measurable reductions in compressor work and improved coefficient of performance (COP). The hybrid formulation contributed to enhanced dispersion stability and synergistic thermal performance, outperforming single-particle nanofluids. These findings are particularly relevant for systems requiring robust part-load performance and extended duty cycles.

Jiang et al. (2023) [108] explored the role of TiO₂-based nanolubricants in an R134a-based refrigeration cycle. The experimental setup revealed tangible improvements in both cooling capacity and power consumption, owing to improved lubricant film strength and more effective heat transfer within the compressor. Lower compressor discharge temperatures were also noted, suggesting not only enhanced performance but also a likely extension of component life due to reduced mechanical stress.

In HVAC systems, Micali et al. (2025) [109] and Milanese et al. (2022) [110] provided valuable insights from full-scale implementations of nanofluid-enhanced air conditioning units. Micali et al. focused on optimizing CO₂ emissions and energy consumption in data center applications, showing that nanofluid-enhanced secondary loops can cut CO₂ emissions by over 20%, while improving thermal control and reducing compressor cycling. Milanese et al. emphasized the operational resilience of nanofluids, confirming improved load-following performance and control precision under variable ambient conditions. These studies underscore the broader applicability of nanofluids beyond component optimization, highlighting their role in achieving whole-system energy goals.

Raghavulu and Rasu (2025) [111] adopted a sustainability-driven approach, evaluating nanorefrigerants in environmentally friendly R600a systems. Their results demonstrated up to 35% COP enhancement and considerable energy savings. The use of nanoparticles in low Global Warming Potential (GWP) refrigerant environments offers a path forward for balancing energy performance with climate impact mitigation—an increasingly critical metric for future-ready HVAC&R technologies.

Taken together, these experimental investigations validate that nanofluids can play a transformative role in system-level optimization. The improvements are not merely confined to static test benches but extend to integrated operating conditions, influencing compressor behavior, refrigerant flow regulation, and thermal stability across various subsystems. These findings reinforce the need for future work to explore long-term reliability, optimal nanoparticle loading, and compatibility with newer refrigerants and lubricants. Ultimately, nanofluid adoption at the system scale offers a scalable and practical route to achieve next-generation energy efficiency and decarbonization targets in thermal systems.

To better understand the performance characteristics and comparative benefits of various nanofluids applied in HVAC systems, Table 7 synthesizes key experimental findings from the recent literature. Studies are organized according to the type of nanoparticle used, base fluid or refrigerant, and the evaluated parameters such as COP enhancement, temperature reduction, pressure drop, or energy efficiency. This grouping not only reveals the breadth of experimental conditions but also underscores the recurring trends and dominant nanofluid combinations explored for thermal performance improvements.

Table 7. Grouped summary of experimental studies on nanofluid-enhanced HVAC systems, categorized by nanoparticle type, base fluid or refrigerant, and performance parameters evaluated.

Ref.	Nanoparticle Type	Base Fluid/Refrigerant	Key Findings
[87,112,113,114,115]	Hybrid Al2O3/CuO/TiO2	Zeotropic refrigerant blend (23% R32/25% R125/52% R134a)	↑ COP 3.10% (at blend 011) ↑ Compressor power coefficient 13.51% ↑ Cooling capacity by 5.78% ↑ TEGWI 1.06 kg/s CO2 ↑ Optimal heat transfer at 0.0075 Vol.% nanoparticle concentration
[100,101,107,108,109,110,111,115,116,117,118,119,120]	Al2O3/TiO2	Chiller fluid (not explicitly stated, inferred to be water-based), Distilled Water, H2O–LiBr, POE (polyol ester) oil, R134a, Water–glycol mixture; Ammonia–water solution, NH3–H2O, NH3–H2O–LiBr, Water	↑ COP (9.1% to 14%) $\downarrow$Energy consumption about 32%, $\downarrow$50.9 tons/year CO2 emission; net LCA benefit of 36.6 tons/year Winter: Avg. ↑ 9.8% Summer: Avg. ↑ 8.9% Al2O3/R134a compared to pure refrigerant COP ↑ to 6.5 (about 15% improvement) ↑ COP 17.27% Absorption efficiency ↑ up to 85% Improved Thermal Performance
[88]	Graphene-Based	Polyester Oil (POE)	↑ COP by ~29%
[112,121,122,123]	Binary Nanofluids (e.g., Iron Oxide + LiBr-H2O)	LiBr-H2O; Water–ethylene glycol; H2O–LiBr	↑ Absorption rate 4.9–7.6% without magnetic field ↑ TC by up to 37.7% over baseline fluid, ↑ Max desorption rate by 7.9% (CNT); ↑ Max SCOP by 14%; ↑ cooling effect up to 6%; Optimal desorber modules: <50, cost cap: EUR 35/module
[124,125,126,127]	Noble/Carbon-Based (Ag, MWCNT)	Water; Various compressor oils	↑ COP with nanofluid absorbent ↑ Overall efficiency from 77.3% to 81% with Ag nanofluid Exergy efficiency of the system: 29% At 50 °C and 0.1% mass conc., ↑ viscosity by 40–90%, depending on base oil;
[128,129,130,131]	Hybrid Experimental (Al2O3, CuO, TiO2)	Deionized Water; Water	↑ Max COP: 24.2% (vs. water) Al2O3/H2O had 6.2% higher COP vs. hybrid TiO2 had 5.6% lower COP vs. hybrid $\downarrow$ Compression ratio up to 6.9%
[130,132]	Copper-Based (Cu, CuO)	SAE50 Oil; Water	$\downarrow$ Viscosity by up to 15% at low vol. fractions; ↑ by 14% at 1% vol.; ↑ COP by 23% (Al2O3) and 72% (Cu) at 5% volume fraction Higher TC of Cu yielded better performance than Al2O3
[133]	Mixed Metal Oxides (MgO, TiO2)	Water	↑ COP by 16.7% (MgO) and 11.5% (TiO2) at 1% wt. Max performance at PVC fill, 12 mm, 90° spray angle
[134,135,136,137]	TiO2 with Surfactants (SDBS)	NH3–H2O	↑ COP up to 27% overall ↑ 14–26.2% under challenging conditions (e.g., low evap temp, high cooling water temp); best performance with 0.5% TiO2 + 0.02% SDBS

Table 7. Grouped summary of experimental studies on nanofluid-enhanced HVAC systems, categorized by nanoparticle type, base fluid or refrigerant, and performance parameters evaluated.

Ref.	Nanoparticle Type	Base Fluid/Refrigerant	Key Findings
[87,112,113,114,115]	Hybrid Al2O3/CuO/TiO2	Zeotropic refrigerant blend (23% R32/25% R125/52% R134a)	↑ COP 3.10% (at blend 011) ↑ Compressor power coefficient 13.51% ↑ Cooling capacity by 5.78% ↑ TEGWI 1.06 kg/s CO2 ↑ Optimal heat transfer at 0.0075 Vol.% nanoparticle concentration
[100,101,107,108,109,110,111,115,116,117,118,119,120]	Al2O3/TiO2	Chiller fluid (not explicitly stated, inferred to be water-based), Distilled Water, H2O–LiBr, POE (polyol ester) oil, R134a, Water–glycol mixture; Ammonia–water solution, NH3–H2O, NH3–H2O–LiBr, Water	↑ COP (9.1% to 14%) $\downarrow$Energy consumption about 32%, $\downarrow$50.9 tons/year CO2 emission; net LCA benefit of 36.6 tons/year Winter: Avg. ↑ 9.8% Summer: Avg. ↑ 8.9% Al2O3/R134a compared to pure refrigerant COP ↑ to 6.5 (about 15% improvement) ↑ COP 17.27% Absorption efficiency ↑ up to 85% Improved Thermal Performance
[88]	Graphene-Based	Polyester Oil (POE)	↑ COP by ~29%
[112,121,122,123]	Binary Nanofluids (e.g., Iron Oxide + LiBr-H2O)	LiBr-H2O; Water–ethylene glycol; H2O–LiBr	↑ Absorption rate 4.9–7.6% without magnetic field ↑ TC by up to 37.7% over baseline fluid, ↑ Max desorption rate by 7.9% (CNT); ↑ Max SCOP by 14%; ↑ cooling effect up to 6%; Optimal desorber modules: <50, cost cap: EUR 35/module
[124,125,126,127]	Noble/Carbon-Based (Ag, MWCNT)	Water; Various compressor oils	↑ COP with nanofluid absorbent ↑ Overall efficiency from 77.3% to 81% with Ag nanofluid Exergy efficiency of the system: 29% At 50 °C and 0.1% mass conc., ↑ viscosity by 40–90%, depending on base oil;
[128,129,130,131]	Hybrid Experimental (Al2O3, CuO, TiO2)	Deionized Water; Water	↑ Max COP: 24.2% (vs. water) Al2O3/H2O had 6.2% higher COP vs. hybrid TiO2 had 5.6% lower COP vs. hybrid $\downarrow$ Compression ratio up to 6.9%
[130,132]	Copper-Based (Cu, CuO)	SAE50 Oil; Water	$\downarrow$ Viscosity by up to 15% at low vol. fractions; ↑ by 14% at 1% vol.; ↑ COP by 23% (Al2O3) and 72% (Cu) at 5% volume fraction Higher TC of Cu yielded better performance than Al2O3
[133]	Mixed Metal Oxides (MgO, TiO2)	Water	↑ COP by 16.7% (MgO) and 11.5% (TiO2) at 1% wt. Max performance at PVC fill, 12 mm, 90° spray angle
[134,135,136,137]	TiO2 with Surfactants (SDBS)	NH3–H2O	↑ COP up to 27% overall ↑ 14–26.2% under challenging conditions (e.g., low evap temp, high cooling water temp); best performance with 0.5% TiO2 + 0.02% SDBS

To consolidate the diverse experimental investigations discussed in this section, a summary table was developed by systematically grouping studies based on the nanoparticle types and their respective base fluids or refrigerants. The table captures essential parameters such as system configuration, performance metrics evaluated, and the key outcomes. This clustering not only highlights the predominance of Al₂O₃, TiO₂, and copper-based nanofluids across HVAC subsystems but also facilitates cross-comparison of their thermal enhancement trends. It becomes evident that while certain nanoparticles exhibit superior heat transfer or system efficiency, others offer advantages in terms of stability, economic feasibility, or compatibility with renewable integrations. The insights drawn here provide a foundational reference for selecting optimal nanofluid formulations in future HVAC&R applications. Building upon these consolidated findings, the next subsection delves deeper into comparative and application-specific analyses. By examining performance outcomes across similar HVAC&R configurations and operating conditions, we aim to uncover nuanced trends and evaluate the practical viability of each nanofluid formulation. This approach not only clarifies relative advantages but also aligns material choices with targeted HVAC&R applications and performance goals.

Comparative and Application-Specific Analysis

A nuanced understanding of nanofluid integration into HVAC&R systems requires comparative assessment across specific application domains and working conditions. The current body of experimental literature has shown that nanofluid-enhanced systems do not offer uniform improvements across all components and configurations; instead, performance gains are context dependent—shaped by factors such as nanoparticle type, concentration, dispersion medium (refrigerant or lubricant), and the targeted subsystem (e.g., compressor, condenser, evaporator).

In the realm of refrigeration systems, Yousif, Al-Obaidi, and Al-Muhsen (2024) [138] investigated the use of CuO nanoparticles dispersed in R134a refrigerant in a vapor–compression refrigeration cycle. Their experimental outcomes showed a consistent improvement in the coefficient of performance (COP), with a peak increase of approximately 19% when compared to the baseline. The authors noted reductions in compressor work and energy consumption, emphasizing that nanorefrigerants can substantially enhance system efficiency when nanoparticle stability and uniform dispersion are ensured.

Echoing similar findings, Shewale et al. (2024) [139] focused on the application of TiO₂ nanorefrigerants in R134a-based vapor compression systems. Their comparative analysis revealed that the introduction of nanofluids not only elevated the COP but also shortened the cooling time and reduced power consumption. They emphasized that these enhancements are attributable to increased thermal conductivity and improved refrigerant flow characteristics induced by the nanoparticles. However, their study also highlighted challenges regarding long-term dispersion stability and potential clogging effects, particularly at higher concentrations.

In contrast, Mohammed et al. (2024) [140] concentrated on HVAC systems, where Al₂O₃ nanoparticles were integrated into R600a refrigerants. Their work aimed to analyze both thermodynamic and exergetic performance across a range of nanoparticle concentrations. Results indicated a peak improvement of 24.56% in cooling capacity and COP at a 0.1% mass fraction. Notably, the exergy destruction in the compressor and evaporator components was significantly reduced, underscoring how nanoparticle integration can mitigate entropy generation in HVAC applications.

Further supporting this trend in HVAC-focused applications, Patel et al. (2024) [141] explored hybrid Al₂O₃–TiO₂ nanoparticles dispersed in R600a in both experimental and simulation settings. Their results demonstrated a substantial enhancement of 16.5% in COP at an optimal nanoparticle volume fraction of 0.2%. Additionally, the hybrid nanofluid contributed to improved compressor work reduction and system responsiveness under fluctuating thermal loads. Their study also noted that hybrid nanoparticles can offer synergistic benefits—combining high thermal conductivity from one material, such as Al₂O₃–TiO₂, with good dispersion and stability from another—thus making them especially effective in HVAC systems with variable operating conditions [142,143].

A distinct application-specific innovation is presented by Narayanasarma and Kuzhiveli (2020) [144], who focused not on the refrigerant, but on the lubricant used in refrigeration compressors. They experimentally evaluated the performance of SiO₂ nanoparticles added to polyolester (POE) and mineral oils. Their analysis revealed enhanced thermal conductivity and superior lubricity properties, which collectively led to improved compressor reliability and efficiency. Importantly, the study highlighted that nanolubricants could also reduce wear and friction losses, demonstrating a valuable yet often overlooked application route for nanotechnology in refrigeration systems.

From a comparative perspective, these studies collectively demonstrate that the influence of nanofluids varies across systems and target components. In refrigeration applications, most gains are reported in COP improvement, compressor power reduction, and shortened cooling cycles—especially when using CuO or TiO₂ in refrigerants such as R134a. However, system stability is highly dependent on the nanoparticle concentration and surface treatment, which affects dispersion behavior. Meanwhile, HVAC&R systems tend to benefit from hybrid nanoparticles and lower GWP refrigerants like R600a, with pronounced improvements in heat transfer and exergy efficiency. Studies using Al₂O₃ and hybrid Al₂O₃–TiO₂ (Patel et al., 2024 [141]; Mohammed et al., 2024 [140]; Babarinde et al., 2020 [145]) show the importance of nanoparticle design and refrigerant compatibility in dictating system responsiveness and thermal load handling.

Additionally, the application of nanofluids to lubricants—as opposed to refrigerants—offers a complementary pathway to system performance enhancement. While this approach may not directly alter thermodynamic cycles, it can extend equipment lifespan and reduce energy consumption through mechanical efficiency improvements.

Quantitatively, the reported studies collectively reveal that nanofluid integration can yield COP enhancements ranging from 16.5% to 24.6%, with compressor work reductions up to 15% depending on the nanoparticle–refrigerant combination and concentration (Devendra et al.,2019 [146], Lin et al., 2017 [147]). Lubricant-based modifications with SiO₂ nanoparticles demonstrated thermal conductivity improvements of around 18%, contributing to smoother mechanical operation and improved compressor performance. Most optimal concentrations for achieving these gains fall within the 0.05% to 0.2% range by volume or mass, beyond which performance often plateaus or deteriorates due to viscosity increase or sedimentation. These figures underscore that nanofluid application in HVAC&R systems is a quantitatively validated strategy, though success depends on careful alignment of formulation, system design, and operational stability.

While experimental studies provide concrete evidence of nanofluid-enhanced performance in HVAC&R systems, they often face constraints related to cost, scalability, and real-time control over boundary conditions. To address these limitations and explore a broader design space, researchers have increasingly turned to numerical modeling and simulation. Section 2.3.2 delves into these numerical investigations, highlighting how computational tools such as CFD and thermodynamic modeling frameworks are used to predict nanofluid behavior, optimize system parameters, and validate experimental findings under controlled and hypothetical scenarios.

2.3.2. Numerical Investigations

The growing reliance on numerical approaches in nanofluid research for HVAC&R systems reflects the need for scalable, cost-effective, and repeatable methods to probe complex thermal phenomena. Section 2.3.2 synthesizes findings from 25 numerical studies, categorized into two principal methodological clusters: Computational Fluid Dynamics (CFD)-based studies (n = 14) and theoretical or analytical modeling approaches (n = 11).

These numerical methods serve two essential roles: first, they provide detailed insight into local heat transfer behavior, phase interactions, and pressure distributions that are difficult to measure experimentally; second, they allow rapid prototyping of design configurations and parametric sensitivity analysis under idealized boundary conditions. Notably, CFD simulations have been widely adopted for their ability to model nanofluid flow in complex geometries, such as microchannels, condenser tubes, and evaporators. Meanwhile, theoretical studies often rely on thermodynamic cycle modeling, heat balance equations, and exergy analyses to assess the macroscopic performance impact of nanofluid integration.

In terms of application domains, refrigeration systems account for the majority of numerical investigations (17 studies), compared to 7 studies on HVAC&R systems, and a single study focused on thermal management. This reflects a continued interest in optimizing the vapor compression cycle, compressor performance, and refrigerant behavior, particularly using nanorefrigerants and nanolubricants. HVAC-oriented models, on the other hand, emphasize energy efficiency at the system scale, often incorporating external variables such as ambient temperature, airflow dynamics, and hybrid heat exchanger designs.

This section aims to distill methodological insights, model structures, boundary condition considerations, and validation strategies employed across these studies. By doing so, it bridges the gap between conceptual modeling and practical implementation, highlighting how numerical tools contribute to both hypothesis testing and performance forecasting in nanofluid research.

Among the numerical approaches, Computational Fluid Dynamics (CFD) has emerged as a particularly powerful and versatile tool for evaluating nanofluid behavior in HVAC&R applications. By solving governing equations for fluid flow and heat transfer at both micro and macro scales, CFD studies enable detailed visualization of temperature distribution, velocity fields, pressure drops, and thermal boundary layer characteristics. The following subsection synthesizes insights from 14 CFD-based investigations, emphasizing their modeling frameworks, simulation parameters, and how these studies validate or extend experimental findings across various application domains.

Computational Fluid Dynamics (CFD) Studies

Computational Fluid Dynamics (CFD) plays a vital role in exploring the thermal and hydrodynamic behavior of nanofluids in HVAC&R systems. These simulation-based investigations provide detailed insight into convective mechanisms, pressure gradients, entropy generation, and system optimization—parameters often challenging to capture through experimental approaches alone. Fourteen studies in this category demonstrate the versatility and predictive power of CFD in modeling nanofluid-enhanced systems across various application domains (Table 8).

In the HVAC sector, Aziz et al. (2025) [148] developed a hybrid modeling approach combining CFD with advanced neural network optimization to assess the energy performance of a PV-integrated heat pump system. Their simulation employed nanofluids to improve thermal conductivity within the condenser and evaporator loops. Results showed that system efficiency improved significantly, and the integration of Levenberg–Marquardt backpropagation models allowed for real-time optimization of operational parameters, such as flow rate and temperature setpoints. Benazzouz et al. (2025) [149] examined the thermal behavior of a domestic-scale HVAC system using nanorefrigerants through 3D CFD modeling. The simulation framework incorporated variable nanoparticle concentrations of Al₂O₃ dispersed in R134a refrigerant. Their results indicated that optimal nanoparticle dispersion enhanced heat transfer efficiency by up to 21%, particularly under transient load conditions. They also demonstrated that the nanoparticle-enhanced refrigerant suppressed thermal stratification and enabled quicker attainment of setpoint temperatures, reducing overall energy demand.

In refrigeration systems, Boldoo et al. (2022) [150] performed a numerical investigation on the thermal performance of nanolubricants in a vapor–compression refrigeration system. The study utilized Al₂O₃ and TiO₂ nanoparticles suspended in mineral oil as the lubricant base. CFD simulations showed that Al₂O₃ nanolubricants enhanced thermal conductivity by approximately 12% and improved compressor work efficiency by 8%, while TiO₂ showed better flow stability across narrow piping configurations. Their comparative modeling confirmed the suitability of these nanolubricants for miniaturized systems operating under fluctuating thermal loads.

Khan et al. (2023) [151] advanced the field with a three-dimensional mathematical model of a solar-assisted HVAC&R system integrated with nanofluid-based thermal storage. While the full set of simulation parameters and nanoparticle types was not explicitly detailed, the model focused on exergy analysis and entropy reduction across system components. The incorporation of CFD allowed the authors to validate the energy gain potential of solar–HVAC coupling and demonstrate the utility of nanofluids in dynamic thermal storage control, although their conclusion was not explicitly provided.

Another significant contribution to HVAC&R system modeling comes from Colangelo et al. (2021) [152], who numerically assessed a complex HVAC network using hybrid nanofluids—specifically Al₂O₃-Cu/water suspensions—in multiple loop configurations. Their CFD model revealed up to 28% improvement in heat transfer coefficient compared to conventional water-based fluids, particularly in spiral and wavy tube geometries. This study underscored the effectiveness of multi-component nanoparticles in enhancing both thermal and flow performance, offering strong implications for future HVAC system design strategies (Colangelo et al., 2021) [152].

Collectively, these CFD-based studies illustrate that nanoparticle-enhanced working fluids—whether used as refrigerants, lubricants, or coolants—offer significant thermohydraulic benefits. Simulation frameworks commonly apply steady-state or transient Navier–Stokes solvers coupled with energy conservation equations, often incorporating turbulence models (e.g., k–ε or RNG) and conjugate heat transfer formulations. The studies differ in complexity and scope, from system-level simulations of solar-assisted HVAC loops to microchannel and heat exchanger-focused investigations.

Moreover, nanoparticle type and base fluid pairing strongly influence simulation outcomes. Al₂O₃ remains the most commonly modeled nanoparticle due to its well-documented thermal properties and availability. However, hybrid combinations and metal oxide variants (e.g., CuO, TiO₂) are increasingly simulated to capture synergistic heat transfer effects. Base fluids range from water and ethylene glycol in cooling applications to R134a and R410A in refrigeration, each selected based on the intended system’s thermal load characteristics and environmental footprint.

In summary, CFD studies bridge the gap between conceptual design and experimental validation by offering a cost-effective, scalable, and insightful tool for evaluating nanofluid integration in thermal systems. These models not only confirm the potential performance benefits observed in lab-scale tests but also offer parametric sensitivity insights that guide optimal system configurations for real-world deployment.

Table 8. Types of nanoparticles, base fluid/refrigerant, and key findings.

Ref.	Nanoparticle Type	Base Fluid/Refrigerant	Key Findings
[151,153,154,155,156]	Al2O3, SiO2, Fe	R134a, Water	↑ HVAC system efficiency by approximately 10% $\downarrow$Compressor power at 0–5% vol fraction ↑ 10% in thermal performance using nanofluids Maximum COP with 0.2% volume fraction of nanoparticles ↑ 7.6% in effectiveness of a plate heat exchanger $\downarrow$11.6% compressor power 14.6% improvement in COP ↑ cold chain efficiency by reducing energy consumption ↑ Thermal characteristics by inlet velocity, but a higher pressure drop
[150]	MWCNT	R1234yf	Analyze the specific heats of the various MWCNT nanoparticle-enhanced [HMIM] cation-based ionic liquids Effect of concentration (0–1%) on COP at assorted Generator temperatures (303–383 K) were obtained experimentally at a temperature and a concentration of 303–383 K and 0–1 wt%, respectively, and were subsequently used for modeling the solubility and absorption refrigeration cycle
[152]	Hybrid	Ethyl glycol	Impact of activation parameters and Schmit number on dimensionless concentration for different nanofluids
[156,157,158,159,160]	CuO, Cu-based nanofluids	Water, R134a, ethylene-glycol	Optimal performance at 4% nanofluid concentration ↑ cold chain efficiency by $\downarrow$energy consumption ↑ COP 20% for CuO/Water, 25% for CuO/water with insulation ↑ Cooling power (about 5%) Simulation results show that, a 9 m2 solar collector, a 0.3 m3 storage tank, and 0.05 m thick polystyrene insulation|The active barocaloric regenerative refrigeration cycle is supposed to work as a domestic refrigerator in temperature range of 255 ÷ 290 K
[153,156,158,160,161]	TiO2, MO	R134a, POE Oil, LPG	0.5 to 1 g TiO2 addiction to the POE oil performs better than that of 1.5 g TiO2. Predicting irreversibility using ANFIS-SC model: RSME = 0.998, MAPE = 0.078% ↑ cold chain efficiency by $\downarrow$ energy consumption The comparison of variance, root mean square error (RMSE), mean absolute percentage error (MAPE) were 0.996–0.999, 0.0296–0.1726 W, and 0.108–0.176% marginal variability values

Table 8. Types of nanoparticles, base fluid/refrigerant, and key findings.

Ref.	Nanoparticle Type	Base Fluid/Refrigerant	Key Findings
[151,153,154,155,156]	Al2O3, SiO2, Fe	R134a, Water	↑ HVAC system efficiency by approximately 10% $\downarrow$Compressor power at 0–5% vol fraction ↑ 10% in thermal performance using nanofluids Maximum COP with 0.2% volume fraction of nanoparticles ↑ 7.6% in effectiveness of a plate heat exchanger $\downarrow$11.6% compressor power 14.6% improvement in COP ↑ cold chain efficiency by reducing energy consumption ↑ Thermal characteristics by inlet velocity, but a higher pressure drop
[150]	MWCNT	R1234yf	Analyze the specific heats of the various MWCNT nanoparticle-enhanced [HMIM] cation-based ionic liquids Effect of concentration (0–1%) on COP at assorted Generator temperatures (303–383 K) were obtained experimentally at a temperature and a concentration of 303–383 K and 0–1 wt%, respectively, and were subsequently used for modeling the solubility and absorption refrigeration cycle
[152]	Hybrid	Ethyl glycol	Impact of activation parameters and Schmit number on dimensionless concentration for different nanofluids
[156,157,158,159,160]	CuO, Cu-based nanofluids	Water, R134a, ethylene-glycol	Optimal performance at 4% nanofluid concentration ↑ cold chain efficiency by $\downarrow$energy consumption ↑ COP 20% for CuO/Water, 25% for CuO/water with insulation ↑ Cooling power (about 5%) Simulation results show that, a 9 m2 solar collector, a 0.3 m3 storage tank, and 0.05 m thick polystyrene insulation|The active barocaloric regenerative refrigeration cycle is supposed to work as a domestic refrigerator in temperature range of 255 ÷ 290 K
[153,156,158,160,161]	TiO2, MO	R134a, POE Oil, LPG	0.5 to 1 g TiO2 addiction to the POE oil performs better than that of 1.5 g TiO2. Predicting irreversibility using ANFIS-SC model: RSME = 0.998, MAPE = 0.078% ↑ cold chain efficiency by $\downarrow$ energy consumption The comparison of variance, root mean square error (RMSE), mean absolute percentage error (MAPE) were 0.996–0.999, 0.0296–0.1726 W, and 0.108–0.176% marginal variability values

Theoretical Modeling and Analytical Studies

Theoretical and analytical modeling plays a critical role in evaluating the performance of nanofluids in HVAC&R systems by allowing researchers to predict system behavior under idealized or parametric conditions, often without the cost or complexity of experimental setups. These models provide insight into energy and exergy performance, thermoeconomic viability, and the thermophysical impact of nanoparticle inclusion within various refrigerant cycles.

Li and Lu (2022) [162] conducted a comprehensive thermodynamic modeling of vapor–compression refrigeration cycles using Al₂O₃-based nanorefrigerants across several environmentally friendly refrigerants, including R600a, R134a, R1234yf, and R1233zd(E). Their theoretical framework employed both energetic and exergetic analyses, revealing that the coefficient of performance (COP) improved by more than 20% with Al₂O₃ nanofluids. Moreover, the highest exergetic efficiency was observed with R600a, whereas R1233zd(E) yielded the lowest environmental impact, making it the most sustainable option despite lower thermodynamic output.

Akhtar and Rajput (2023) [163] explored the energy and exergy dynamics of a vapor–compression refrigeration system using nanolubricants, specifically MWCNTs and graphene blended with POE oil. Their theoretical investigation showed a potential reduction in power consumption by up to 16%, along with lower pressure ratios and improved compressor performance. This highlights the efficacy of carbon-based nanoparticles in enhancing thermal conductivity and lubrication properties in traditional systems.

In a similar context, Kumar and Rajput (2023) [164] assessed a computerized refrigeration cycle operating with CuO-Al₂O₃-based mineral oil nanolubricants and low-GWP refrigerants such as R600a, R134a, R1234yf, and R1233zd(E). Their results indicated up to a 23% reduction in power consumption and substantial improvement in exergy efficiency, especially when hybrid nanoparticles were used. This study also emphasized the role of computerized control systems in optimizing nanofluid-based cycles through dynamic parameter tuning.

The study by Yilmaz et al. (2024) [165] focused on the theoretical analysis of a cascade vapor–compression refrigeration system utilizing CNT, CuO, and TiO₂ nanofluids with R290 and R1233zd(E) refrigerants. Their thermodynamic analysis demonstrated an 11% reduction in compressor power consumption and significant improvement in environmental metrics, such as CO₂ emissions. Among the refrigerants tested, R290 consistently exhibited better energetic performance, while the hybrid nanofluids led to enhanced heat transfer across cascade stages.

Gürbüz, Keçebaş, and Sözen (2022) [166] applied exergy and thermoeconomic modeling to evaluate nanofluid integration in NH₃–H₂O absorption refrigeration systems. Utilizing various binary nanofluids such as MgO–Al₂O₃, ZnO–Al₂O₃, and TiO₂, their analysis revealed TiO₂ as the most effective additive in terms of both exergy efficiency and cost–performance balance. The levelized cost of cooling for the system ranged between USD 0.110 and USD 0.180 per kWh, suggesting that thermoeconomic modeling provides essential decision-making tools for large-scale deployment.

Collectively, these theoretical studies underscore the utility of nanofluids in enhancing both energy efficiency and sustainability in vapor–compression and absorption systems. Across various refrigerants and base oils, nanoparticle inclusion—whether metallic oxides like Al₂O₃ or carbon-based like CNTs and graphene—proved to offer substantial thermodynamic benefits, including up to 23% power reduction and over 20% improvement in COP. Furthermore, the incorporation of exergetic and economic modeling provides a more holistic understanding of system feasibility, especially in the context of emerging low-GWP refrigerants and renewable-integrated HVAC architectures.

To consolidate the diverse theoretical investigations discussed in this subsection, a summary table, Table 9, has been developed that captures the key modeling characteristics and performance outcomes. The table categorizes each study by nanoparticle type, base fluid or refrigerant, modeling scope, and reported thermodynamic or economic benefits. This structured synthesis facilitates cross-comparison of findings and highlights recurring modeling trends—such as the predominance of Al₂O₃ and hybrid nanofluids, frequent use of R600a and R1233zd(E) as sustainable refrigerants, and consistent improvements in COP, exergy efficiency, or power savings. These insights provide a valuable reference point for researchers and practitioners seeking to integrate nanofluid-based enhancements into advanced HVAC&R models.

Table 9. Summary of theoretical and analytical modeling studies on nanofluid-enhanced HVAC&R systems, highlighting nanoparticle types, base fluids or refrigerants, modeling approaches, and key quantitative findings.

Ref.	Nanoparticle Type	Base Fluid/Refrigerant	Key Findings
[162,164,167,168,169]	AL2O3, CuO-Al2O3/MO	LiBr-H2O, R600a, R134a, R1234yf, R1233ZDE	↑ COP more than 20%, ↑ max exergy efficiency: 38.48% $\downarrow$Power consumption: up to 23%, Exergy destruction up to 49%, ↑ COP by 29%, ↑ Second law efficiency to 28% ↑ COP of the solar cycle by 4.51% ↑ COP range: 16 to 25%
[164,165,167,168]	Cu, CuO	R600a, R134a, R1234yf, R1233ZDE	$\downarrow$Power consumption: up to 23%, ↑ COP by 29%, ↑ Second law efficiency up to 28% $\downarrow$ Power consumption by 11%,$\downarrow$ CO2 emission: 12.5%
[166,170,171]	TiO2, FeOTiO2	POE Oil, NH3–H2O	TiO2 is most effective, Levelized cost ranges USD 0.773/kWh to USD 0.875/kWh ↑ COP, circulation ratio, and internal temperature reached 17%, 57%, and 20% $\downarrow$Irreversibility up to 18%
[163,165]	CNT, MWCNT	POE Oil, R290/R1233ZDE	$\downarrow$ Power consumption: up to 16%, $\downarrow$ Pressure ratio up to 5.6%, $\uparrow$COP by 11%
[163]	Graphene	POE Oil	$\downarrow$ Power consumption reduced: up to 16%, $\downarrow$Pressure ratio up to 5.6%, ↑ COP by 11%
[172,173]	Ag, Fe2O3	Ethylene glycol/DI-water	↑ convective HTC increased up to 11%

Table 9. Summary of theoretical and analytical modeling studies on nanofluid-enhanced HVAC&R systems, highlighting nanoparticle types, base fluids or refrigerants, modeling approaches, and key quantitative findings.

Ref.	Nanoparticle Type	Base Fluid/Refrigerant	Key Findings
[162,164,167,168,169]	AL2O3, CuO-Al2O3/MO	LiBr-H2O, R600a, R134a, R1234yf, R1233ZDE	↑ COP more than 20%, ↑ max exergy efficiency: 38.48% $\downarrow$Power consumption: up to 23%, Exergy destruction up to 49%, ↑ COP by 29%, ↑ Second law efficiency to 28% ↑ COP of the solar cycle by 4.51% ↑ COP range: 16 to 25%
[164,165,167,168]	Cu, CuO	R600a, R134a, R1234yf, R1233ZDE	$\downarrow$Power consumption: up to 23%, ↑ COP by 29%, ↑ Second law efficiency up to 28% $\downarrow$ Power consumption by 11%,$\downarrow$ CO2 emission: 12.5%
[166,170,171]	TiO2, FeOTiO2	POE Oil, NH3–H2O	TiO2 is most effective, Levelized cost ranges USD 0.773/kWh to USD 0.875/kWh ↑ COP, circulation ratio, and internal temperature reached 17%, 57%, and 20% $\downarrow$Irreversibility up to 18%
[163,165]	CNT, MWCNT	POE Oil, R290/R1233ZDE	$\downarrow$ Power consumption: up to 16%, $\downarrow$ Pressure ratio up to 5.6%, $\uparrow$COP by 11%
[163]	Graphene	POE Oil	$\downarrow$ Power consumption reduced: up to 16%, $\downarrow$Pressure ratio up to 5.6%, ↑ COP by 11%
[172,173]	Ag, Fe2O3	Ethylene glycol/DI-water	↑ convective HTC increased up to 11%

2.3.3. Hybrid Studies

Hybrid studies that integrate both experimental testing and numerical modeling provide a robust approach for evaluating nanofluid performance in HVAC&R systems. By complementing empirical data with theoretical and computational insights, these studies offer both the realism of physical systems and the flexibility of simulation to explore design alternatives, parameter optimization, and scale-up feasibility.

Akhayere et al. (2023) [174] performed a comprehensive assessment of nanorefrigerants in a modified vapor compression system, examining their effects on system efficiency and environmental impact under post-Kyoto Protocol climate objectives. The hybrid methodology involved comparative simulation and real-world testing to evaluate thermal and energetic performance improvements using advanced refrigerant formulations.

Khetib et al. (2022) [175] explored the impact of nanofluid-enhanced ejector refrigeration systems for thermal management in lithium-ion battery storage. Their hybrid approach combined CFD simulation with experimental evaluation using Al₂O₃ nanoparticles. Results demonstrated that a 0.3% volume fraction of nanofluids improved the system’s coefficient of performance by 20% and reduced the evaporator outlet temperature by 5.3 °C, thereby enhancing cooling uniformity and reducing the temperature gradient within battery cells. The study highlighted nanofluids as an effective strategy for improving both energy efficiency and thermal safety in advanced energy storage applications.

In a different line of inquiry, Gürbüz et al. (2022) [166] conducted a hybrid investigation involving zinc oxide (ZnO) nanofluids in diffusion absorption refrigeration systems (DARS). The dual-method approach enabled validation of heat transfer coefficients, flow stability, and refrigerant absorption rates under varying nanoparticle concentrations. Their study focused on solar-integrated refrigeration systems and emphasized the influence of nanoparticle morphology on both heat and mass transfer behaviors.

Wang et al. (2018) [176] examined the mass transfer performance of nanosolution-based falling film absorption systems. Their hybrid analysis involved formulating nanosolutions using LiBr-H₂O as the base pair with metallic nanoparticles. The experimental portion confirmed enhanced absorption rates, while the numerical model evaluated surface tension effects, diffusion rates, and film stability. This study demonstrated that adding nanoparticles reduced absorption time and increased film efficiency, thereby improving refrigeration system responsiveness under partial load conditions.

Finally, Li et al. (2019) [177] performed one of the most rigorous comparative hybrid investigations using H₂O-based silica nanofluids as secondary refrigerants. Both experimental measurements and modeling demonstrated that using nanofluids significantly improved heat exchanger performance. Key thermophysical properties such as thermal conductivity and viscosity were characterized and validated, showing a consistent heat transfer enhancement of up to 18% and a reduction in thermal response time by over 15%. This dual-method study confirmed nanofluids as viable candidates for retrofitting secondary fluid loops in refrigeration systems.

These hybrid studies collectively validate the tangible benefits of nanofluid application across diverse refrigeration contexts—from absorption and ejector cycles to battery cooling and secondary loop retrofits. The blend of modeling and experimentation provides more reliable assessments of nanoparticle behavior in real-world conditions, accounting for non-idealities such as agglomeration, pressure drops, and thermal degradation. Across all studies, nanofluids based on Al₂O₃, ZnO, and silica consistently improved heat transfer and system performance, while also contributing to better environmental outcomes in terms of reduced energy consumption and enhanced thermal efficiency.

2.3.4. AI/ML-Based Approaches

The integration of Artificial Intelligence (AI) and Machine Learning (ML) in nanofluid research has shown transformative potential, particularly in accelerating predictions of thermophysical properties and performance metrics like coefficient of performance (COP), heat transfer coefficients, and cooling capacity. While there remains a notable gap in direct applications of AI-ML to full-scale HVAC&R systems incorporating nanofluids, a significant number of studies have focused on foundational thermophysical modeling, parameter optimization, and component-level system enhancements. These studies collectively offer a trajectory for future work that may bridge data-driven modeling and system-level optimization.

Artificial intelligence (AI) and machine learning (ML) techniques have emerged as powerful tools for analyzing complex nonlinear behaviors and optimizing performance in thermal systems. While direct applications of AI/ML to full-cycle HVAC or refrigeration systems containing nanofluids remain limited, a rapidly expanding body of the literature has focused on predicting thermophysical properties, key system parameters such as coefficient of performance (COP), heat transfer coefficient, and thermal conductivity—parameters that underpin system-level behavior. These studies serve as foundational work for integrating AI/ML tools into larger-scale predictive frameworks, control systems, and optimization modules for HVAC&R systems enhanced by nanotechnology.

In refrigeration systems, hybrid models combining support vector regression (SVR), genetic algorithms, and extreme learning machines have demonstrated substantial improvement in the prediction of relative cooling power (RCP) of magnetocaloric materials (Shamsah, 2024) [178]. Similarly, Gill et al. (2020) [179] employed adaptive neuro–fuzzy inference systems (ANFIS) to analyze exergetic performance of LPG-refrigerated systems using TiO₂-based nanolubricants, reporting predictive accuracies with variations under 1% from experimental observations.

Several studies have advanced the use of Gaussian Process Regression (GPR), Support Vector Regression (SVR), and Artificial Neural Networks (ANNs) for optimizing nanofluid performance in HVAC systems. Sharma et al. (2021) [180] utilized GPR and SVR to predict COP and cooling capacity for TiO₂-POE nanolubricants in a domestic refrigeration cycle, achieving R² values exceeding 0.99. Meanwhile, Aziz et al. (2025) [148] developed an ANN model integrated with the Levenberg–Marquardt algorithm to predict thermal performance in photovoltaic-based HVAC systems using tri-hybrid nanofluids, achieving extremely low error rates (10⁻⁷ to 10⁻⁵).

Optimization studies combining multi-objective evolutionary algorithms with AI models are also notable. Boyaghchi et al. (2016) [181] employed Non-dominated Sorting Genetic Algorithm II (NSGA-II) to enhance both exergetic efficiency and economic cost for cascade refrigeration systems using CuO nanofluids and eco-friendly refrigerants. Their study achieved thermal and exergy COP improvements of 10.13% and 0.63, respectively.

In lubrication and material synthesis domains, AI models have demonstrated precise predictive capabilities. For instance, a cascaded forward neural network (CFNN) developed by a recent study modeled viscosity and thermal conductivity of Al₂O₃–CuO nanofluids, yielding correlation coefficients above 0.99 (MAPE < 1%)—highlighting its suitability for predictive integration in solar thermal systems. In another innovative study, an ANN-GA hybrid was successfully used to predict the optimal synthesis conditions of ZnO nanoparticles for photocatalytic use, predicting sizes as small as 5.3 nm with high agreement to experimental outcomes.

The scope of ML deployment has extended even to nanoscale simulations. A study combining molecular dynamics with decision tree and K-nearest neighbors models predicted thermophysical properties of iron-oxide nanofluids in CO₂ media, with the decision tree achieving a 99% accuracy rate across temperature and concentration ranges.

These works collectively show the potential of AI/ML models in reducing experimental loads, improving predictive capabilities, and enabling real-time optimization of nanofluid-based systems. However, most models are currently constrained to material or subsystem level. Future work should focus on:

• Developing system-level digital twins for HVAC&R applications that integrate nanofluid property predictions into full-cycle simulations.
• Applying reinforcement learning or advanced control-based AI models for real-time system optimization, considering dynamic environmental loads.
• Creating large-scale datasets through experimental or simulated studies to train generalizable AI models across multiple refrigerants and nanofluids.

This transition from property-level modeling to system-level deployment represents the next frontier in intelligent HVAC&R systems research.

To facilitate a rapid understanding of the methodological and performance-related trends across AI/ML-based investigations on nanofluids, a condensed summary is provided Table 10. The table categorizes studies based on nanoparticle type and highlights the key modeling techniques, performance parameters evaluated, and the principal findings. Abbreviations are employed to maintain brevity without compromising technical clarity. This synthesized overview enables researchers and practitioners to identify promising material–method combinations and guide future investigations toward system-level HVAC&R integration.

Table 10. Summary of AI/ML-based studies grouped by nanoparticle type, including employed techniques, evaluated parameters, and condensed key findings.

Ref.	Type of Nanoparticle	Method/Technique	Parameter Evaluated	Key Findings
[148,177,179]	RE2TM2Y ternary intermetallic compounds (e.g., Gd, Tb, Dy, Ho, Er, Tm with Ni, Cu, Co and Sn, In, Cd, Ga, A	MOO using (NSGA-II), (GPR) (SVR) with RBF kernel, (SELM), (GSVR) with Gaussian and Polynomial kernels	Thermal and exergy COP, total product cost rate, Relative Cooling Power (RCP), applied magnetic field, ionic radii, RMSE, MAE, correlation coefficient (CC)	GU-GSVR vs. SELM (1–7 T field): RMSE ↓ 27.97% MAE ↓ 76.01% CC ↑ 10.55% R134a Refrigerant: Thermal COP: 10.13% Exergy COP: 0.6530% ↑ 8.5%/12.3% over base case R1234ze Refrigerant: Annual cost reduced to USD 6148 (↓ 2.4%) Optimal nanoparticle fraction: 0.0149–0.041 GPR-RBF Model: Highest predictive accuracy COP improved with 0.5–1.0 g TiO2 Performance declined at 1.5 g TiO2
[182,183,184]	Halloysite nanotubes (HNTs) in SAE 5W40	RSM, NSGA-II, MLP, ML algorithms, Cascaded Forward Neural Network (CFNN), GPR, ANN, SVM, DT	Dynamic viscosity, TC, density, specific heat capacity	Property Enhancement: Max TC increase: 23.24% at 1 vol.% and 60 °C Max viscosity increase: 21.4% at 1 vol.% and 30 °C Trend: TC and viscosity increase with nanoparticle concentration
[183,185,186,187,188,189,190,191,192]	Hybrid nanofluid (Al2O3–Cu–Water) in porous media	(ANN) with (PSO), (ANN) + (GA), (SVR), (RF); CFD-based simulation, SVR, MLP, RF (regressor models)	Temperature, time, NaOH concentration → Nanoparticle size, pressure, substrate thermal effusivity, Nusselt number, heat transfer efficiency, thermal performance factor, Reynolds number, permeability, nanoparticle volume fraction, entropy generation, shear stress	ML Prediction of CHF Trends: MLP: Best performance (mean R2 ~87%) RF: High single-run R2 (90%) but lower mean R2 SVR: Lowest performance Top features: Pressure, nanoparticle size Substrate thermal effusivity: Negligible impact ML models outperformed Kandlikar’s correlation Heat Transfer Enhancement: CNi inserts: 12.5–23.9% improvement CDNi inserts: Up to 32.6% improvement ML trained on 220 CFD + experimental data showed high prediction accuracy ZnO Nanoparticle Synthesis and Efficiency: Predicted size: 5.67 nm at 59 °C, 56 min, 0.08 M NaOH Experimental: 5.3 ± 0.4 nm Photocatalytic efficiency: 74% (vs. 58% for commercial ZnO) Flow and Entropy Analysis: Heat transfer ↑ with Re, permeability, and volume fraction Entropy shows nonmonotonic trend ANN-PSO provided accurate correlations for thermal parameters
[148,189,193,194]	Tri-hybrid nanofluid (specific composition not explicitly stated); used as coolant under PV panels	(ANN) trained with Levenberg–(LMA-TNN); combined with Lobatto IIIa numerical solver, Bayesian (BRNN), (QLM), k (ANN) with hyperparameter optimization, combined with CFD using Keller Box Method (KBM)	Temperature, velocity profile, thermal radiation, magnetic field, Casson parameter, ANN regression accuracy, Reynolds number, Deborah number, MSE, regression, ST index, heat transfer rate, shear stress, magnetic field	High model accuracy with errors ranging from 10−7 to 10−11 ANN models reduced computational time and effectively captured flow/temperature behavior ANN and KBM achieved near-perfect regression (R2 ≈ 1.0) PG–water nanofluid showed comparable heat transfer to EG-based nanofluid Curvature and suction increased velocity and decreased temperature in flow regime BRNN addressed uncertainty and reduced overfitting efficiently Magnetic field and curvature enhanced overall heat transfer
[179,195,196]	TiO2 nanoparticles in MO oil; LPG refrigerant	ANFIS with GP, SC, FCM clustering; WS optimization, Gene Expression Programming (GEP), and Adaptive Neuro–Fuzzy Inference System (ANFIS), Adaptive Neuro–Fuzzy Inference System (ANFIS) using Grid Partitioning and Subtractive Clustering	Thermal conductivity, viscosity, density, specific heat, temperature (20–60 °C), concentration (0–0.3%), specific heat capacity (SHC), Second-law efficiency, total irreversibility, RMSE, MAPE, variance	ANFIS (subtractive clustering): High accuracy (Var: 0.996–0.999, RMSE: 0.0296–0.1726 W, MAPE: 0.108–0.176%); outperformed ANN GP-ANFIS: Best for SHC (R = 0.99992, MAPE = 0.036%) and TC (R = 0.99833, MAPE = 0.218%) SC-ANFIS: Best for density and viscosity (R ≈ 0.9989) Key sensitivities: Density_np (0.53%), VF (1.9%), SHC_np (1.6%), TC_np (1.7%) GEP vs. ANFIS: GEP more accurate (R > 0.9825, RMSE = 0.79, MAPE < 2.16%) than ANFIS (R > 0.96, RMSE = 1.50, MAPE < 2.93%)

Table 10. Summary of AI/ML-based studies grouped by nanoparticle type, including employed techniques, evaluated parameters, and condensed key findings.

Ref.	Type of Nanoparticle	Method/Technique	Parameter Evaluated	Key Findings
[148,177,179]	RE2TM2Y ternary intermetallic compounds (e.g., Gd, Tb, Dy, Ho, Er, Tm with Ni, Cu, Co and Sn, In, Cd, Ga, A	MOO using (NSGA-II), (GPR) (SVR) with RBF kernel, (SELM), (GSVR) with Gaussian and Polynomial kernels	Thermal and exergy COP, total product cost rate, Relative Cooling Power (RCP), applied magnetic field, ionic radii, RMSE, MAE, correlation coefficient (CC)	GU-GSVR vs. SELM (1–7 T field): RMSE ↓ 27.97% MAE ↓ 76.01% CC ↑ 10.55% R134a Refrigerant: Thermal COP: 10.13% Exergy COP: 0.6530% ↑ 8.5%/12.3% over base case R1234ze Refrigerant: Annual cost reduced to USD 6148 (↓ 2.4%) Optimal nanoparticle fraction: 0.0149–0.041 GPR-RBF Model: Highest predictive accuracy COP improved with 0.5–1.0 g TiO2 Performance declined at 1.5 g TiO2
[182,183,184]	Halloysite nanotubes (HNTs) in SAE 5W40	RSM, NSGA-II, MLP, ML algorithms, Cascaded Forward Neural Network (CFNN), GPR, ANN, SVM, DT	Dynamic viscosity, TC, density, specific heat capacity	Property Enhancement: Max TC increase: 23.24% at 1 vol.% and 60 °C Max viscosity increase: 21.4% at 1 vol.% and 30 °C Trend: TC and viscosity increase with nanoparticle concentration
[183,185,186,187,188,189,190,191,192]	Hybrid nanofluid (Al2O3–Cu–Water) in porous media	(ANN) with (PSO), (ANN) + (GA), (SVR), (RF); CFD-based simulation, SVR, MLP, RF (regressor models)	Temperature, time, NaOH concentration → Nanoparticle size, pressure, substrate thermal effusivity, Nusselt number, heat transfer efficiency, thermal performance factor, Reynolds number, permeability, nanoparticle volume fraction, entropy generation, shear stress	ML Prediction of CHF Trends: MLP: Best performance (mean R2 ~87%) RF: High single-run R2 (90%) but lower mean R2 SVR: Lowest performance Top features: Pressure, nanoparticle size Substrate thermal effusivity: Negligible impact ML models outperformed Kandlikar’s correlation Heat Transfer Enhancement: CNi inserts: 12.5–23.9% improvement CDNi inserts: Up to 32.6% improvement ML trained on 220 CFD + experimental data showed high prediction accuracy ZnO Nanoparticle Synthesis and Efficiency: Predicted size: 5.67 nm at 59 °C, 56 min, 0.08 M NaOH Experimental: 5.3 ± 0.4 nm Photocatalytic efficiency: 74% (vs. 58% for commercial ZnO) Flow and Entropy Analysis: Heat transfer ↑ with Re, permeability, and volume fraction Entropy shows nonmonotonic trend ANN-PSO provided accurate correlations for thermal parameters
[148,189,193,194]	Tri-hybrid nanofluid (specific composition not explicitly stated); used as coolant under PV panels	(ANN) trained with Levenberg–(LMA-TNN); combined with Lobatto IIIa numerical solver, Bayesian (BRNN), (QLM), k (ANN) with hyperparameter optimization, combined with CFD using Keller Box Method (KBM)	Temperature, velocity profile, thermal radiation, magnetic field, Casson parameter, ANN regression accuracy, Reynolds number, Deborah number, MSE, regression, ST index, heat transfer rate, shear stress, magnetic field	High model accuracy with errors ranging from 10−7 to 10−11 ANN models reduced computational time and effectively captured flow/temperature behavior ANN and KBM achieved near-perfect regression (R2 ≈ 1.0) PG–water nanofluid showed comparable heat transfer to EG-based nanofluid Curvature and suction increased velocity and decreased temperature in flow regime BRNN addressed uncertainty and reduced overfitting efficiently Magnetic field and curvature enhanced overall heat transfer
[179,195,196]	TiO2 nanoparticles in MO oil; LPG refrigerant	ANFIS with GP, SC, FCM clustering; WS optimization, Gene Expression Programming (GEP), and Adaptive Neuro–Fuzzy Inference System (ANFIS), Adaptive Neuro–Fuzzy Inference System (ANFIS) using Grid Partitioning and Subtractive Clustering	Thermal conductivity, viscosity, density, specific heat, temperature (20–60 °C), concentration (0–0.3%), specific heat capacity (SHC), Second-law efficiency, total irreversibility, RMSE, MAPE, variance	ANFIS (subtractive clustering): High accuracy (Var: 0.996–0.999, RMSE: 0.0296–0.1726 W, MAPE: 0.108–0.176%); outperformed ANN GP-ANFIS: Best for SHC (R = 0.99992, MAPE = 0.036%) and TC (R = 0.99833, MAPE = 0.218%) SC-ANFIS: Best for density and viscosity (R ≈ 0.9989) Key sensitivities: Density_np (0.53%), VF (1.9%), SHC_np (1.6%), TC_np (1.7%) GEP vs. ANFIS: GEP more accurate (R > 0.9825, RMSE = 0.79, MAPE < 2.16%) than ANFIS (R > 0.96, RMSE = 1.50, MAPE < 2.93%)

While Table 10 contains details study and AI methodology, to better illustrate the chronological development of artificial intelligence and machine learning applications in nanofluid heat transfer studies, a timeline diagram is presented in Figure 10. The diagram highlights the progressive adoption of various models—from NN and ANFIS to advanced hybrid techniques like QLM and BRNN—across prediction tasks such as thermal conductivity, irreversibility, viscosity, and performance metrics. The increasing model complexity reflects growing computational integration within thermal-fluid research.

2.3.5. Review-Based Papers

A growing body of the literature has systematically reviewed the role of nanofluids and nanolubricants in enhancing the performance of HVAC&R systems. These review studies provide consolidated insights into the thermophysical mechanisms, experimental advancements, modeling strategies, and emerging trends associated with the integration of nanoparticles into conventional systems. Unlike primary studies that focus on specific nanofluid formulations or experimental validations, review-based investigations offer a broader outlook across various nanomaterials, system configurations, and operating conditions.

Several reviews have focused on vapor compression refrigeration (VCR) systems using nanorefrigerants. Bhattad et al. (2018) [197] provided one of the most comprehensive evaluations of nanofluids in refrigeration, identifying mechanisms such as reduced viscosity, improved thermal conductivity, and nanoparticle dispersion stability as key drivers of performance gains. Bilen et al. (2022) [198] and Kumar et al. (2023) [199] expanded upon this by evaluating system-level impacts, such as enhancement in COP, compressor work reduction, and energy savings across different refrigerant–nanoparticle combinations.

In the domain of vapor absorption systems, Pisal et al. (2024) [200], Jiang et al. (2022) [201], and Talpada and Ramana (2021) [202] examined the effects of adding nanoparticles to absorbents and working fluids. These studies underscore that metal oxide-based nanofluids (e.g., Al₂O₃, TiO₂, ZnO) significantly enhance heat and mass transfer in absorber and generator sections, while stability and sedimentation remain limiting factors.

Another critical theme is the use of nanolubricants in compressors, as discussed by Marcucci Pico et al. (2023) [203]. Their review highlights the synergistic effect of nanoparticles (e.g., CuO, MoS2) in reducing friction, enhancing lubrication, and mitigating compressor wear. Azmi et al. (2017) [204] and Patil et al. (2015) [205] similarly noted the dual benefit of nanolubricants in both improving energy efficiency and extending component life.

From a methodological standpoint, reviews by Ponticorvo et al. (2022) [206] and R et al. (2021) [207] provide crucial insights into nanoparticle dispersion stability, agglomeration prevention techniques, and the tribological behavior of nanorefrigerants. Their analyses are pivotal in identifying research gaps around surfactant compatibility, long-term stability, and nano-additive lifecycle effects.

Further, Du et al. (2018) [208] offered a broader scope by evaluating phase change materials (PCMs) integrated with nanostructures for cooling, heating, and power generation. Although not confined to refrigeration cycles alone, their findings have relevance in cold storage and building thermal management systems.

A few reviews, such as those by Kundan and Singh (2020) [209] and Valiyandi and Thampi (2023) [210], explored hybrid systems and alternative working fluids. Their discussions provide a forward-looking perspective on multi-component working pairs, hybrid nanofluids, and eco-friendly refrigerants for sustainable development.

Overall, the review-based literature plays an indispensable role in consolidating evidence, identifying optimal nanoparticle–base fluid combinations, addressing stability concerns, and shaping future experimental and computational research directions in nanofluid-enhanced HVAC&R systems.

2.3.6. Critical Analysis and Future Directions

The body of research conducted between 2015 and 2025 has successfully established nanofluids as a promising technology for enhancing the performance of HVAC&R systems. The reported improvements in heat transfer and the coefficient of performance (COP) are substantial. However, a critical evaluation of the existing literature reveals significant imbalances and unresolved challenges that currently impede the transition from laboratory-scale validation to widespread commercial deployment. This section provides a critical analysis of the current research landscape and proposes a strategic roadmap for future investigations.

A Critical Appraisal of the Current Research Landscape

A systematic review of the research methodologies, application scope, and analytical depth highlights five key areas of concern:

• Methodological Imbalance and Limited Synergy: The field is disproportionately reliant on experimental investigations. While empirical data is indispensable, the pronounced scarcity of hybrid studies—which integrate experimental work with validated numerical modeling—suggests a prevailing trial-and-error approach. This underutilization of synergistic methodologies means the predictive and optimization power of computational tools is not being fully leveraged to accelerate discovery and reduce research overhead.
• Asymmetrical Application Focus: There is a distinct and substantial focus on refrigeration systems at the expense of broader HVAC applications. This neglects the significant energy-saving potential in areas such as commercial chillers, large-scale heat pumps, and data center cooling, which constitute a major portion of global energy consumption for thermal management. This represents a strategic misalignment of research effort with the sectors promising the greatest potential impact.
• The Unresolved Challenge of Long-Term Viability: A critical deficiency in the current literature is the predominant focus on short-term performance metrics. Issues of paramount importance for practical implementation—such as long-duration fluid stability, nanoparticle agglomeration and sedimentation, potential for erosion and corrosion of system components, and material compatibility—remain largely unaddressed. Without robust, long-term operational data, the remarkable performance enhancements reported in laboratory settings remain academically compelling but commercially unproven.
• Deficiency in Economic and Lifecycle Analysis: The research convincingly demonstrates that nanofluids can improve thermodynamic efficiency, but it largely fails to address whether this improvement is economically viable or environmentally beneficial over the full lifecycle. The high initial cost of nanomaterials and the energy intensity of fluid preparation processes may offset the economic and environmental gains from operational energy savings. A comprehensive technoeconomic analysis (TEA) and lifecycle assessment (LCA) are conspicuously absent from most studies.
• Nascent Integration of AI and Machine Learning: While the application of AI/ML is a promising development, its current use is rudimentary. Existing models are almost exclusively focused on predicting the thermophysical properties of the nanofluids themselves or the performance of isolated components. A critical gap exists between this foundational, property-level prediction and the development of holistic, system-level models capable of dynamic optimization and control.

3. Conclusions and Future Directions

This review has systematically synthesized and critically evaluated the body of research on nanofluid applications in HVAC&R systems published between 2015 and 2025. The findings unequivocally confirm that nanofluids offer significant potential to enhance thermal performance, with numerous studies demonstrating substantial improvements in heat transfer and system efficiency. However, despite two decades of research, the transition from academic promise to widespread commercial adoption is impeded by several persistent and critical challenges. The current research landscape is characterized by a methodological imbalance favoring experimental studies; a narrow application focus on refrigeration systems; and a general deficiency in the long-term, economic, and system-level analyses required for industrial confidence.

To accelerate progress and mature this technology, future research must adopt a more strategic and integrated approach. Based on the critical analysis conducted in this review, the following directions are proposed as a roadmap for the field:

• Prioritize Long-Term, System-Level Validation: The foremost priority must be to shift from short-term, component-based experiments to long-duration studies on integrated, pilot-scale systems. For example, this involves running a nanofluid-enhanced chiller for a full cooling season (>2000 h) while continuously monitoring for performance degradation, changes in pressure drop, filter clogging, and signs of component erosion. This is the only way to generate conclusive data on fluid stability, material compatibility, and sustained performance under realistic operational cycling.
• Broaden the Application Scope to High-Impact HVAC Systems: Research efforts should be deliberately expanded into under-investigated, high-impact areas where the potential for energy savings is greatest. Specific examples include investigating nanorefrigerants in large-scale commercial chillers for office buildings, employing nanolubricants in the compressors of multi-zone heat pumps, and using nanofluids in the secondary loops of data center cooling systems.

While the thermodynamic benefits of these applications are promising, comprehensive evaluations of their cost-effectiveness are often lacking. Specifically, few studies provide quantifiable comparisons between the upfront cost premium of nanofluids and the achievable long-term energy savings. As a result, direct assessments of return on investment or payback periods remain limited. Future work should aim to bridge this gap by integrating performance testing with technoeconomic metrics to better support real-world decision-making and adoption.

3.

Promote Synergistic Hybrid Methodologies: To move beyond trial-and-error, hybrid studies that couple experimental work with validated numerical simulations (CFD) and theoretical models must become the standard. A practical workflow would involve using CFD to computationally screen and optimize the geometry of a microchannel heat exchanger for a specific nanofluid before fabricating and experimentally testing only the most promising designs. This synergy will enable more rapid and cost-effective optimization.

4.

Advance from Property Prediction to System-Level AI/ML: The research community must leverage AI and machine learning to develop dynamic “digital twins” of entire HVAC&R systems. For instance, an AI model could be trained to predict the onset of nanoparticle agglomeration using real-time sensor data (temperature, pressure, flow rate) and automatically adjust system operation to maintain stability, or forecast the remaining useful life of the fluid for predictive maintenance.

However, while data-driven AI/ML models have shown high predictive accuracy, their performance is highly dependent on the quality and representativeness of training data. These models may be vulnerable to extrapolation errors, especially under dynamic or extreme operating conditions such as low-temperature gradients or variable loads. To address this, future research should integrate physics-informed machine learning (PIML) frameworks that embed governing equations, boundary conditions, and thermodynamic constraints into model architectures. Such hybrid approaches can enhance generalizability, improve interpretability, and enable robust predictions with limited data—marking a critical step toward trustworthy AI-assisted nanofluid system design.

5.

Mandate Technoeconomic and Lifecycle Assessments: To establish commercial viability, rigorous technoeconomic analysis (TEA) and lifecycle assessment (LCA) must be integrated into research projects. This means quantifying whether the operational energy savings from a 15% COP improvement can justify the initial cost of graphene-based nanoparticles and the energy footprint of their synthesis over a 10-year system lifespan.

6.

Foster Innovation in Material Science for Inherent Stability: Foundational research into novel nanomaterials is critical to overcoming the core challenge of stability. This includes developing advanced materials like core-shell nanoparticles, where a highly conductive core (e.g., copper) is coated with a chemically inert shell (e.g., silica) to prevent oxidation and improve long-term dispersion in water–glycol mixtures without relying on potentially degradable surfactant additives.

By systematically addressing these strategic imperatives, the scientific community can guide the evolution of nanofluid technology from a promising concept into a validated, economically sound, and impactful solution for enhancing energy efficiency in thermal management systems worldwide.