Intelligent thermal management through nanofluids in porous media a review of AI-driven design and o

Abstract

Keywords: Nanofluids; porous media; heat transfer enhancement; machine learning; computational fluid dynamics; thermal management; unmanned aerial vehicles

1. Introduction

Nanofluids are base fluids in which nanoparticles (1–100 nm) are dispersed; they have improved heat-transfer properties in industrial and energy applications. Many of the studies indicate that performance of nanofluid is superior to the traditional fluids because of the higher thermal conductivity, an increased convective heat transfer and the energy efficiency [1-2].The thermal transport at the microscale is different because of the profound nanoparticle-fluid interactions[3].Enhancement in thermal conductivity is observed experimentally, computa-

tionally, and theoretically in the study of nanofluid optimization, and Al2O3, TiO2, and CuO nanoparticles are found to be optimal in performance. One-step and two-step synthesis processes have been reported as to ensure colloidal stability, which is important for industrial applications. Stability is enhanced by sonication and surfactant modification using SDS, SDBS, and CTAB to prevent aggregation of the nanoparticles. Besides, porous media structures maximize the contact areas between matrix materials and fluids, thus one characteristic of these materials is that they enhance heat transfers bending over theoretical considerations [4]. The models include

Corresponding Author E-mail Adress: suman.e11804@cumail.in *

This paper was recommended for publication in revised form by Editorin-Chief Ahmet Selim Dalkılıç Published by Yıldız Technical University, İstanbul, Türkiye This is an open access article under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

Darcy’s law, the Brinkman-Forchheimer equation, and LTE/LTNE modelling. Studies by Delgado have shown that integrating porous structures with nanofluids improves heat-transfer performance in thermal devices by enhancing conduction and convection. However, challenges include nanoparticle sedimentation, increased viscosity at high concentrations, and difficulties in scaling up. Nanofluids can be applied to solar collectors, electronic cooling systems, and combustors, but applications in UAV component cooling systems are on the rise because of their multifunctional heat-transfer properties in aerospace technology. Several thorough reviews have provided the basis for understanding these components separately. Seminal works on nanofluids have extensively investigated thermophysical properties, synthesis, and convective heat transfer. In the same way, the principles of heat and fluid flow in a porous medium, both at the Darcy scale and LTNE scales, have been well-characterized [4]. More recently, there has been a movement towards reviews that attempt to address the overlap between two fields: heat transfer of nanofluids and porous-media systems. Nevertheless, such works are mostly based on traditional numerical and experimental analyses. A critical review of this synergy, viewed through the transformative prism of AI and ML for designing and optimizing models and overcoming basic modelling bottlenecks, remains conspicuously absent. Although solar and electronics applications are usually mentioned, the urgent thermal management challenges posed by new high-power-density systems, i.e., aerospace and electric propulsion, have not been comprehensively reviewed in a unified way. This review synthesizes traditional research and cutting-edge artificial intelligence, going beyond standalone research on nanofluid properties or transport models. It uniquely examines the strong synergy between hybrid nanofluids, advanced immutable porous materials, and machine learning (ML) for predictive optimization and builds on earlier intelligent systems, such as fuzzy-logic expert systems applied to solar thermal collectors. One of its key novelties is its critical focus on combining ML methods, such as ANNs and SVMs, with CFD. This integration addresses the high cost and complexity of modelling dynamic flows in porous media. Finally, the review provides a systematic indication of performance improvements of up to 30% in industrial practice, coherently translating advances from theory to practice. The primary objective is to consolidate and comprehensively assess recent developments in nanofluid heat transfer in porous media. This is achieved by synthesizing experimental results, simulation outcomes, and theoretical models to construct a holistic view of the underlying mechanisms. The review also attempts to systematically analyse and critically examine important adoption issues, such as colloidal instability and settlement, and potential solutions, including the use of surfactants. Furthermore, it aims to elucidate the role of the hybrid nanofluid and its synergism with optimized porous structures. This work targets ML to predict systems behaviour and to pave the way for efficient multi-objective design optimisation with the aid of a CFD-ML hybrid for next-generation thermal systems. From this analysis, some important findings can be drawn. As such, the intelligent synergy

between nanofluids and porous media is responsible for the greatest improvement in heat transfer, which cannot be achieved by optimizing either component individually. Machine learning promises to be a gamechanger, providing non-intuitive conditions for optimality, that is, enabling a departure from the standard to which one is accustomed. Furthermore, the review may identify a key research gap, namely the need for standardized experimental validation under real-world conditions. Thus, the future of high-efficiency thermal management lies in the simultaneous development of stable hybrid nanofluids and the implementation of AI-based design models, while new applications in aerospace and UAV propulsion and cooling require focused studies on lightweight porous composites and flow stability. Although this review evaluates these challenges and opportunities within the high-energy, emerging thermal-management application of UAVs, the synthesized findings on AI-driven nanofluid-porous-media design can nevertheless be generalized to solar thermal engineering, electronic cooling, industrial heat-exchanger engineering, and other high-flux thermal-engineering scenarios. This gap is addressed in this review. The main novelty of it is tripartite. It goes beyond examining nanofluids or porous media at isolated heights to critically assess their synergistic heat-transfer enhancement. It specifically focuses on the role of ML- and AIbased schemes—especially ANNs and SVM combined with CFD— as transformative agents for eliminating modelling bottlenecks and enabling optimal design. It places this discussion in the context of an up-and-coming, high-stakes application—thermal management in UAVs— which poses the main challenges for dynamic stability, lighting design, and extreme heat fluxes. The main aims of the review are to summarize and evaluate recent advances in nanofluid heat transfer in porous media; to assess it both experimentally and through simulations and theory; to conduct a coherent analysis of the main barriers to adoption, such as colloidal instability; to elucidate the presence of hybrid nanofluids and optimized porous structures; and to show how AI/ML can predict system behaviour and allow it to perform multi-objective design optimization of next-generation thermal systems. This conceptual framework is represented in the flowchart in Figure 1. Thus, the flowchart is illustrated in Figure 1 as follows.

Figure 1. Conceptual framework of nanofluid-enhanced heat transfer in porous media systems with AI-driven optimization for UAV thermal management

Several authoritative reviews in the past five years have comprehensively mapped the landscape of nanofluids and their interaction with porous media. Mahian et al. [66] provided a foundational overview of modelling and simulation techniques for nanofluid flows, while Xu et al. [143] specifically reviewed heat conduction, convection, and phase change of nanofluids within porous structures, consolidating theoretical and experimental progress. More recently analysed hybrid nanofluids in thermal systems, and Riyadi et al. [26] surveyed the intersection of nanofluids and machine learning for heat exchangers. These studies have established a robust understanding of component properties and conventional analytical methods. However, a critical synthesis that places AI and ML as the central, enabling paradigm for the design and optimization of the synergistic nanofluid-porous media system remains absent. Furthermore, prior reviews often generalize applications; this work distinguishes itself by contextualizing the discussion within the rigorous demands of an emerging, high-stakes domain such as thermal management for high-performance aerospace systems, specifically UAVs. Thus, this review aims to bridge the gap between established nanofluid-porous media science and next-generation intelligent design frameworks, offering a forward-looking perspective on a critical technological frontier.

2. Enhancing heat transfer in porous media using nanofluids

Nanofluid technologies are having a major influence on shaping industrial energy systems with versatile applications, now making inroads into advanced aerospace areas like unmanned aerial vehicle (UAVpropulsion [[5]. Nanofluids are a development in the field of fluid dynamics, in which 1-100 nm nanoparticles are dispersed in standard fluids such as water, oil, and coolants. Nanoparticles impart distinctive thermal and fluidic properties to base fluids. Advances in nanotechnology have enabled the creation of nanofluids designed to optimize heat transfer across a wide range of technological applications, such as the thermal control of high-performance drone motors and propellers. Numerous research demonstrates an improvement in solar system performance, heat and mass transfer by nanofluids, as well as improving the thermal properties of materials [2-9]. Nanofluid research practice combines dynamic experimental design, post-processing and characterization, computational modelling, and optimization of critical parameters and their impacts [10-14]. Metal or metal-oxide nanoparticles, such as CuO, titanium dioxide, or aluminium oxide, are dispersed in a base fluid to create a nanofluid. The addition of nanoparticles improves the thermal conductivity of the base fluids drastically [15-17]. The addition of nanoparticles drastically changes the thermophysical properties of the base fluid, with thermal conductivity (k) increasing most significantly. This improvement depends on nanoparticle content (metallic > oxide), size (smaller tends to be better), form (e.g., high-aspect-ratio nanotubes), and volume fraction. This advantage, however, is accompanied by a significant rise in dynamic viscosity (μ), thereby increasing pumping power. In addition, the base fluid has a relatively high specific heat capacity (Cp), which is not necessarily preserved in the nanofluid because the Cp of solids is usually

lower and may reduce thermal storage capacity. Density (ρ) increases according to the simple rule of mixtures. The maximization of a nanofluid formulation can be obtained then with the maximization of the k enhancement whilst controlling the penalties in μ and Cp to the net system level gain [15-17, and 20]. Research shows that addition of nanoparticles dramatically boosts the thermal conductivity of water and propylene glycol [18-21]. The production of nanofluids is one step method and two-step method as main methods [22-25]. Illustrated in Figure 2. The one-step method involves synthesis and distribution of nanoparticles and the two-step method involved preparing nanoparticles prior to the ingredients being mixed with the base fluid [26]. Figure 2 illustrates the two major methods of nanofluid synthesis. The one-step technique generates and disperses nanoparticles in the base fluid simultaneously, usually through chemical precipitation or direct evaporation/condensation. This will reduce the likelihood of hard aggregation during storage, which normally impairs initial dispersion and stability. Nevertheless, it is less scalable, may leave residual chemicals, and provides limited control over final nanoparticle size. The more widely used two-step procedure is preferred for its scalability; in this technique, dry nano powders are prepared separately on a large scale and are then dispersed into the base fluid by mechanical agitation in the presence of a chemical surfactant. The main challenge in this case is that dry nanoparticles are prone to forming strong, irreversible agglomerates; therefore, dispersion of the nanoparticles is difficult to achieve, and the nanoparticles may eventually begin to sediment. It is the methodology that will introduce a structural trade-off between stability (one-step) and scalability/manufacturing cost (two-step) [22-26].

Figure 2. Composition and production methods of nanofluids (one-step and two-step)

Currently, two-step methods are the predominant processes for nanofluid preparation, but they are not stable enough for industrial use and perform poorly when systems are subjected to high-vibration environments such as drone propellers [27-28]. The stabilization of polypeptide-coated nanoparticles is attributed to the surface modification of nanoparticles, changing the properties of the surface through the presence of surfactants [29-30]. Surfactants shift nanoparticle surfaces, basically changing the hydrophobic character of the nanoparticle surface to a hydrophilic one; this change repels it more from motion, as depicted in Figure 3, and makes it more stable through surfactants [31-33]. Common surfactants for stabilizing the nanoparticles are Sodium dodecyl sulphate (SDS) [34-35], Sodium dodecyl benzenesulfonate (SDBS) [35-36], Oleic acid [2425], Cetyltrimethylammonium bromide (CTAB)[2], Polyvinylpyrrolidone (PVP)[37]. The stabilization strategy and the surfactant of choice are also dependent on the properties of the nanoparticle. Metal nanoparticles (e.g., Cu, Ag) are highly electrophilic and can be easily oxidized, so selecting appropriate their capping materials or certain surfactants that adhere firmly to their metal surface is necessary to keep particles apart. Metal oxide nanoparticles (e.g., Al2O3, TiO2) also frequently have surface hydroxyl groups, which may help in electrostatic stabilization of polar base fluids, such as water. Nanoparticles made of carbon (e.g. CNTs) are intrinsically hydrophobic and prone to stacking; they need to be dispersed with a surfactant containing aromatic rings (such as SDBS) or to have covalent functional groups attached to them to be stabilized. This behaviour that is dependent on materials requires customized methods of colloidal stability [29-33]. Research on heat transfer in porous media is of significant practical importance for industries that use heat exchangers, storage systems, geothermal systems, and drying systems. Passive thermal performance improvement in mechanical systems is generally achieved with the incorporation of porosities [38]. The theory informs the design of next-generation drone propellers, in which a lightweight porous metal foam can be incorporated into blade design. While colloidal stability in laboratory settings is well understood for SDS- and SDBS-type surfactants, the actual performance has scarcely been studied under the strain conditions encountered on a rotating drone propeller subjected to extreme centrifugal forces and vibrations. This merely represents a knowledge gap between laboratory synthesis and field application. These studies show that enhancement of the thermal conductivity of the system can be obtained by addition of porous materials, which change the fluid flow patterns [39-42]. Conductive nanoparticles are known to improve heating efficiency when combined with base fluids. The addition of conducting nanoparticles to a base fluid resulted in the formation of a nanofluid. These nanoparticles can be metallic (e.g., aluminium and copper), metal oxides, or carbon-based. The highest percentage use of nanoparticles incorporation to fluids is for fluid thermal conductivity improvement[43].For UAVs, such nano-thermodynamic loading of a porous propeller-blade spar by a circulating nanofluid has the potential to provide a revolutionary active cooling mechanism that can directly counter the aerodynamic and electromagnetic heating mechanisms that reduce performance and lifespan.

Figure 3. Nanoparticle stabilization mechanism using surfactants Although nanoparticles can enhance the performance of fluids, their incorporation may introduce problems. High concentrations of nanoparticles increase the fluid’s viscosity, which reduces heat transfer efficiency. Nanofluids are prepared by dispersing 10–100nm nanoparticles into base fluids at controlled volumes that provide a balance between thermal enhancement and manageable fluid properties. In general, nanoparticles have lengths of 10 to 100 nm, depending on the application for which they are intended. Diverse views on the mini processes have prompted different approaches to modelling and formulation. In some models, the motion of the two types of nanoparticles and the base fluid particles is disregarded. The pseudo two-phase approach differs from the homogeneous analysis by considering the transport of the nanoparticles, whose concentration is uneven throughout the flow domain due to transport, compared with the initial distribution. In this way, this approach is a non-homogeneous, pseudo-two-phase model that accounts for heat transfer behaviour affected by gravity, Brownian motion, and thermophoretic forces. For a rotating drone propeller, these forces are intensified by strong centrifugal acceleration, which can particularly promote particle migration and sedimentation. Therefore, two-phase modelling should be used to accurately map the drone’s long-term performance and to prevent clogging. The Brownian and thermophoresis forces have been established as the basic forces which determine the behaviour [43]. The two-phase model represents nanofluids as solid particles, base fluid, and their combination, and solves the conservation equations independently for each constituent. A nanofluid is made up of hybrid systems of multiple types of nanoparticles [40]. Mixing different nanoparticles in a base fluid increases thermal conductivity. For instance, when aluminium oxide is introduced into water, heat transfer increases. Another small quantity of Cu added to this mixture can improve it still further [41, 44]. This synergy is being investigated for UAVs, because

a hybrid CNT-CuO nanofluid can provide enhanced cooling performance while meeting lower density requirements to minimize influence on flight dynamics.

3. Nanofluid modelling in porous media

The average Nusselt number (Nuave) for Cu/water nanofluid in square cavity with varying concentration (0.1-0.7 wt%) increase the Ra but decrease the porosity as reported by Sheremet et al. [45], using the Tiwari and Das model. Such fundamental relationships between porosity and heat transfer are critically important for the design and performance of porous composite cores in drone propeller blades, which require an optimized pore structure to maximize cooling potential without compromising structural integrity. Using a model of thermal hydraulic flows of stress engineer Buongiorno applied to Al2O3/water nanofluid system (1.0-4.7 vol%) and horizontal plate showed that Nusselt number decreases with increase of buoyancy ratio, according to Zargartalebi et al. [46]. Hossain et al. [47] melt rate of nano-PCM (Cyclohexane + CuO, 10% vol.) is in the decreasing trend with increase in porosity values. Grosan et al. [48] investigated that Cu /water nanofluid heat transfer was studied in square cavities using extended Buongiorno model at different particle concentration. Ghalambaz et al. [49] stated that Cu/water nanofluids in a parallelogram cavity showed enhancement in heat transfer with nanoparticles. Sheremet et al. and Pop et al. investigated the influence of porosity enhancement on Nuave for Cu/water (2.5% vol.) flow around a horizontal cylinder. Table 1 summarizes key studies, revealing a critical contradiction while Sheremet et al. and Pop et al. [50] studied Cu/water (2.5 vol%) around a horizontal cylinder and showing that Nuave increases with porosity. Study found that in a triangular cavity, Nuave rises with Ra and number but decrease both because of Brownian motion, buoyancy ratio and thermophoresis [51]. These complex multi-force interactions are particularly relevant to the rotating, heated environment of a drone propeller, in which centrifugal forces are the dominant factor in the formation of a resultant force field that determines nanoparticle distribution and thermal performance. Rosmila et al. [52] found thermal stratification increases the heat transfer in Cu, Al₂O₃, and Ag nanofluids by an enhancement provided by magnetic fields. Ferdows et al. [53] proved that viscous ratio has an important effect on thickening of boundary layer on exponentially stretching sheet. Chamkha et al. reported an improvement of heat transfer in a square domain while conducting a study Cu/ water, Al₂O₃/water, and TiO₂/water jointly with the wall thickness and Ra [54]. Umavathi et al. [55] showed that the increased heat and mass transfer with Ra and with nanoparticle volume fraction on a horizontal plate. Ismael et al. [56] demonstrated in square channel that porous Cu/water nanofluid transfers heat via the layer permeability. The research puts that increase in transfer of heat for the thicker walls using CuO/water for triangular solid at low Ra values [57]. The results of heat transfer through permeable layers and the effect of wall thickness directly determine the design of a propeller’s internal spar and skin; these components must be thick enough to

accommodate a porous cooling channel, yet thin enough to remain aerodynamically efficient. Thus, the works described in Table 1 provide meaningful information on the behaviour of nanofluids in porous media. Commonly, heat transfer, as indicated by the Nusselt number, is highly sensitive to the Rayleigh number, porosity, and nanoparticle concentration. However, a critical contradiction arises concerning the role of porosity. Some authors have reported a reduction in Nusselt number as the porosity of the vascular region increases, whereas others have noted an increased Nusselt number [45, 50]. This difference makes it clear that the relation is not a general one. It is strongly influenced by certain system geometry, flow regime, and dominant heat-transfer mechanism. Furthermore, the current body of work is mainly numerical and based on steady-state models. A major drawback is the lack of consideration of the dynamic redistribution of particles under external forces. In practice, when dealing with nanoparticle migration in a rotating UAV propeller, centrifugal and Coriolis forces dominate. This critical behaviour cannot be predicted by existing models, resulting in a significant discrepancy between simulated and real-world performance.

4. Nanofluid methods to enhance heat transfer in porous

media Different methods of surface roughness modification, lengthy fin, vibration, and electric fields are few of the common passive and active approaches to heat transfer enhancement as expounded by Singh et al. [180,181]. This section focuses on the specific synergies and practices for using nanofluids in porous media that integrate fluid-property engineering with structural alteration.

5. Applications in nanofluids in porous media systems

Nanofluids have a wide range of applications. They have thermal conductivity, stability and heat transfer properties compared with regular fluids [133-136]. These properties are currently being exploited outside Earth systems to solve thermal issues in aerospace, especially for cooling high-performance drone propellers when traditional methods are inadequate. Several studies have been carried out on nanofluids in hybrid systems. They focus on enhancing the heat transfer and energy capturing ability of the solar collectors and heat exchangers as well as the hybrid photovoltaic/thermal systems using carbon and metal-based nanoparticles [132-138]. Using nan-transfer equipment is an effective approach to increase heat-transfer rates. It is therefore still a strong subject of study in many industrial applications [139-141]. Nanoparticles enhance heat transfer in porous media for industrial applications. Their use in heat exchangers, burners, reactors, furnaces, and dryers has caused much interest in research [142, 143]. This fundamental knowledge is directly pertinent to the design of next-generation UAV propulsion, in which the propeller blade is constructed from porous composite material and serves as the microscale heat exchanger itself, and is therefore comparable to a high-efficiency heat exchanger. Nanoparticles enhance the thermal conductivity of base fluids. Porous media enhance heat transfer between solids and liquids [144]. Research shows the effectiveness of heat transfer is dependent on an increased thermal conductivity in addition to the increment in surface area of contact between heat transfer [145]. For a drone propeller, such a principle means using a nanofluid-saturated porous metal foam in the blade spar, which provides both a large internal surface area for cooling and the structural strength to withstand centrifugal loads. It’s important to study the motion of fluids close to still regions which are called stagnation-point flow (SPF) [146]. Nanofluids exhibit natural stagnation point flow in a variety of other systems including sheets, cylinders, rotating discs, and stretched or contracted surfaces [147150]. Analysis of stagnation-point flow in drone propellers is critically important because the blade root, where it contacts the hub, is a primary stagnation region that is likely to experience excessive heat accumulation, which one must mitigate using internal nanofluid cooling. Nanofluids have been used as replacements for common refrigerants to cool electronic components, and many studies have been conducted. They have shown a great potential for managing heat transfer in microchannel heat sinks [151]. The success of microelectronics cooling provides a technological example of placing miniaturised, closed-loop nanofluid cooling systems within the confined internal geometry of a propeller blade. To produce a stable and uniform temperature profile, two artificial nozzles were installed, 2 mm apart, below the MCHS and 180° out of phase [26]. Research illustrates recent advancements in computational and machine-learning simulations of nanofluid flow and transport in porous media, including heat transfer, viscosity, and the velocity and temperature fields. Nanofluids are a great development in contemporary heat transfer [182]. It enhances thermal performance and efficiency, enabling in-

novations in areas such as electronics cooling, the automotive sector, and renewable energy. A future application of this technology is in the aerospace sector, where nanofluid-porous-media systems would enable lighter, more energetic, and more durable UAVs through revolutionary thermal management of propulsion machinery. Alizadeh et al. [125] was using a SVM in predicting the transport processes in hybrid nanofluids, which can save time and costs for Multiphysics systems. Aiming to find the flow characteristics of porous materials, Hayat et al. [127] performed homotopy analysis for modelling flow around extended sheet providing the information about flow and heat behaviours. Lastly, Abad et al. [128] utilized ANN and PSO were used to study the reactive nanofluid flow around a bluff-body. Taken together, these studies highlight the role of ML in the development of nanofluid-porous media systems, enabling efficiency optimization, a better mechanistic understanding of how these systems behave, and improved predictions for industrial applications such as energy systems and chemical engineering. These computational tools can the toolbox necessary for the design and optimization of the nanofluid-cooled porous propeller, enabling engineers to assess performance across a large design space prior to building the actual prototype and to evaluate performance under real flight conditions. In general, these works highlight the application of ML to the development of nanofluid-porous-media systems, which improve efficiency, enhance mechanistic understanding, and strengthen predictive capabilities for industrial processes such as energy systems and chemical engineering.

6. Recent developments in nanofluid-porous media research

The field of nanofluid-enhanced heat transfer in porous media has witnessed unprecedented advancements in recent years, marking a transformative period in which AI-driven methodologies have become central to the design of thermal systems. Table 5 illustrates the key developments, highlighting the evolution from conventional approaches to AI-integrated thermal management systems.

Table 5. Summary of recent developments in nanofluid-porous media research Research Focus

Frontiers in thermal science driven by artificial intelligence

Deep learning and physics-informed neural networks revolutionize intelligent thermal management systems; AI enables accurate prediction of nanofluid behavior in complex porous geometries with substantially less training data than conventional approaches

ANN-LMA achieved perfect regression scores (R² = 1.0) with absolute error range of 10⁻³ AI and machine learningto 10⁻⁶ for blood-integrated ternary nanofluid flow; demonstrates extraordinary accuracy driven CFD for ternary achievable with modern AI architectures and potential for biomedical applications nanofluids including targeted drug delivery Spring-structured fins combined with ZnO-CuO hybrid nanofluids achieved significant UAV radiator cooling with cooling performance improvement for MALE class UAVs; first experimental study nanofluids validating nanofluid cooling in actual UAV-scale radiators, marking critical transition from theoretical modelling to practical implementation Artificial neural networks with Bayesian regularization backpropagation successfully ANN modelling of hybrid analyzed hybrid nanofluid bioconvection under LTNE conditions; regression analysis nanofluids with LTNE and mean square error metrics validated model accuracy for predicting heat and mass effects transfer in complex porous systems

Comprehensive review of ML applications; identified 20-30% performance improvement potential through AI-driven optimization; established ML as transformative tool for thermal system design

Multi-objective optimization of nanofluidbased photovoltaic thermal systems

Achieved 15.7% improvement in overall efficiency using NSGA-II optimization; demonstrated effectiveness of evolutionary algorithms for complex thermal system design

Comprehensive comparative study on aerodynamic efficiency and structural integrity of UAV frames; established design guidelines for application-specific performance optimization directly relevant to integrating nanofluid cooling systems in nextgeneration UAV platforms.

Hybrid nanofluids in serpentine channels with D-shaped jaggedness

28.5% enhancement in Nusselt number with Al₂O₃-CuO/water hybrid nanofluids; geometric optimization critical for maximizing heat transfer performance

Colloidal stability of nanofluids under dynamic flight conditions

Identified critical stability thresholds for nanofluids under high-g forces and vibration; zeta potential > 30 mV required for stable operation in aerospace environments

Physics-informed neural 40% improvement in prediction accuracy compared to conventional ML models; PINNs networks for multiphysics incorporate governing equations directly into learning process, ensuring physical validity modeling

SVM-PSO hybrid models for hybrid nanofluid transport prediction

Achieved 99.2% accuracy in predicting Nusselt number with 80% computational cost reduction; demonstrates power of hybrid AI-optimization approaches

Local thermal nonequilibrium effects in nanofluid-saturated porous cavities

Quantified LTNE effects under various operating conditions; established that LTE assumption can lead to 15-25% errors in heat transfer predictions

First comprehensive model incorporating dynamic particle redistribution; essential for predicting long-term system reliability

Successfully predicted complex thermal-chemical interactions; demonstrated sensitivity of system performance to nanoparticle concentration variations

Magnetic field effects on hybrid nanofluids in porous enclosures

Demonstrated active control of heat transfer using variable magnetic fields; enables dynamic thermal management capabilities

Identified optimal nanoparticle combinations for maximum heat transfer with minimal pressure drop; critical for pumping power constrained systems

Comprehensive review of nanofluid heat transfer in porous media

Consolidated theoretical and experimental progress across multiple applications; established foundational understanding of nanofluid-porous media interactions

Recent developments in Table 5 highlight key trends in nanofluid–porous media thermal management, with artificial intelligence emerging as the most significant shift; Lei et al. [184] showed that PINNs and deep learning can accurately predict nanofluid behavior in complex porous geometries using less training data, addressing data scarcity and accelerating design. AI models now achieve very high accuracy, as Begum et al. [186] reported R² = 1.0 with minimal error using ANN-LMA, while Aoudia et al. [188] demonstrated effective prediction of heat and mass transfer in hybrid nanofluid systems under LTNE conditions using ANN-BRS, indicating that AI may replace CFD for certain problems and extend to biomedical uses like drug delivery. In aerospace, Topuz et al. [185] provided the first experimental validation of nanofluid cooling in UAV radiators using spring fins and ZnO-CuO nanofluids, confirming that geometry and nanofluid selection must be optimized together, while Dey et al. [187] established UAV frame design guidelines for integrating such systems without affecting performance. AI-driven optimization shows strong gains, with Riyadi et al. [26] reporting 20–30% improvement, Alizadeh et al. [125] achieving 99.2% accuracy with 80% lower computation using SVM-PSO, and Davis et al. [171] showing 40% higher accuracy with PINNs. Geometric and material optimization also enhances performance, as Ratul et al. [137] achieved 28.5% Nusselt number improvement and Nuhash et al. [138] improved efficiency by 15.7% using NSGA-II. Fundamental studies revealed key insights, with Alsabery et al. [79] showing LTNE assumptions can cause 15–25% errors, Baghsaz et al. [81] modelling nanoparticle redistribution, and Patel et al. [175] identifying stability thresholds above 30 mV zeta potential. Active thermal control is enabled by magnetic fields, as shown by Izadi et al. [83], which is useful for UAVs with varying loads. Building on earlier reviews like Xu et al. [143], recent work integrates AI, experiments, and multi-physics modelling, enabling advanced and efficient thermal systems for next-generation aerospace applications. Thus, the convergence of AI-driven methodologies, experimental validation, and multiphysics modelling documented in this section constitutes a paradigm shift in the thermal management of nanofluid-porous-media systems. Recent years have witnessed transformative advances, ranging from perfect-prediction AI models that achieve unprecedented accuracy to first-ever UAV-scale experimental validation and physics-informed neural networks that substan-

tially reduce training data requirements. These developments collectively address the long-standing simulation-to-application gap, while complementary research on UAV frame optimization and LTNE-aware neural network modelling provides the integration framework for next-generation aerospace thermal systems. As the field matures, the synergistic integration of stable hybrid nanofluids, optimized porous architectures, and AI-based design frameworks will enable thermal management solutions previously considered unattainable, particularly for high-performance UAV platforms operating under extreme conditions.

7. Conclusion and future scopes

This review synthesizes existing research on heat transfer using nanofluids in porous media, with particular emphasis on the paradigm shift toward integrating AI and its future implementation in advanced aerospace systems. The central conclusion is that the synergy between nanofluids and porous media provides a pathway for superior thermal management in high-performance systems; this principle is demonstrated by its potential application in advanced aerospace systems, such as UAV propulsion. The successful deployment of intelligent systems, both fuzzy-logic expert systems and current fuzzy-MCDM frameworks, provides a validated paradigm for delivering performance benefits, which we argue must now be extrapolated to the even more challenging arena of nanofluid-porous media systems in dynamic aerospace environments. Nanoparticles and hybrids are important contributors to the thermal conductivity of base fluids, and porous matrices provide a large surface area for heat exchange. Nonetheless, such synergy is moderated by the persistent problems such as colloidal instability, nanoparticle sedimentation, and a consequential rise in pumping energy due to high viscosity. The Novelty of this review is the critical examination of the paradigm shift caused by ML. Techniques like ANNs and SVMs were initially viewed as analytical tools but have now become central components of the design. Their combination with CFD has been proven capable of slashing computational expenses, discovering non-intuitive optimal designs, and accurately predicting complex multi-physics behaviour in difficult-to-model situations that traditional methods cannot address. This CFD-ML hybrid model is essential for exploring the vast design space of such systems. The practicality of this technology is explored through the discovery

of a high-stakes application, such as thermal management in UAV propellers. The multi-functional approach to the next-generation systems is embodied in the conceptual design of a propeller with a nanofluid-cooled porous metal foam core, which integrates thermal management, structural integrity, and aerodynamic efficiency into a single system. Evidence consolidated from the literature points to the fact that such integrated systems can achieve up to 30% improvement in heat transfer performance, which directly correlates with improved UAV reliability, power, and endurance. Finally, this review confirms that the future of high-performance thermal systems is not in incremental improvements but in a concurrent, holistic approach to their development. Such an approach should strategically combine the development of stable and advanced hybrid nanofluids with the implementation of AI-based design and optimization models and address challenging practical applications from the outset. Bridging the gap between theoretical promise and practical implementation in future work requires several core priorities, including the need for nanofluid formulations that ensure long-term colloidal stability under dynamic, real-world conditions such as high-g forces and vibration. Systematic exploration of novel hybrid nanoparticles and functionally graded porous matrices should be pursued to optimize the trade-off between thermal performance, pressure drop, and structural weight. The experimental validation and scalability research should be undertaken to transition the successful labscale studies into a more stable pilot-scale prototype. In addition, research must move beyond steady-state models to more complex, multi-physics and transient models that represent dynamic system behaviour, incorporate phenomena such as local thermal non-equilibrium, and increase the involvement of artificial intelligence not only as a potentially useful predictive tool but also as a generative tool for inverse design and real-time adaptive control. In high-potential applications such as UAV propulsion, special efforts are needed to surmount system-level integration problems, including development of lightweight seals, small-scale pumps, and novel additive manufacturing processes to produce complex internal cooling architectures within components such as propeller blades, and these efforts should include full life-cycle analysis.

Acknowledgements

The completion of this work was made possible solely by the dedication and effort of the authors. No funding was received for this work. Special thanks to Dr. Suman Debnath and Dr. Raj Kumar for their continuous guidance and expertise.

Author contributions

Kaustav Dey, Suman Debnath, and Raj Kumar wrote the main manuscript and reviewed the manuscript.

Declaration of conflicting interest There is no conflict of interest among the authors.

Data availability statement

No new data were generated or analysed in support of this review.

Ethics Declaration The authors declare that all procedures performed in this study involving human participants were in accordance with the ethical standards of Chandigarh University, Punjab, India. No animals were used in this research.

Consent to Participate Declaration Informed consent was obtained from all individual participants included in the study. No participants were under 18 years of age. Participants were informed of the study’s purpose, procedures, and benefits, and were assured that participation was voluntary and that they could withdraw at any time without penalty.

Copyright and permission statement The authors confirm that permission has been obtained from the respective copyright holders for all figures reproduced in this manuscript. The sources for all figures are original creations of the authors for this review article. No third-party copyrighted figures have been reused without explicit permission.

nm NP HNF PCM UAV LTE LTNE SPF MCHS CCHP Al₂O₃ TiO₂ CuO Cu Ag CNT

Mwcnt

Nanometre Nanoparticle Hybrid nanofluid Phase change material Unmanned aerial vehicle Local thermal equilibrium Local thermal non-equilibrium Stagnation-point flow Microchannel heat sink Combined cooling, heating, and power Aluminium oxide Titanium dioxide Copper oxide Copper Silver Carbon nanotube Multi-walled carbon nanotube Magnetite (iron oxide) Water Weight percent Volume percent

Sodium dodecyl sulphate Sodium dodecyl benzenesulfonate Cetyltrimethylammonium bromide Polyvinylpyrrolidone Computational fluid dynamics Nusselt number (average) Rayleigh number Reynolds number Darcy number Hartmann number

Lssvm

Prandtl number Gravitational acceleration (e.g., in high-g forces) Magnetohydrodynamics Artificial intelligence Machine learning Artificial neural network Support vector machine Least squares support vector machine

Anfis

Multilayer perceptron Particle swarm optimization Physics-informed neural network Response surface methodology Gaussian process regression Random forest K-nearest neighbours Block-based machine learning Synthetic jet Mean absolute error Coefficient of determination

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