Applications In Energy Systems Research Articles

Methanol Electrochemical Upgrading to Formate for Energy Applications.

The electrochemical oxidation of methanol to formate (MTF) has emerged as a promising route for sustainable chemical production and energy storage. Despite its potential, the development of efficient MTF systems faces significant challenges, including insufficient mechanistic understanding and suboptimal catalyst design. This review highlights recent advances in MTF electrocatalysis, focusing on three key aspects: 1) reaction mechanisms at molecular level, 2) rational catalyst design strategies, and 3) practical applications in energy systems. Critical factors governing catalytic performance, including active site engineering, intermediate stabilization, and reaction pathway modulation are systematically analyzed. The integration of MTF with hydrogen production and carbon utilization technologies is also discussed as a potential approach for sustainable energy cycles. Finally, current limitations are identified in product selectivity and system efficiency, while proposing future research directions to advance this field. This work provides valuable insights for developing next-generation electrocatalysts and optimized MTF processes.

Comprehensive review of artificial intelligence applications in renewable energy systems: current implementations and emerging trends

As the world faces pressing climate and energy challenges, Artificial Intelligence is proven as a transformative force in advancing renewable energy systems. This study reviews the current and future applications of Artificial Intelligence in renewable energy, highlighting its transformative role in enhancing the efficiency, reliability, and scalability of renewable energy systems. The study draws from over 400 recent publications, selected based on their relevance to Artificial Intelligence and renewable energy systems. We discuss the use of Artificial Intelligence techniques including machine learning, deep learning, and reinforcement learning models for optimizing energy production, forecasting demand, predictive maintenance, and managing decentralized energy systems. Emerging fields such as quantum machine learning and Artificial Intelligence-augmented reality are also considered because of their potential to transform energy infrastructures. The survey reviews significant innovations in wind and solar energy, energy storage, and smart grid technologies, focusing on how Artificial Intelligence addresses challenges like intermittency and variability. Furthermore, we discuss the importance of big data, the Internet of Things, and real-time analytics in advancing Artificial Intelligence models, along with the evolving landscape of Artificial Intelligence-driven policy and market modeling for renewable energy adoption. Real-world case studies, like Google’s collaboration with DeepMind for optimizing wind energy output and Australia’s National Electricity Market integrating Artificial Intelligence for grid stability, underscore the practical impact of Artificial Intelligence in renewable energy. This paper highlights challenges that are hindering Artificial Intelligence adoption in renewable energy systems and offers recommendations for improving the available technology to maximize Artificial Intelligence’s potential in promoting sustainable energy and addressing climate change.

Solvent Engineering and Metal-Doping Dual Effects Enable Morphology Precise Controlled MOF-based Electrocatalysts for Highly Efficient Oxygen Evolution Reaction.

Designing economically viable electrocatalysts with superior activity for the oxygen evolution reaction (OER) represents a critical challenge in advancing practical water electrolysis systems for renewable hydrogen generation. In this work, CoFe-MOF(W) with a layered structure is created through synergistic modulation of a dual-regulation mechanism combining solvent engineering with metal doping, exhibiting superior electrocatalytic performance, requiring merely 276mV overpotential to reach a current density of 10mA cm-2 while demonstrating fast kinetics with a 55mV dec-1 Tafel slope. The experimental results indicated that the solvent engineering facilitated the inducing unsaturated coordination states that tailored morphology and exposed more active sites, meanwhile, Fe-doping modulated the electronic structure of Co sites while introducing multimetal synergy for enhanced charge transfer, resulting in superior OER performance. Further mechanistic studies revealed that CoFe-MOF(W) underwent surface reconstruction, generating Co(Fe)OOH is the true OER active species. Moreover, the density-functional theory (DFT) calculations confirmed that Fe doping optimized *OH adsorption free energy, thus enhancing the OER kinetics. This work elucidates a new insight into solvent modulation and metal doping strategies for MOF-based electrocatalysts to achieve efficient OER performance, which is potentially promising for applications in sustainable energy systems.

Salt-Tuned Mechanical Properties of Hydrogels: An O:H-O Bond Perspective.

Hydrogels are versatile functional materials with applications in biomedical devices, sensors, and energy systems. However, tailoring their mechanical properties remains a critical challenge for expanding their practical utility. Here, the mechanical properties of poly(vinyl alcohol) (PVA) hydrogels prepared via the freeze-thaw method were effectively tailored through the incorporation of various chloride salts. This study reveals the molecular mechanisms by which salt ions influence the water structure, polymer chain interactions, and freezing behavior to modulate hydrogel performance. Using Raman spectroscopy, X-ray diffraction (XRD), and Fourier-transform infrared spectroscopy (FTIR), we elucidate how cation-specific effects alter the crystallinity, cross-linking density, and hydrogen bond network in the hydrogels. Hydrogels containing K+ exhibited superior rigidity and toughness due to enhanced crystallinity and optimized water-polymer interactions, while hydrogels of Mg2+ and Ca2+ had much lower modulus and adhesion strength since the divalent ions reduced freezing points resulting in reduced polymer aggregation. These findings provide a molecular-level understanding of the salting effects on hydrogel properties, offering new insights into designing materials with tunable mechanical performance for diverse applications.

Influences of Variable Thermal Conductivity, Viscous Dissipation, and Cattaneo–Christov Heat and Mass Fluxes on MHD Tangent Hyperbolic Ternary Hybrid Nanofluid Flow Over a Stretching Sheet

ABSTRACTThis study focuses on the heat and mass transfer characteristics of magnetohydrodynamic tangent hyperbolic ternary hybrid nanofluid flow over a stretching plate, incorporating complex factors such as variable thermal conductivity, Joule heating, viscous dissipation, chemical reactions, Darcy–Forchheimer flow, and Cattaneo–Christov heat and mass fluxes, alongside nonlinear thermal radiation. The research employs ternary hybrid nanofluids composed of copper, silver, and aluminum oxide nanoparticles suspended in ethylene glycol, which are selected for their superior thermal conductivity and potential to enhance heat transfer efficiency. These nanofluids are of significant importance for applications in energy systems, heat exchangers, aerospace, and electronic cooling, where efficient thermal management is crucial. The study systematically analyzes how key physical factors including viscous dissipation, Joule heating, thermal and concentration relaxation times, the Darcy–Forchheimer effect, and nanoparticle volume fraction affect the flow and thermal behaviors of the fluid. The governing partial differential equations are transformed into ordinary differential equations using a similarity variable and are solved numerically with the sixth‐order Runge–Kutta (RK6) method in MATLAB. Results are benchmarked against previous literature to verify accuracy. The findings reveal that an increase in the magnetic field, porosity, Forchheimer number, and nanoparticle volume fraction reduces the flow velocity. Conversely, the temperature distributions are enhanced by the magnetic field, Eckert number, variable thermal conductivity, and higher nanoparticle concentration. Additionally, the concentration profile decreases with higher concentration relaxation time and chemical reaction rate. Notably, the Nusselt number increases with nanoparticle volume fraction and thermal relaxation time, significantly improving heat transfer efficiency. The results demonstrate that ternary hybrid nanofluids can significantly enhance heat and mass transfer, with promising applications in industrial cooling, renewable energy, and biomedical systems, while providing valuable insights for advancing energy and environmental technologies.

Speed Control of PMSM Motor by FOC Method Using MATLAB & Simulink

Field-oriented control is a vector control method that allows for accurate and independent control of flux and torque in permanent magnet synchronous motors. By transforming stator currents into a rotating reference frame aligned with the rotor flux, FOC decouples torque and flux control, allowing for independent adjustment of both. This results in high-performance operation with a fast dynamic response, high efficiency, and a wide speed range. FOC finds widespread application in various industries, including automotive, robotics, and renewable energy systems. Field-Oriented Control (FOC) constitutes a highly effective method for controlling permanent magnet synchronous motors. It allows independent control of the motor’s torque and flux, making it highly efficient and responsive. Keywords: PMSM Motor, FOC Method, Park transformation, Inverse Park transformation, Clarke transformation, Clarke-Park transformation, MATLAB & Simulink

Accurate SRT-BGK model evaluation of heatlines visualization and entropy generation of convective heat transfer inside an inclined U cavity receiver as application of solar thermal energy systems

The present study analyzes thermal and dynamic fields, heatlines visualization and entropy generation as well as flow energy, flow rate and heat transfer through a natural convection flow inside a top-open cavity receiver. For the case of a horizontal cavity, lower walls are heated at a uniform temperature while vertical walls are treated as adiabatic. The lattice Boltzmann method (LBM) is applied to solve governing equations of the problem. Effects of Rayleigh number (103 ≤ Ra ≤ 105), cavity orientation (0° ≤ θ ≤ 75) and cavity aspect ratio (1 ≤ A ≤ 1.75) on thermo-fluid characteristics of the flow are performed. It was found that current findings computed by LBM are in line with existing literature. Findings reveal that flow patterns and heat transfer are strongly affected by variations of Ra, θ and A. The rise of Ra leads to a change in the orientation of heatlines trajectories with a growth of the stratification degree of entropy generation within the horizontal square cavity. Additionally, an enhancement of the convective heat transfer is detected as increasing Ra accompanied with more energy absorbed by the flow and an intensification of the entrainment phenomenon of fresh air by thermal plumes. For Ra = 5×104, the optimization of heat transfer and total entropy generation demonstrate the existence of a critical angle of the square cavity receiver corresponding to the cavity orientation of θ = 45°.Increasingthe angle θ reduces the stratification degree of heatlines and entropy generation as well as the flow rate. The rise of the geometrical parameter A entrains an increase of thermal gradients with a deceleration of the flow circulation. A decrease of flow rate and convective heat transfer with the growth of the aspect ratio of a horizontal cavity is detected for Ra = 5×104.

Neural network-based prediction interval estimation with large width penalization for renewable energy forecasting and system applications

Neural network-based prediction interval estimation with large width penalization for renewable energy forecasting and system applications

Integrated Biomimetics: Natural Innovations for Urban Design, Smart Technologies, and Human Health

Biomimetics has emerged as a transformative interdisciplinary approach that harnesses nature’s evolutionary strategies to develop sustainable solutions across diverse fields. This study explores its integrative role in shaping smart cities, advancing artificial intelligence and robotics, innovating biomedical applications, and enhancing computational design tools. By analysing the evolution of biomimetic principles and their technological impact, this work highlights how nature-inspired solutions contribute to energy efficiency, adaptive urban planning, bioengineered materials, and intelligent systems. Furthermore, this paper discusses future perspectives on biomimetics-driven innovations, emphasising their potential to foster resilience, efficiency, and sustainability in rapidly evolving technological landscapes. Particular attention is given to neuromorphic hardware, a biologically inspired computing paradigm that mimics neural processing through spike-based communication and analogue architectures. Key components such as memristors and neuromorphic processors enable adaptive, low-power, task-specific computation, with wide-ranging applications in robotics, AI, healthcare, and renewable energy systems. Furthermore, this paper analyses how self-organising cities, conceptualised as complex adaptive systems, embody biomimetic traits such as resilience, decentralised optimisation, and autonomous resource management.

Multifunctional Silicon Surfaces with Enhanced Mechanical Resistance

Structuring silicon (Si) surfaces at the micro‐ and nanoscale is an effective strategy to achieve broadband antireflective behavior, essential for photonic and energy applications. Herein, scalable and low‐cost fabrication techniques including self‐assembly, metal dewetting, and nonchlorine dry etching are used to produce micro‐ and nanostructured Si surfaces with enhanced antireflective properties. Total reflectivity is reduced to below 4% for microstructures and down to 2% for nanostructures, resulting in a pronounced black silicon effect. These optical improvements are validated by finite‐difference time‐domain simulations, which closely match the experimental results. Despite the improved optical performance, surface structuring leads to increased fragility. To address this, a 200 nm conformal Al2O3 coating is applied via atomic layer deposition. Nanoscratch testing reveals significant mechanical reinforcement: critical load values tripled for microstructured surfaces and quadrupled for nanostructures. Notably, embedding the nanostructures beneath the coating preserves the low reflectivity while further enhancing durability. This multifunctional design approach enables the fabrication of surfaces that are both optically efficient and mechanically robust. Moreover, the fabrication techniques are compatible with large‐area processing, offering a promising pathway for industrial‐scale applications in advanced optical and energy systems.

Characterization of Resin‐Infused Nonwoven Natural Fiber‐Reinforced Epoxy Composites for Renewable Energy Structural Applications: Comparative Experimental and Finite Element Analysis

ABSTRACTNatural resource‐based composites are biodegradable, eco‐friendly, and sustainable; however, variations in their properties and performance quality present significant challenges. This study presents the fabrication and performance evaluation of nonwoven natural fiber epoxy composites intended for structural applications in renewable energy systems. The objective is to assess the mechanical and thermal behavior of resin‐infused nonwoven coir, palm, and kenaf fiber laminates, produced via vacuum bagging infusion technique, as sustainable alternatives to synthetic composites. Mechanical characterization included tensile, flexural, impact, and hardness testing, while thermal behavior was evaluated through thermogravimetric analysis. Finite Element Analysis (FEA) was employed to predict stress distribution, and Analysis of Variance (ANOVA) was used to determine the statistical significance of material and treatment effects. All treated nonwoven composites exhibited enhanced performance compared to untreated counterparts. Treated palm fiber composites achieved the highest impact energy absorption (5.90 J), while treated kenaf composites recorded superior tensile strength (56.12 MPa), flexural strength (76.70 MPa), and hardness (114 HRB), showing improvements of 16.9%, 36%, and 12.5%, respectively over untreated counterparts. Thermal onset degradation for treated kenaf composites reached 342°C. ANOVA results confirmed that fiber type and treatment had statistically significant effects on mechanical performance (p ≤ 0.05). FEA predictions closely matched experimental trends, validating model accuracy. These findings support the suitability of bio‐based composites for lightweight, high‐performance and thermally stable components in renewable energy infrastructure such as wind turbine blades and solar panel frames.

Re-purposing Wind Turbine Blades

The decommissioning of wind turbine blades poses an escalating environmental and waste management challenge, as the composite materials found in these blades are difficult to recycle and often end up in landfills. With the projected growth in retired wind turbine blades, the waste burden is expected to reach 40–60 million tons by 2050. Traditional disposal methods, like mechanical and thermochemical recycling, are costly and result in low-value materials. Repurposing presents a sustainable alternative; however, matching decommissioned blades with appropriate industrial applications requires an innovative governance approach. This study leverages insights from innovation governance research and proposes a conceptual model for reusing decommissioned blades, focusing on bridging the gap between potential suppliers and users in various industries. By examining applications in construction, agriculture, and renewable energy systems, the study illustrates feasible repurposing strategies and explores the regulatory mechanisms needed to facilitate effective matching. Through expert interviews and case studies, the paper identifies barriers, such as logistical constraints and material degradation, and advocates for intermediate and third-party governance forms to support this marketization process. The findings highlight the importance of systematic matching mechanisms and regulatory frameworks in addressing the wind turbine blade disposal issue, underscoring the need for collaborative efforts in shaping sustainable solutions for end-of-life turbine materials.

Grid Integration of Single‐Phase Inverters Using a Robust PLL‐Less Control Strategy for Renewable Energy Applications

ABSTRACTIn this paper, a PLL‐less control technique for single‐phase grid‐connected voltage source converter (VSC) system is proposed that overcomes shortcomings in traditional PLL‐based and existing PLL‐less techniques. The proposed method avoids the use of a PLL and thus significantly decreases the computational complexity of the system as well as enhance system response dynamics for weak grid conditions connected to renewable energy sources. Using the proposed current control technique, the method ensures precise power injection and synchronisation with the grid voltage. Unlike conventional methods that require multiple controllers and complex transformations, the proposed method uses a single proportional‐integral (PI) controller for implementation simplicity, along with better dynamic performance. The main contribution of this study is to use an αβ–dq transformation for reference generation without PLL dependency, enabling robust synchronisation and high‐quality power injection. Performance evaluation results demonstrate the method's satisfactory response time, low total harmonic distortion (THD) and compliance with IEEE power quality standards under varying load and grid conditions. The case and comparative analysis highlight the proposed control method's ability to maintain stability and efficiency, outperforming traditional PLL‐based and other PLL‐less techniques. The study highlights the potential of the PLL‐less approach for applications in renewable energy systems and provides a simplified, reliable and cost‐effective alternative for grid integration. This controller design contributes to the advancement of grid‐tied inverter technology, leading to more efficient use in renewable energy applications for the future.

Degradation Prediction of Proton Exchange Membrane Fuel Cell Based on Multi-Head Attention Neural Network and Transformer Model

Proton exchange membrane fuel cells are a clean energy technology with wide application in transportation and stationary energy systems. Due to the problem of voltage degradation under long-term dynamic loads, predicting their performance degradation trend is of great significance for extending the life of proton exchange membrane fuel cells and improving system reliability. This study adopts a data-driven approach to construct a degradation prediction model. In view of the problem of many input parameters and complex distribution of degradation features, a neural network model based on a multi-head attention mechanism and class token is first proposed to analyze the impact of different operating parameters on the output voltage prediction. The importance of each input variable is quantified by the attention weight matrix to assist feature screening. Subsequently, a prediction model is constructed based on Transformer to characterize the voltage degradation trend of fuel cells under dynamic conditions. The experimental results show that the root mean square error and mean absolute error of the model in the test phase are 0.008954 and 0.006590, showing strong prediction performance. Based on the importance evaluation provided by the first model, 11 key parameters were selected as inputs. After this input simplification, the model still maintained a prediction accuracy comparable to that of the full-feature model. This result verifies the effectiveness of the feature screening strategy and demonstrates its contribution to improved generalization and robustness.

Thermal Propagation Effects of Hydromagnetic Micropolar Nanofluid Flow over an Expanding Stagnation-point Surface

The current study investigates the transient stagnation-point flow and heat transfer characteristics of a magneto-micropolar nanofluid over an expanding surface under the influence of thermal radiation and boundary slip. The developed model incorporates the effects of micropolarity with weak concentration, external magnetic fields, and microscopic nanoparticles for enhanced thermal conductivity. The governing partial differential equations are formulated using boundary layer assumptions and transformed into a system of nonlinear ordinary differential equations through appropriate similarity transformations. These equations are solved numerically via shooting and Runge-Kutta Felberg scheme to analyze the effects of key parameters, with results presented in graphical and tabular forms. The findings reveal that increasing the magnetic parameter suppresses the velocity field while enhancing the thermal boundary layer thickness and temperature distribution. Additionally, thermal radiation significantly elevates the fluid temperature, indicating potential applications in thermal management and various energy systems. The results of this study contribute to the optimization of nanofluid-based technologies in transient, high-temperature environments.

Computational analysis for thermo-diffusion applications of concentrated tri-hybrid nanoparticles with nonlinear radiated effects: the non-classical thermal model

The tri-hybrid nanofluids are an engineered class of nanomaterials, possessing effective heat transfer properties, promoting new applications in advanced energy systems, solar thermal collectors and heat exchangers. Due to such high-level and inspired uses of tri-hybrid concentrated nanofluids, the purpose of the present analysis is to provide biomedical applications of magnetized tri-nanoparticles under exposure to human blood. Tri-hybrid nanofluid thermal behaviour is supported using three various nanoparticles: aluminium dioxide (Al2O3), titanium oxide (TiO2), and silicon oxide (SiO2) with blood base material. The applications of thermo-diffusion effects are also observed. The problem of flow is considering the improved thermal theories. Thermal outcomes are also evidenced by the nonlinear-radiated model and applications of heat generation. Realistic convective thermal boundary conditions for thermal inspection simulation. After having the governing model represented by nonlinear differential equations, numerical computation is carried out by a shooting scheme. It has been noticed that the thermal impact of tri-hybrid nanofluid is more prominent and progressive. The heat transfer is enhanced due to the Biot number and Dufour constant. Furthermore, the increasing significance of the Soret number is detected for the concentration profile. The results assert that tri-nanofluid exhibits more eminent thermal outcomes, due to the synergistic characteristics of the three nanoparticles.

Voltage-lift technique in non-isolated boost DC-DC converters: An enhanced topology for high voltage gain applications

Abstract This paper introduces a novel non-isolated boost DC-DC converter employing the voltage-lift technique to address limitations of conventional designs. The proposed topology, comprising two switches, two inductors, three capacitors, and three diodes, achieves high voltage gain, reduces current ripple, and minimizes component stress while maintaining simplicity and cost-effectiveness. Detailed analyses in continuous current mode (CCM) explore the voltage gain characteristics and identify the conditions for stable operation. Switch current stress is examined, and critical inductance requirements are determined. A notable feature of the converter is that the voltage across the main switch remains lower than the output voltage, enhancing its operational efficiency. Validation through PSCAD/EMTDC simulations and experimental results confirm an efficiency of 96%. These findings highlight the converter’s suitability for high-performance applications in renewable energy systems, electric vehicles, and other advanced power conversion scenarios.

An Overview of the Impact of Vibrations on Li‐Ion Battery Performance, Degradation, Battery Thermal Management System and Key Focus Areas

ABSTRACTLithium‐ion batteries (LIBs) have gained significant attention in recent years due to their widespread applications in electric vehicles, portable electronics, energy storage, and renewable energy systems. However, their increasing use raises concerns about safety, reliability, and performance under various operating conditions. Among these, the impact of vibrations encountered during transportation, operation in vehicles, and industrial environments on battery performance, state of charge (SoC) and other critical parameters is of particular importance. This study is an overview that focuses on understanding the effects of vibrations on Li‐ion batteries (especially cylindrical, pouch, and prismatic cells) through a combination of experimental testing and simulation modeling results. The experimental results highlight the influence of vibration‐induced stress on electrical performance and battery degradation behavior. Simulations complement these findings by providing insights into the mechanical and electrochemical responses, effect on battery thermal management systems under different vibration frequencies and amplitudes. By addressing these effects comprehensively, this overview aims to contribute to the design of more robust Li‐ion battery systems capable of withstanding dynamic environments. The experimental studies show that discharge capacities were consistently decreasing depending on the cycles, frequencies, and the amplitudes of vibrations; this could be attributed to the separator and graphite anode material degradation. Also, the simulation results showed that the battery temperature management systems will be more effective under vibration conditions.

Fuel Cells and its efficiency based on different materials and their configuration in Automobile Industry for Enhanced Efficiency, and Sustainability

This study provides a review of Fuel cells and its materials combined applications in automotive industry. The research focuses on analyzing the modified proton exchange membrane cell design featuring two valves. The results showed several improvements over the traditional single-inlet/single-outlet designs. Firstly, the dual-inlet system allowed for more uniform and smooth distribution of hydrogen and oxygen gases inside the cell, resulting in the reduction of concentration losses and improving electrochemical reaction rates, inside the cell. This configuration enhanced water and heat removal, which helped in maintain optimal operating temperatures and conditions, resulting in minimized risk of flooding in the membrane. This configuration resulted in a noticeable increase in overall cell efficiency, with improved power output under both steady and dynamic load conditions. Secondly, the study focused on impact of using advanced, corrosion-resistant materials for bipolar plates and membranes. These materials can show better thermal stability and chemical stability, inside the cell, mainly under high humidity and temperature environments. The modified fuel cell showed a gradual increase in operational lifespan of cell and reduced performance degradation over time. In general, the combination of both structural configuration and good material selection for cell proved effective, increasing both the efficiency and durability of the fuel cell system. These findings suggest that a dual-inlet/outlet design, with high-performance materials having good properties, are strong potential for future applications in automotive and energy systems.

Optimized entropy analysis of unsteady electromagnetic Powell–Eyring hybrid nanofluid flow over an inclined surface for solar water heating applications

Abstract Enhancing thermal and mass transport in solar water heating systems is critical for improving their operational efficiency and sustainability. This study presents a comprehensive numerical and statistical analysis of unsteady magnetohydrodynamic Powell–Eyring hybrid nanofluid flow over an inclined stretching sheet, targeting its application in solar thermal energy systems. The hybrid nanofluid consists of multiwall carbon nanotubes and silicon dioxide nanoparticles dispersed in a water base fluid, selected for their superior thermal conductivity and stability. The flow is modelled as two‐dimensional, incompressible, and subjected to electromagnetic effects, incorporating Soret and Dufour phenomena to capture coupled heat and mass transfer mechanisms. The governing nonlinear partial differential equations are solved using the finite element method, while multiple linear regression analysis is employed to quantify the sensitivity of unsteadiness, porosity, and magnetic field strength on system performance indicators. The results reveal that skin friction increases with higher unsteadiness but decreases with increasing porosity and magnetic field intensity. Heat transfer, characterized by the Nusselt number, is significantly enhanced in the presence of unsteadiness, Dufour number, and thermal radiation. Similarly, mass transfer, represented by the Sherwood number, is predominantly influenced by the Schmidt number, Soret number, and solutal Biot number. Quantitatively, the proposed hybrid nanofluid model demonstrates a 23.26% enhancement in heat transfer and a 17.64% improvement in mass transfer compared to the conventional single‐phase nanofluid. These findings establish the hybrid model as a promising candidate for optimizing solar water heating applications through improved thermofluidic performance.