Search Results (475)

There has been a rising trend for recovering waste heat, especially after the invention of new types of semiconductors. Among all available utilization options, thermoelectric generation (TEG) systems are promising for recovering waste heat. Thermoelectric devices are environment-friendly, operate silently, and are suitable for low- to high-power applications. This review paper presents a comprehensive study of TEGs, starting with the current problem, state of the art, advantages, disadvantages, generation and related principles, and applications, and covers different arrangements (individual and combined) and working fluids. Furthermore, this article systematically covered various experimental and numerical studies, including optimization, offering insights into heat exchanger configurations, working fluids, and performance parameters. Here, an effort is made to describe the contributions of individual/coupled TEG systems. As a coupled system, the individual TEG system is used with other systems like solar, distillation, solar pond, etc., for cogeneration and enhanced efficiency. The thermal/system parameters of individual/coupled systems are thoroughly discussed, and their impact on efficiency and power generation is illustrated. It was found that the design of the heat exchanger configuration varies from plate type to an efficient liquid-based electricity generation system in these TEG systems. The working fluid inside the fluid loop of a thermoelectric generation system varies from simple fluids to nanofluids. The current state of thermoelectric generation technology is facing challenges in module materials, equipment cost optimization, and commercialization. The progressive TEG generation capabilities have improved with recent advancements in these areas. The power densities are increasing from 0.5 to 1.2 W/cm${}^2$ in earlier standalone TEGs to 2.5–4.8 W/cm${}^2$ in recent optimized hybrid configurations, and overall system efficiencies are rising from an average of 5.2% (standalone) to 18.7% in coupled solar-TEG or waste heat recovery systems. The reported maximum ZT values are also improved from ∼1.2 to 2.1–2.8 in next-generation materials. Liquid-based heat exchangers in conjunction with nanofluids are the most efficient way to maximize temperature gradient coefficient (0.75–0.92) and minimize parasitic losses. While flexible, ionic, and hybrid next-generation material platforms are still in the early phases of development (TRL 3–5), liquid-based heat exchanger systems improved with nanofluids are closest to commercialization (Technology Readiness Level, TRL 6–8). Therefore, further research in these areas is required to mitigate these challenges. Finally, the recent developments in the thermoelectric generation field and future research direction are briefly discussed. Full article

Hybrid nanofluids combining thermally conductive nanoparticles with latent heat-storing nanocapsules have attracted growing interest for near-ambient liquid-based thermal management, yet direct comparisons between mono and hybrid phase-change-material-containing systems on a common experimental basis remain scarce. In this work, water-based mono Al₂O₃, mono polyurethane-nanoencapsulated n-nonadecane (PU-NEPCM), and Al₂O₃/PU-NEPCM hybrid nanofluids were prepared under identical surfactant, sonication, and dispersion conditions, and their thermal conductivity, dynamic viscosity, and Day-1 colloidal stability were characterized over 298–313 K at total volume fractions of 0.1, 0.3, and 0.5 vol.%, with the hybrids prepared at a 50:50 volumetric ratio. At 0.5 vol.% and 313 K, the hybrid (NFH3) exhibited the highest thermal conductivity enhancement (+8.27%), exceeding the corresponding mono Al₂O₃ and mono PU-NEPCM nanofluids by 4.6 and 5.2 percentage points, respectively, while maintaining a moderate viscosity penalty. The hybrid formulations also achieved |ζ| = 32–37 mV, exceeding the conventional electrostatic-stabilization threshold and outperforming both mono families. A two-factor analysis of variance (ANOVA) identified particle concentration as the dominant factor governing both properties (p < 0.001), with temperature becoming statistically significant only for the hybrid viscosity (p = 0.043). The synergy index varied between 0.85 and 1.43 across the tested conditions—reaching values of 1.20–1.43 for the lowest-loaded hybrid (NFH1)—while the performance index remained close to unity (0.97–1.01). These results identify low-loaded Al₂O₃/PU-NEPCM hybrid nanofluids as a balanced and stable candidate for near-ambient liquid-based thermal management applications. Full article

Energy consumption and resource utilization have become critical challenges in modern machining due to increasing manufacturing costs, stringent environmental regulations, and global carbon-reduction targets. While sustainable machining strategies such as dry machining, minimum quantity lubrication (MQL), and cryogenic cooling have been widely investigated, recent years have witnessed the rapid development of advanced assisted and hybrid machining processes aimed at further reducing energy demand and material waste. However, existing review studies largely focus on individual techniques or lubrication approaches, lacking a systematic perspective on the combined energy–resource saving mechanisms in advanced sustainable machining. This review presents a comprehensive and up-to-date analysis of energy consumption characteristics and resource-saving strategies in advanced sustainable machining processes. Particular attention is given to emerging and hybrid technologies, including ultrasonic-assisted machining, ultrasonic-assisted MQL, electrostatic MQL (eMQL), multi-nozzle MQL systems, nanofluid-based MQL, laser-assisted machining, vortex tube-assisted cooling, dry ice machining, and hybrid cryogenic–MQL strategies such as LN₂-MQL and CO₂-MQL. The review systematically discusses how these techniques influence energy flow, tool–workpiece interactions, lubrication efficiency, and thermal behavior during machining. Furthermore, this paper highlights the synergistic effects of combining multiple assistance methods, emphasizing their role in achieving simultaneous improvements in productivity, tool life, surface integrity, and sustainability performance. Energy-based metrics, resource efficiency indicators, and carbon emission considerations reported in the literature are critically evaluated to identify current limitations and inconsistencies. Finally, key research gaps and future directions are outlined, including the need for standardized sustainability assessment frameworks, data-driven energy optimization, and intelligent hybrid machining systems. This review aims to provide a valuable reference for researchers and practitioners seeking to design next-generation sustainable machining processes with enhanced energy efficiency and reduced environmental impact. Full article

Effective thermal management is crucial for the performance, thermal safety, and lifespan of lithium-ion batteries in electric vehicles (EVs). Thermal management strategies are essential for preventing overheating, thermal imbalance, and the associated risk of thermal runaway. Nanofluids are emerging and attracting considerable attention as potential coolants for high-power energy storage and electronics systems. This review updates and summarizes the most recent advances in nanofluid-based cooling strategies for battery thermal management systems (BTMSs) over the past five years, emphasizing their implications for battery thermal safety. Three main nanofluid-based cooling strategies have been evaluated in depth, including nanofluid-based indirect liquid cooling, nanoparticle-enhanced PCM cooling, and nanofluid-based heat pipe cooling. Various nanofluid formulations, including mono, hybrid, and ternary nanofluids, have been considered and evaluated for their heat dissipation under high charge/discharge and abuse-relevant conditions. Thermal and hydraulic performance characteristics, including maximum temperature, maximum temperature difference, and pressure drop, have been comprehensively evaluated for different nanofluid-based cooling strategies. The findings demonstrated that nanofluids significantly improved heat transfer rates and enhanced temperature control efficiency. In particular, hybrid and ternary nanofluids exhibit superior thermal performance and effectively suppress the escalation of safety-critical temperatures. Beyond summarizing cooling performance, this review further discusses the role of nanofluid-based cooling strategies as functional thermal-control layers within intelligent BTMS architectures. Particular attention is given to their compatibility with sensing networks, BMS-/VCU-level supervisory control, predictive thermal models, actuator responsiveness, fault-warning algorithms, and long-term reliability under realistic driving and fast charging conditions. Therefore, this review provides architecture-oriented insights for developing safe, energy-efficient, and control-ready BTMSs for next-generation high-power and connected EVs. Full article

A numerical investigation of forced convection heat transfer in a three-dimensional T-shaped bifurcating channel with an upstream rotating cylinder and a downstream vibrating wavy wall is presented. The working fluid is a ternary hybrid nanofluid (Fe₂O₃, CuO, MoS₂ in water) exhibiting Casson rheology under an inclined magnetic field. The novelty of this work lies in the first integrated configuration combining these simultaneous mechanical, magnetic, and non-Newtonian effects. Using COMSOL Multiphysics, 413 parametric combinations of Reynolds number, Hartmann number, Casson parameter, nanoparticle shape and volume fraction, magnetic field angle, cylinder rotation speed, wall amplitude (Am), and period were solved. Average Nusselt and Bejan numbers quantified heat transfer enhancement and thermodynamic irreversibility. To interpret the high-dimensional parameter space and to circumvent the prohibitive computational cost of additional 3D magnetohydrodynamics simulations, machine learning (XGBoost) models were developed to rank feature importance and provide fast, accurate surrogate predictions (R² > 0.99). Cylinder rotation dominates heat transfer, increasing the Nusselt number by over 980% (feature importance 0.42) with a modest entropy penalty. Nanoparticle volume fraction reduces the Nusselt number via viscous damping. Magnetic field parameters negligibly affect heat transfer but strongly influence entropy generation; a perpendicular field recovers up to 97% thermal efficiency at high Hartmann numbers. Full article

An artificial neural network (ANN) prediction model based on the Levenberg–Marquardt (LM) algorithm has been developed to predict the nonlinear heat and mass transfer characteristics of Cattaneo–Christov Carreau–Yasuda tri-hybrid nanofluid (CCHMF–THNF) flow over a porous stretching sheet. A mathematical model of the phenomenon was developed based on a number of elements, including the combined effect of magnetohydrodynamic forces, thermal and solutal relaxation and the influence of viscoelastic fluid behavior and is numerically analyzed utilizing MATLAB bvp4c software. A set of standard data was generated as a reference for developing the ANN-LM model with one hidden layer containing 10 neurons and log-sigmoid activation function, to achieve rapid predictions of velocity, temperature and concentration profiles from the identified data set. This study introduces a novel methodology to provide fast prediction capabilities for transport characteristics through integration of the ANN–LM model with the non-linear CCHMF-THNF model, producing computational savings by providing prediction accuracy of transport characteristics with MSE values on the order of $1.0\times {10}^{-10}$ using ANN–LM in place of repeated bvp4c solutions. Furthermore, the predictive capability of the developed ANN–LM framework may be beneficial in the areas of thermal management systems, polymer processing, energy transport applications, and magnetically controlled cooling technologies since they all share a need for fast access to transportation characteristic evaluation data. Full article

Solar water heating systems (SWHS) offer a sustainable solution for reducing reliance on conventional energy sources; however, their performance is often limited by insufficient heat transfer within the collector. This study presents a CFD-based numerical investigation on the optimization of finned thermal collectors in a solar water heating system using Al₂O₃/water hybrid nanofluid. The effects of nanoparticle volume fraction (1–3%), fin geometry (triangular and hexagonal), and mass flow rate (5–20 kg/h) on the thermal and heat transfer performance of the system were analyzed. Key performance indicators including absorber/PV temperature, outlet fluid temperature, convective heat transfer coefficient, thermal efficiency, and improved daily efficiency were evaluated under transient operating conditions. The results show that increasing Al₂O₃ concentration enhances heat transfer and thermal efficiency due to improved thermophysical properties of the working fluid. Fin geometry significantly influences thermal behavior, with hexagonal fins generally producing higher outlet temperatures and thermal efficiency of 65%, while triangular fins provide higher daily efficiency improvement under optimized conditions. The convective heat transfer coefficient increased with both nanoparticle concentration and flow rate, reaching peak values during mid-day hours corresponding to maximum solar input. The study confirms that combining optimized fin structures with Al₂O₃/water nanofluids provides an effective strategy for improving the thermal performance of solar water heating collectors, while CFD modelling offers a reliable approach for system design and performance prediction. Full article

Electromagnetic hydrodynamic (EMHD) mixed convective flow of tetra hybrid nanofluid (TeHNF) in a Darcy-Forchheimer porous medium in a vertical channel with thermal radiation is considered in the paper. The electric and magnetic fields are homogeneous, magnetic perpendicular to the walls of the channel, and electric perpendicular to the plane formed by the directions of the magnetic field and the basic current. The channel walls are impermeable, and they are at constant but different temperatures. The basic equations that describe this problem are ordinary nonlinear differential equations (ODEs), and they are transformed into dimensionless ODEs by introducing dimensionless quantities, which are analytically solved using the homotopy perturbation method (HPM). The relations for velocity and temperature distributions, Nusselt numbers and shear stresses on the channel walls were determined. These relations are functions of introduced physical parameters that characterize the observed problem. For TeHNF, where the base fluid is water and the nanoparticles are made of aluminum oxide, titanium dioxide, magnesium oxide and magnetite, a part of the obtained results is given. Velocity and temperature plots are presented in the form of graphs, and Nusselt numbers and shear stresses are presented in the form of tables. Based on the analysis of the obtained results, appropriate conclusions were drawn. It was concluded that an increase in the Hartmann number as well as an increase in the porosity factor decrease the fluid velocity and shear stress, and increase the fluid temperature and Nusselt numbers. Higher values of the Forchheimer factor and higher heat radiation correspond to lower fluid velocities, lower temperatures, lower values of shear stresses and Nusselt numbers. By increasing the value of the Grashof number, the velocity of the fluid increases, and so do the shear stresses. TeHNF shows advantages over simpler hybrid nanofluids and commercial fluids. Full article

Due to advancements in thermal engineering and nanotechnology, nanofluids—base fluids containing dispersed nanoparticles (1–100 nm)—have emerged as promising high-performance coolants. Their enhanced thermal properties make them attractive for application in hybrid-electric aircraft, which require efficient Thermal Management Systems (TMS) to dissipate significant heat loads. This study employs a dynamic TMS model to assess the influence of key nanofluid features, including nanoparticle type, volume fraction, particle diameter, and base fluid. Metal nanoparticles provided the greatest thermal improvement (up to 19%). Increasing concentration enhanced cooling efficiency, with 0.5%, 1%, and 2% volume fractions reducing mean temperature by 14%, 19%, and 24%, respectively. Smaller particles performed better, as 20 nm nanoparticles achieved a 21.3% temperature reduction compared to 17.5% for 60 nm. Water-based nanofluids exhibited the best overall thermal behaviour, although they remain unsuitable for aeronautical applications. Full article

Efficient thermal management is critical for maintaining the safety, durability, and performance of lithium-ion batteries used in electric vehicles (EVs). In this study, a comprehensive numerical investigation is conducted to evaluate the heat transfer characteristics of mono- and hybrid-nanofluids in a microchannel-cooled lithium-ion battery module. A three-dimensional computational model of a 5S7P battery module composed of cylindrical 21700 cells is developed. Battery heat generation during 3C high discharge rate operation is predicted using the Newman-Tiedemann-Gu-Kim (NTGK) electrochemical model, while coolant flow and heat transfer are simulated using the governing conservation equations for mass, momentum, and energy. The cooling system consists of six liquid-cooling plates with circular microchannels. The performance of water-glycol (50/50) coolant is compared with several mono nanofluids of Al₂O₃ and Cu, and hybrid nanofluids of Al₂O₃-Cu, Al₂O₃-MWCNT, Al₂O₃-Graphene, Cu-MWCNT, and Cu-Graphene across multiple coolant flow rates from 1–5 LPM. The results demonstrate that nanofluids significantly enhance convective heat transfer and reduce battery temperature compared with the conventional water-glycol coolant. Among the investigated coolants, the Al₂O₃-Cu hybrid nanofluid (0.45–0.45%) operating at 1 LPM achieves the best overall thermo-hydraulic performance with a performance evaluation criterion (PEC) of 1.065. Further analysis of nanoparticle composition ratios shows that a Cu-dominant hybrid mixture (Al₂O₃-Cu: 0.27–0.63%) slightly improves the PEC to 1.0657, indicating marginally superior cooling performance. The findings highlight the potential of hybrid nanofluids as advanced coolants for microchannel-based battery thermal management systems in EVs, particularly under moderate coolant flow conditions. Full article

Hybrid nanofluids possess exceptional thermal conductivity, but one of the major concerns with nanoparticles is agglomeration. While the usage of surfactants or dispersants can be used to mitigate this issue, numerical investigation and sensitivity analyses can be more affordable when attempting to optimize and design a thermal device. The consideration of thermal radiation with conductive and convective heat transfer and appropriate nanoparticles may provide a greater solution without compromising the efficacy of hybrid nanofluids. In the present work, the concept of magnetohydrodynamics (MHD) is used to examine the impact of thermal radiation on a stable, two-dimensional, incompressible hybrid fluid consisting of nanoparticles (MWNCT)${\mathrm{-Fe}}_3{\mathrm{O}}_4$ and water flowing over a vertical surface. The flow is governed by established equations of fluid dynamics, which use the Rosseland diffusion model to incorporate radiation effects. The implicit finite difference (IFD) was used to solve the mathematical equations. Sensitivity analyses were conducted as functions of volume fraction, radiation and magnetic variables. This study also examines the streamlines and isotherm lines with respect to the volume fraction, radiation parameter and magnetic parameter of the heat source. The results indicate that for a fixed radiation parameter, increasing the nanoparticle volume fraction by up to $20\%$ leads to a reduction of approximately $37\%$ in the skin friction coefficient, while the corresponding Nusselt number increases by nearly $50\%$. Furthermore, the introduction of a magnetic field parameter significantly suppresses wall shear stress and modifies the thermal boundary layer thickness, demonstrating the competing interaction between Lorentz-force-induced momentum damping and radiation-enhanced thermal diffusion. These quantified trends highlight the sensitivity of coupled momentum and heat transport to combined magnetic and radiative effects in hybrid nanofluid systems. Full article

This study introduces an efficient and accurate two-stage explicit computational scheme for solving partial differential equations (PDEs) containing first-order time derivatives. The suggested method is a modification of the classical Runge–Kutta scheme that introduces a new first-stage formulation. This minimizes numerical error with moderate step sizes while preserving the stability region of the classical method. Spatial discretization is performed using a sixth-order compact finite-difference scheme to obtain high-resolution solutions. The analysis of stability and convergence is strictly determined for both scalar and system forms of convection–diffusion-type equations. To illustrate the suitability of the method, a dimensionless mathematical model of the unsteady, incompressible, laminar flow of a Prandtl-type non-Newtonian nanofluid over a Riga plate is considered, accounting for viscous dissipation, thermophoresis, Brownian motion, and a magnetic field. Here, the Prandtl ternary nanofluid is defined as a non-Newtonian nanofluid that follows the Prandtl rheological model, and it exhibits three critical transport phenomena: heat conduction, viscous dissipation, and nanoparticle diffusion. Representative values of the Prandtl number $P_r=3$ and Reynolds number $R_e=5$ are used to perform the simulation, and other parameters, including but not limited to the Hartmann number $H_a$, Williamson number $W_e$, thermophoresis $N_t$ and Brownian motion $N_b$, are varied to evaluate the flow behavior. Moreover, an artificial neural network (ANN)-developed surrogate model is used to calculate the skin friction coefficient and the local Sherwood number, using five input parameters: the Reynolds number, Prandtl number, Schmidt number, Brownian motion parameter, and thermophoresis parameter. The governing partial differential equations yield high-fidelity numerical data used to train the surrogate model. The data is split into 80% for training, 10% for validation, and 10% for testing. The ANN is tested using regression analysis and error histograms, which demonstrate high accuracy and generalization capacity. Numerical simulation combined with AI-based prediction is a cost-efficient method for real-time estimation of complex non-Newtonian nanofluid systems. Full article

Extensive research and empirical evidence demonstrate that nanofluids enhance heat transfer efficiency in microchannels, but this improvement is often accompanied by increased pressure drop and particle clogging. This study aims to determine the optimal parameters for preparing stable nanofluids and to discuss the effects of different parameters on thermal and hydraulic performance. By analyzing the impact of varying ultrasonication time, particle concentration, particle size, surfactant type, and mixing ratios on stability, the most stable nanofluid was selected for evaluation of flow heat transfer and cost-effectiveness. Results indicate that a 1:1 mixed nanofluid of TiO₂ (20 nm)-SiO₂ (50 nm) exhibits optimal stability under conditions of 90 min ultrasonication, 0.20 vol% total particle concentration, and 0.15 wt% xanthan gum. At a Reynolds number of 550, this mixed nanofluid exhibits superior thermal performance. Compared with deionized water, its convective heat transfer coefficient and Nusselt number increase by 40.25% and 37.94%, respectively, while the pressure drop rises by only 17.18%. The performance evaluation criterion reaches 1.43, accompanied by a high cost–performance factor. These findings demonstrate that mixing large and small particles of TiO₂ and SiO₂ not only significantly enhances thermal performance but also positively impacts stability and hydraulic properties. A 90 min ultrasonic treatment time markedly improves stability and optimizes dynamic light scattering results. Full article