Search Results (2,153)

Nanofluids have emerged as promising candidates for enhancing the dielectric and thermal performance of insulating liquids used in power transformers. While numerous studies report significant improvements in breakdown voltage (up to +10–40%) and thermal conductivity, the underlying mechanisms remain only partially understood and often contradictory, particularly with respect to long-term stability and ageing behavior. This paper presents a comprehensive and critical review of nanofluids applied to transformer insulation, adopting a system-level approach focused on the oil–paper insulation system. The analysis reveals that the reported performance strongly depends on key parameters such as nanoparticle concentration, dispersion quality, and experimental conditions, leading to significant inter-study variability. Dielectric improvements are shown to be maximized within narrow concentration ranges and may deteriorate due to nanoparticle aggregation, while thermal enhancements are often accompanied by increased viscosity, resulting in a thermo-hydraulic trade-off. Furthermore, this review highlights major contradictions in the literature, including the paradoxical relationship between electrical conductivity and dielectric strength, as well as the unclear impact of nanofluids on cellulose ageing. The findings demonstrate that performance observed at the fluid level cannot be directly extrapolated to real transformer conditions without considering the complex interactions between nanoparticles, oil, cellulose, and moisture. To address these limitations, a conceptual framework termed Nano-Modified Composite Insulation (NMCI) is proposed. This model provides a unified description of multiphase interactions and offers a basis for a more realistic evaluation of nanofluids under operational conditions. This work emphasizes the need for standardized experimental methodologies and long-term studies and provides clear research directions toward the development of reliable and industrially applicable nanofluid-based insulation systems. 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

Efficient thermal management of lithium-ion batteries is critical for the safety, performance, and longevity of electric vehicles. This work numerically investigates a battery thermal management system (BTMS) for a LiFePO₄ battery, featuring a liquid-cooling plate with novel droplet-shaped turbulators and coolant with different nanofluids. Computational Fluid Dynamics (CFD) simulations were employed to analyze the effects of cooling channel geometry, nanofluid type, nanoparticle volume fraction, coolant inlet velocity, and battery discharge rate on the system’s thermal performance and pressure drop. Results show that the droplet-shaped channel reduces the maximum battery temperature by 1.64 K compared to a conventional straight channel, owing to enhanced turbulence and larger heat-transfer area. Among different coolants, the 6% Cu–water nanofluid demonstrated the highest cooling effectiveness due to its superior thermal conductivity. To balance competing objectives, a multi-objective optimization using Response Surface Methodology (RSM) and the Non-dominated Sorting Genetic Algorithm II (NSGA-II) was performed. The optimal design was achieved with a coolant velocity of 0.097 m/s and a volume fraction of Cu nanoparticle of 3.85%, which maintained the maximum battery temperature of 299.7 K with a minimal pressure drop of 26.27 Pa at a 1.03 C discharge rate. These findings highlight that a BTMS combining droplet-shaped turbulators with a Cu–water nanofluid provides a highly effective and energy-efficient thermal management strategy. Full article

This work numerically reveals a novel strategy to modulate two ionic conductance state in a nanochannel via pressure-dependent fluidic motion inside the channel. Steady and transient simulations based on Poisson–Nernst–Planck–Stokes equations demonstrate that the two states with distinct ionic conductance and ion selectivity can be reversibly switched by external pressure, with a characteristic time of ~100 μs. Furthermore, the two conductance states are found to depend on the transversal electric field, which gives rise to two distinct intrachannel fluidic flow patterns, namely laminar flow and vortex flow, respectively. This finding suggests the potential of pressure-controlled ionic conductance switching for applications in nanofluidic ionic circuits, flow-regulated sensing, and integrated micro/nanoscale devices. It also provides insights into nonlinear ionic current–voltage behaviors. 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

The study addresses a selected issue in industrial cooling, that is, how to transport heat more efficiently when the process involves fiber spinning and extrusion, in which conventional fluids usually cannot work. We considered a ternary nanofluid that passed around a porous stretching cylinder and particularly considered the synergistic effect of quadratic thermal buoyancy, and the thermally generated double-diffusive heat and solute (TGDHS) effect. Through the Casson fluid model and considering the magnetic fields, radiations, and nonlinear chemical reactions, we reduced complex PDEs to simple ODEs. The results were evident using the BVP4C numerical method. Although in reality, magnetic fields and thermal radiation become a retarding force, the quadratic thermal buoyancy is the driving force behind accelerating the flow. An important trade-off that we discovered is that a heavier Casson fluid reduces heat and mass transfer. The addition of Nimonic 80A, AA7072, and AA7075 nanoparticles to ethylene glycol consistently enhances heat transfer, outperforming the base fluid by 7.8% even at low concentrations. While AA7072 and AA7075 drive significant increases of over 16%, Nimonic 80A offers a much more marginal contribution of 1.23%. Consequently, the Nusselt number is far more sensitive to the concentration of the aluminum alloys than to the Nimonic 80A. Finally, this work demonstrates that the most significant parameter in intensifying convective heat and mass transfer in such industrial systems is the strong forces of buoyancy. Full article

Nanofluid flooding technology has demonstrated enormous potential in enhancing the recovery efficiency of unconventional oil and gas resources. However, due to the complex physicochemical properties of nanofluids and their intricate interaction mechanisms in different reservoir environments, the research and application of nanofluids still face numerous challenges. Although existing review articles have systematically covered various aspects of nanofluid flooding technology and its enhanced oil recovery (EOR) mechanisms, they have not comprehensively addressed all facets of nanofluid-based EOR. In particular, they lack detailed introductions to the field applications of nanofluid flooding technology in reservoirs with different geological structural characteristics, the preparation of bio-based nano-oil displacement materials, the technology of forming nanofluids through in situ self-assembly of silica nanoparticles by reservoir microorganisms, and nanomaterial-mediated carbon dioxide flooding and microbial flooding technologies. This paper aims to identify the existing deficiencies in current nanofluid EOR technologies, especially focusing on the green and low-carbon microbial composite nanofluid flooding technology based on the utilization of reservoir microbial resources. Furthermore, targeted future development directions are proposed, with the goal of providing a more comprehensive, in-depth, and forward-looking reference for the theoretical research and industrial application of nanofluid EOR technologies, thereby further promoting the advancement of EOR technologies for low-permeability and tight oil reservoirs. 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

In this article, we present a new approximate solution for blood nanofluid having gold nanoparticles as it flows near a stretching porous cylinder in fractal space. A Casson non-Newtonian magneto-bio-nanofluid flowing through a porous medium is considered a potential application in chemotherapy for eradicating cancer cells. Without the need for the nonperturbative approach, the proposed solution uses an alternative approach to dealing with nonlinear problems. This approach transforms the nonlinear cubic jerk model resulting from the simplification of the governing fractional partial differential equations into an equivalent linear formula. This approach is known as highly efficient linear approximation (HELA) or non-perturbation technique (NPT), and this represents a significant advancement over traditional perturbation methods in the analysis of non-linear systems. As a robust mathematical approach, it excels at handling a wide range of coefficient values, particularly in cases of clear nonlinearity. This study also utilized the masking technique simultaneously with HELA, which played a crucial role, as they simplify the complex dynamics of the system, making it more amenable to analysis. The numerical solution by the Runge–Kutta fourth-order (RK-4) method integrated with a shooting technique compared favorably with graphs drawn for the analytical solution from the proposed strategy HELA. The current results show that an increase in the fractal factors enhances the resistance to fluid motion, leading to a suppression of the velocity field. Physically, this often relates to the complexity of the medium or the fractal nature of the transport process, where higher fractal dimensions or factors can lead to slower diffusion or flow rates, like the role of porous media. Therefore, the current study has significant implications in the promotion of nanotechnology fields in medicine, particularly the use of gold nanoparticles in chemotherapy for the eradication of cancerous cells. Full article

To achieve stable dispersion of ZnO nanoparticles in the base fluid and enhance thermal conductivity (λ), a PGPR@ZnO nano-hyperdispersant was synthesized using polyglycerol polyricinoleate (PGPR) and ZnO. FT-IR and DSC confirmed the bonding interaction between PGPR and ZnO, and zeta potential analysis verified the steric hindrance effect that effectively inhibits particle agglomeration. The PGPR@ZnO was dispersed into di(2-ethylhexyl) carbonate (DEHC) by ultrasonication and stirring, yielding a stable DEHC-PGPR@ZnO nanofluid. This nanofluid achieved a 16.2% increase in λ while retaining the low viscosity and low pour point of the base fluid. Stability assessments showed consistent particle size main peaks before and after static and dynamic tests, with no obvious agglomeration peaks, average particle size variation below 6%, PDI below 0.3, and negligible zeta potential fluctuation. Following static and dynamic stability tests, the thermal conductivity decreased by 0.85% and 7.98%, respectively. These results indicate excellent dispersion stability and provide a valuable reference for evaluating the operational adaptability of the coolant. The nanofluid meets the basic standards for immersion coolants and exhibits a figure of merit (FOM) superior to most oil-based coolants. Compared with PAO2, it offers advantages in raw material availability and resistance to hydrolysis and acidification, providing research and practical foundation for the development of high-performance immersion coolants. Full article

Carbon dioxide (CO₂) hydrate slurries have emerged as promising candidates for cold thermal energy storage (CTES) and refrigeration systems due to their high latent heat, controllable flow behavior, and environmentally friendly nature. These slurries are formed by dispersing solid CO₂ hydrate particles in a liquid phase, forming a multiphase system with tunable thermophysical and rheological properties. The performance of these slurries is dependent on nucleation kinetics, particle sizes and their distribution, solid content, and thermal transport characteristics under flow conditions. This review paper gives an assessment of CO₂ hydrate slurries from a thermofluid’ perspective by focusing on key aspects such as hydrate nucleation mechanisms, viscosity behavior, shear response, thermal conductivity, convective heat transfer, and slurry stability. Particular attention is given to the role of surfactants and nanoparticle additives that enhance hydrate formation and improve slurry performance. The addition of nanofluids is discussed both in terms of their effect on thermal properties as well as in flow stability. Full article

The escalating power density in Active Phased Array Radar has made the thermal management of Transmitter and Receiver (T/R) modules a critical bottleneck for radar performance. To address the thermal resistance of traditional cold plates, this study investigates an innovative embedded cooling strategy utilizing micro-pyramid arrays and advanced nanofluids. Thermal performance was evaluated using maximum temperature, maximum temperature difference and surface temperature standard deviation (ST). Higher pyramid density markedly enhances temperature uniformity, an effect that scales positively with the power load. Under a 100 W condition, the 8-circle micro-pyramids configuration (the densest structure with roughness Ra = 1.3) achieved a 22.58 K reduction in maximum temperature and a 22.5% improvement in temperature uniformity compared to the 2-circle structure, and outperformed the 4-circle structure by 16.98 K and 17.9%, respectively. Furthermore, a comparative analysis of nanofluids (Al₂O₃, CuO, graphene, and h-BN) is conducted and it is found that graphene nanofluid exhibits the best overall heat transfer enhancement because of its high thermal conductivity and moderate reduction in specific heat capacity. The thermal performance of the nanofluid is evaluated by comparing the maximum temperatures of the heat source at the 8-circle structure. The synergistic coupling of graphene nanofluid with the 8-circle array yields a remarkable 35.38% enhancement in temperature uniformity at 100 W. The enhancement mechanisms are mainly attributed to intrinsic thermophysical properties of the nanoparticles and convection caused by denser pyramid array. The aforementioned findings provide important guidance for the thermal management design of antenna and other high-density integrated electronic systems with embedded cold plate design demand. Full article

Coal-bed gas well production is too low to realize a highly efficient exploitation of the #8 coal seam in the Shanxi formation in the Nalin region. Based on the reservoir characteristics, the designed poly-aromatic-grafted silicon-quantum-dot-based desorption agent (PQS) has been developed. Then, the adsorption/desorption behavior of CBM on the coal surface under the influence of this active chemical has been studied, and the synergy effect with an anionic–nonionic surfactant to desorption of CBM has also been discussed. The results show that the developed poly-aromatic-grafted silicon quantum dot, with a median size of 4.9 nm and +5.6 mV of zeta potential in neutral condition, has a significant emission peak with 470 nm at the excitation of 380 nm and 150,000 mg/L of salinity resistance, which also generates a strong adsorption capacity on the coal surface. A promoting effect to desorption of CBM for PQS nanofluid is exhibited and the Langmuir pressure is obviously increased. However, when the PQS nanofluid is synergized with an anionic–nonionic surfactant, the desorption of CBM is further improved and the wettability of the coal surface is altered from 78.2° to 84.2°. The desorption rate for this compound system reached 65.3%. It can be found that combining the quantum size, π–π stacking, π–π conjugation, and the synergy effect between PQS nanofluid and surfactant fluid with the traditional intermolecular force has a stronger capacity for promoting desorption of CBM than the conventional desorption agent. This study provides guidance for the molecular design of the desorption agent for deep coal rock and the application of silicon quantum dots. 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