Search Results (443)

Understanding gas–water two-phase flow mechanisms in deep tight gas reservoirs is critical for improving production performance and mitigating water invasion. However, the effects of pore-throat-fracture multiscale structures on gas–water flow remain inadequately understood, particularly under high-temperature and high-pressure conditions (HT/HP). In this study, we developed visualizable multiscale throat-pore and throat-pore-fracture physical nanofluidic chip models (feature sizes 500 nm–100 μm) parameterized with Keshen block geological data in the Tarim Basin. We then established an HT/HP nanofluidic platform (rated to 240 °C, 120 MPa; operated at 100 °C, 100 MPa) and, using optical microscopy, directly visualized spontaneous water imbibition and gas–water displacement in the throat-pore and throat-pore-fracture nanofluidic chips and quantified fluid saturation, front velocity, and threshold pressure gradients. The results revealed that the spontaneous imbibition process follows a three-stage evolution controlled by capillarity, gas compression, and pore-scale heterogeneity. Nanoscale throats and microscale pores exhibit good connectivity, facilitating rapid imbibition without significant scale-induced resistance. In contrast, 100 μm fractures create preferential flow paths, leading to enhanced micro-scale water locking and faster gas–water equilibrium. The matrix gas displacement threshold gradient remains below 0.3 MPa/cm, with the cross-scale Jamin effect—rather than capillarity—dominating displacement resistance. At higher pressure gradients (~1 MPa/cm), water is efficiently expelled to low saturations via nanoscale throat networks. This work provides an experimental platform for visualizing gas–water flow in multiscale porous media under ultra-high temperature and pressure conditions and offers mechanistic insights to guide gas injection strategies and water management in deep tight gas reservoirs. Full article

The drying of nanofluid films on a surface can form various patterns and plays an important role in painting, surface patterning, and nano-fabrication processes. In this paper, a two-dimensional Kinetic Monte Carlo (KMC) model is developed based on the two-dimensional Ising model to investigate the drying patterns of nanofluids in an open domain. In the KMC model, the effective chemical potential is approximated by a linear function, in contrast to the constant value used in previous studies. This ensures that the dewetting front in the open domain consistently recedes from the edges toward the center. Simulation results show that nanoparticles, initially uniformly distributed, can assemble into branched structures that remain on the substrate after complete evaporation of the nanofluid. Furthermore, the structures observed in our study differ from the fractal cavities investigated in previous studies conducted in closed domains. A parametric study reveals that both the particle diffusion rate and the chemical potential distribution significantly influence the resulting patterns. Full article

Achieving optimal lubrication during machining processes, particularly turning of stainless steel, remains a significant challenge due to dynamic variations in cutting conditions that affect tool life, surface quality, and environmental impact. Conventional Minimum Quantity Lubrication (MQL) systems provide fixed flow rates and often fail to adapt to changing process parameters, limiting their effectiveness under fluctuating thermal and mechanical loads. To address these limitations, this study proposes an ambient-aware adaptive Auto-Tuned MQL (ATM) system that intelligently controls both nanofluid concentration and lubricant flow rate in real time. The system employs embedded sensors to monitor cutting zone temperature, surface roughness, and ambient conditions, linked through a feedback-driven control algorithm designed to optimize lubrication delivery dynamically. A Taguchi L9 design was used for experimental validation on AISI 304 stainless steel turning, investigating feed rate, cutting speed, and nanofluid concentration. Results demonstrate that the ATM system substantially improves machining outcomes, reducing surface roughness by more than 50% and cutting force by approximately 20% compared to conventional MQL. Regression models achieved high predictive accuracy, with R-squared values exceeding 99%, and surface analyses confirmed reduced adhesion and wear under adaptive lubrication. The proposed system offers a robust approach to enhancing machining performance and sustainability through intelligent, real-time lubrication control. Full article

Background: The study of non-Newtonian fluids in thin channels is crucial for advancing technologies in microfluidic systems and targeted industrial coating processes. Nanofluids, which exhibit enhanced thermal properties, are of particular interest. This paper investigates the complex flow and heat transfer characteristics of a Sutterby nanofluid (SNF) within a thin channel, considering the combined effects of magnetohydrodynamics (MHD), Brownian motion, and bioconvection of microorganisms. Analyzing such systems is essential for optimizing design and performance in relevant engineering applications. Method: The governing non-linear partial differential equations (PDEs) for the flow, heat, concentration, and bioconvection are derived. Using lubrication theory and appropriate dimensionless variables, this system of PDEs is simplified into a more simplified system of ordinary differential equations (ODEs). The resulting nonlinear ODEs are solved numerically using the boundary value problem (BVP) Midrich method in Maple software to ensure accuracy. Furthermore, data for the Nusselt number, extracted from the numerical solutions, are used to train an artificial neural network (ANN) model based on the Levenberg–Marquardt algorithm. The performance and predictive capability of this ANN model are rigorously evaluated to confirm its robustness for capturing the system’s non-linear behavior. Results: The numerical solutions are analyzed to understand the variations in velocity, temperature, concentration, and microorganism profiles under the influence of various physical parameters. The results demonstrate that the non-Newtonian rheology of the Sutterby nanofluid is significantly influenced by Brownian motion, thermophoresis, bioconvection parameters, and magnetic field effects. The developed ANN model demonstrates strong predictive capability for the Nusselt number, validating its use for this complex system. These findings provide valuable insights for the design and optimization of microfluidic devices and specialized coating applications in industrial engineering. Full article

The fast and accurate calculation of the internal temperature rise in the oil-immersed transformer is the premise to realize the thermal health management and load energy evaluation of the in-service transformer. In view of the influence of nanofluids on the heat transfer process of transformer, a numerical simulation algorithm based on lattice Boltzmann method (LBM) and finite difference method (FDM) is proposed to study the heat and mass transfer process inside nano-modified oil-immersed transformer. Firstly, the D2Q9 lattice model is used to solve the fluid and thermal lattice Boltzmann equations inside the oil-immersed transformer at the mesoscopic scale, and the temperature field and velocity field are obtained by macroscopic transformation. Secondly, the electric field distribution inside the oil-immersed transformer is calculated by FDM. The viscous resistance in LBM analysis and the electric field force in FDM analysis, as well as the gravity and buoyancy of particles, are used to explore the motion characteristics of nanoparticles and metal particles. Finally, compared with the thermal ring method and the finite volume method (FVM), the relative error is less than 5%, which verifies the effectiveness of the numerical model and provides a method for studying the internal electrothermal convection of nano-modified oil-immersed transformers. Full article

This research offers valuable improvements in the efficiency and water yield of a parabolic dish concentrator (PDC) solar distillation system, contributing to more sustainable and effective renewable energy solutions. Three hybrid nanofluids were evaluated, and their performance was measured through experiments and simulations. The numerical model is within 5% agreement with the measurements. Daily distilled water production increases by 25.7% with hybrid nanofluids (from 4.50 L to 5.67 L). The average exergy efficiency is approximately 19%. Furthermore, an interpretable, rule-based AI controller optimized with the Coati algorithm was integrated; this controller suggested operating setpoints and revealed transparent decision thresholds. This work is the first systematic PDC study where three different hybrid nanofluids were examined and explainable artificial intelligence (XAI) was applied within a single framework. The results demonstrate that higher performance and more predictable operation are achievable for producing distilled water based on PDC. Full article

Nanotechnology has become a transformative field in modern science and engineering, offering innovative approaches to enhance conventional thermal and fluid systems. Heat and mass transfer phenomena, particularly fluid motion across various geometries, play a crucial role in industrial and engineering processes. The inclusion of nanoparticles in base fluids significantly improves thermal conductivity and enables advanced phase-change technologies. The current work examines Powell–Eyring nanofluid’s heat transmission properties on a stretched Riga plate, considering the effects of magnetic fields, porosity, Darcy–Forchheimer flow, thermal radiation, and activation energy. Using the proper similarity transformations, the pertinent governing boundary-layer equations are converted into a set of ordinary differential equations (ODEs), which are then solved using the boundary value problem fourth-order collocation (BVP4C) technique in the MATLAB program. Tables and graphs are used to display the outcomes. Due to their significance in the industrial domain, the Nusselt number and skin friction are also evaluated. The velocity of the nanofluid is shown to decline with a boost in the Hartmann number, porosity, and Darcy–Forchheimer parameter values. Moreover, its energy curves are increased by boosting the values of thermal radiation and the Biot number. A stronger Hartmann number M decelerates the flow (thickening the momentum boundary layer), whereas increasing the Riga forcing parameter Q can locally enhance the near-wall velocity due to wall-parallel Lorentz forcing. Visual comparisons and numerical simulations are used to validate the results, confirming the durability and reliability of the suggested approach. By using a systematic design technique that includes training, testing, and validation, the fluid dynamics problem is solved. The model’s performance and generalization across many circumstances are assessed. In this work, an artificial neural network (ANN) architecture comprising two hidden layers is employed. The model is trained with the Levenberg–Marquardt scheme on reliable numerical datasets, enabling enhanced prediction capability and computational efficiency. The ANN demonstrates exceptional accuracy, with regression coefficients $R\approx 1.0$ and the best validation mean squared errors of $8.52\times {10}^{-10}$, $7.91\times {10}^{-9}$, and $1.59\times {10}^{-8}$ for the Powell–Eyring, heat radiation, and thermophoresis models, respectively. The ANN-predicted velocity, temperature, and concentration profiles show good agreement with numerical findings, with only minor differences in insignificant areas, establishing the ANN as a credible surrogate for quick parametric assessment and refinement in magnetohydrodynamic (MHD) nanofluid heat transfer systems. Full article

The integration of nanofluids into solar collectors has gained increasing attention due to their potential to enhance heat transfer and support the transition toward low-carbon energy systems. However, a systematic understanding of their photothermal performance under the direct absorption mode remains lacking. This review addresses this gap by critically analyzing the role of nanofluids in solar energy harvesting, with a particular focus on the direct absorption mechanisms. Nanofluids enhance solar radiation absorption through improved light absorption by nanoparticles, surface plasmon resonance in metals, and enhanced heat conduction and scattering effects. The novelty of this work lies in its comparative evaluation of advanced nanofluids, including magnetic nanofluids, plasma nanofluids, and nanophase change slurries, highlighting their unique capabilities in flow manipulation, thermal storage, and optical energy capture. Future research directions are identified, such as the life cycle assessment (LCA) of nanofluids in solar systems, applications of hybrid nanofluids, development of predictive models for nanofluid properties, optimization of nanofluid performance, and integration of Direct Absorption Solar Collectors (DASCs). In addition, challenges related to the stability, production cost, and toxicity of nanofluids are critically analyzed and discussed for practical applications. This paper offers guidance for the design and application of high-performance nanofluids in next-generation solar energy systems. Full article

This review paper deals with Triply Periodic Minimal Surfaces (TPMS) and lattice structures as a new generation of heat exchangers. Especially, their manufacturing is becoming feasible with technological progress. While some intricate structures are fabricated, challenges persist concerning manufacturing limitations, cost-effectiveness, and performance under transient operating conditions. Studies reported that these complex geometries, such as diamond, gyroid, and hexagonal lattices, outperform traditional finned and porous materials in thermal management, particularly under forced and turbulent convection regimes. However, TPMS necessitates the optimization of geometric parameters such as cell size, porosity, and topology stretching. The complex geometries enhance uniform heat exchange and reduce thermal boundary layers. Moreover, the integration of high thermal conductivity materials (e.g., aluminum and silver) and advanced coolants (including nanofluids and ethylene glycol mixtures) further improves performance. However, the drawback of complex geometries, confirmed by both numerical and experimental investigations, is the critical trade-off between heat transfer performance and pressure drop. The potential of TPMS-based heatsinks transpires as a trend for next-generation thermal management systems, besides identifying key directions for future research, including design optimization, Multiphysics modeling, and practical implementation. Full article

This study investigates the impact of nanofluids (TiO₂, Fe₃O₄, Al₂O₃, and graphene) on thermoelectric power generation within a rectangular heat exchanger equipped with internal winglets. The integration of internal winglets in heat exchangers, alongside the use of nanofluids, is a recent strategy aimed at enhancing convective heat transfer. This numerical research analyzes fluid dynamics and temperature variations on both the cold and hot sides of the thermoelectric generator (TEG). Three different heat exchanger models are evaluated: the first model features a pair of winglets in both ducts; the second model only has winglets in the hot duct; and the third model does not include any winglets. The performance of the nanofluids is systematically compared with that of distilled water. The results show that the Al₂O₃ nanofluid produces the highest power output at 7.8461 watts, which is 1.5% greater than that of TiO₂ and 1.22% higher than distilled water. Moreover, using Al₂O₃ in a heat exchanger with winglets in both ducts results in a 5% increase in power generation compared to a configuration without winglets and a 2% improvement over a model that has winglets only in the hot duct. This enhancement can be attributed to an increased heat transfer area and improved fluid mixing, which together facilitate more effective heat transfer to TEG. Full article

Practical applications such as solar power energy systems, electronic cooling, and the convective drying of vented enclosures require continuous developments to enhance fluid and heat flow. Numerous studies have investigated the enhancement of heat transfer in L-formed vented cavities by inserting heat-generating components, filling the cavity with nanofluids, providing an inner rotating cylinder and a phase-change packed system, etc. Contemporary work has examined the thermal performance of L-shaped porous vented enclosures, which can be augmented by using metal foam, using nanofluids as a saturated fluid, and increasing the wall surface area by corrugating the cavity’s heating wall. These features are not discussed in published articles, and their exploration can be considered a novelty point in this work. In this study, a vented cavity was occupied by a copper metal foam with $PPI=10$ and saturated with a copper–water nanofluid. The cavity walls were well insulated except for the left wall, which was kept at a hot isothermal temperature and was either non-corrugated or corrugated with rectangular waves. The Darcy–Brinkman–Forchheimer model and local thermal non-equilibrium models were adopted in momentum and energy-governing equations and solved numerically by utilizing commercial software. The influences of various effective parameters, including the Reynolds number ($20\le Re\le 1000$), the nanoparticle volume fraction ($0\%\le \phi \le 20\%$), the inflow and outflow vent aspect ratios ($0.1\le D/H\le 0.4$), the rectangular wave corrugation number ($N=5$ and $N=10$), and the corrugation dimension ratio ($CR=1$ and $CR=0.5$) were determined. The results indicate that the flow field and heat transfer were affected mainly by variations in $Re$, $D/H$, and $\phi$ for a non-corrugated left wall; they were additionally influenced by $N$ and $CR$ when the wall was corrugated. The fluid- and solid-phase temperatures of the metal foam increased with an increase in $Re$ and $D/H$. The fluid-phase Nusselt number near the hot left sidewall increased with an increase in $\phi$ by $\left(25–60\right)\%$, while the solid-phase Nusselt number decreased by $\left(10–30\right)\%$, and these numbers rose by around $3.5$ times when the Reynolds number increased from $20$ to $1000$. For the corrugated hot wall, the Nusselt numbers of the two metal foam phases increased with an increase in $Re$ and decreased with an increase in $D/H$, $CR$, or $N$ by $10\%$, $19\%$, and $37\%$. The original aspect of this study is its use of a thermal, non-equilibrium, nanofluid-saturated metal foam in a corrugated L-shaped vented cavity. We aimed to investigate the thermal performance of this system in order to reinforce the viability of applying this material in thermal engineering systems. Full article

We investigated the effects of chemical and physical changes on the interplay between the Néel and Brown superspin relaxation mechanisms in ferrofluids containing 18 nm-diameter Co_0.2Fe_2.8O₄ magnetic nanoparticles. We attempted to tune the ferrofluid’s magnetization dynamics via three methods: (i) changing the carrier fluid from Isopar M to kerosene (ii) doubling the Co-doping level from x = 0.2 to x = 0.4, and (iii) diluting the Co_0.2Fe_2.8O₄/Isopar M nanomagnetic fluid from δ = 1 mg/mL to δ = 0.1 mg/mL. We used temperature-resolved ac-susceptibility measurements at different frequencies, χ″ vs. T|_f, to gain insight into the thermally driven superspin dynamics of the nanoparticles within the ferrofluid. Our data demonstrates that both increasing x and using a different carrier fluid quantitatively alter the temperature dependence of the Néel and Brown relaxation frequency (f_N vs. T and f_B vs. T) by changing the nanoparticles’ magnetic moments and the fluid’s viscosity. Yet, the two mechanisms remain decoupled, as indicated by the presence of two magnetic events (peaks in the χ″ vs. T|_f datasets) one corresponding to the Néel and the other to Brown relaxation. On the other hand, diluting the ferrofluid leads to a qualitative change in the collective superspin dynamics behavior. Indeed, there is just one χ″-peak in the data from the δ = 0.1 mg/mL nanofluid, and its f vs. T dependence is well-described by a model that includes coupled contributions from both the Néel and Brown relaxation: $\mathrm{f}\left(\mathrm{T}\right)=\mathrm{p}·{\displaystyle \frac{T}{{\gamma }_0}}·\mathrm{e}\mathrm{x}\mathrm{p}\left[-{\displaystyle \frac{{\mathrm{E}}^{\prime }}{{\mathrm{k}}_{\mathrm{B}}\left(\mathrm{T}-{\mathrm{T}}_0^{\prime }\right)}}\right]+$ (1 − p) ${\mathrm{f}}_0\mathrm{e}\mathrm{x}\mathrm{p}\left[-{\displaystyle \frac{{\mathrm{E}}_{\mathrm{B}}}{{\mathrm{k}}_{\mathrm{B}}\left(\mathrm{T}-{\mathrm{T}}_0\right)}}\right]$. This is a remarkable behavior that demonstrates the ability to control a ferrofluids magnetization dynamics through simple chemical and physical changes. Full article

The main goal of this study is to create a computational solver that analyzes the effects of magnetohydrodynamics (MHD) on heat radiation in Cu–water-based Prandtl nanofluid flow using artificial neural networks. Copper nanoparticles are utilized to boost the water-based fluid’s thermal effect. This study primarily focuses on heat transfer over a horizontal sheet, exploring different scenarios by varying key factors such as the magnetic field and thermal radiation properties. The mathematical model is formulated using partial differential equations (PDEs), which are then transformed into a corresponding set of ordinary differential equations (ODEs) through appropriate similarity transformations. The bvp4c solver is then used to simulate the numerical behavior. The effects of relevant parameters on the temperature, velocity, skin friction, and local Nusselt number profiles are examined. It is discovered that the parameters of the Prandtl fluid have a considerable impact. The local skin friction and the local Nusselt number are improved by increasing these parameters. The dataset is split into 70% training, 15% validation, and 15% testing. The ANN model successfully predicts skin friction and Nusselt number profiles, showing good agreement with numerical simulations. This hybrid framework offers a robust predictive approach for heat management systems in industrial applications. This study provides important insights for researchers and engineers aiming to comprehend flow characteristics and their behavior and to develop accurate predictive models. Full article

This work investigates the intricate dynamics of the Carreau hybrid nanofluid’s inclined magnetohydrodynamic (MHD) flow, exploring both active and passive control modes. The study incorporates critical factors, including Arrhenius activation energy across a stretched sheet, chemical interactions, and nonlinear thermal radiation. The formulation of the boundary conditions and governing equations is inherently influenced by symmetric considerations in the physical geometry and flow assumptions. Such symmetry-inspired modeling facilitates dimensional reduction and numerical tractability. The analysis employs realistic boundary conditions, including convective heat transfer and control of nanoparticle concentration, which are solved numerically using MATLAB’s bvp5c solver. Findings indicate that an increase in activation energy results in a steeper concentration boundary layer under active control, while it flattens in passive scenarios. An increase in the Biot number ($Bi$) and relaxation parameter ($\mathsf{\Gamma }$) enhances heat transfer and thermal response, leading to a rise in temperature distribution in both cases. Additionally, the 3D surface plot illustrates elevation variations from the surface at low inclination angles, narrowing as the angle increases. The Nusselt number demonstrates a contrasting trend, with thermal boundary layer thickness increasing with higher radiation parameters. A graphical illustration of the average values of skin friction, Nusselt number, and Sherwood number for both active and passive scenarios highlights the impact of each case. Under active control, the Brownian motion’s effect diminishes, whereas it intensifies in passive control. Passive techniques, such as zero-flux conditions, offer effective and low-maintenance solutions for systems without external regulation, while active controls, like wall heating and setting a nanoparticle concentration, maximize heat and mass transfer in shear-thinning Carreau fluids. Full article

Nanofluids are an innovative working medium in solar hot water installations (DHWs), thanks to their increased thermal conductivity and heat transfer coefficient. The aim of this work was to assess the effect of Al₂O₃ nanofluids in a water–ethylene glycol base (40:60%) and with the addition of Tween 80 surfactant (0.2 wt%) on thermal efficiency (ε) and exergy (η_ex) in a plate heat exchanger at DHW flows of 3 and 12 L/min. The numerical NTU–ε model was used with dynamic updating of thermophysical properties of nanofluids and the solution of the ODE system using the ode45 method, and the validation was carried out against the literature data. The results showed that the nanofluids achieved ε ≈ 0.85 (vs. ε ≈ 0.87 for the base fluid) and η_ex ≈ 0.72 (vs. η_ex ≈ 0.74), with higher entropy generation. The addition of Tween 80 reduced the viscosity by about 10–15%, resulting in a slight increase of Re and h-factor; however, the impact on ε and η_ex was marginal. The environmental analysis with an annual demand of Q = 3000 kWh/year and an emission factor of 0.2 kg CO₂/kWh showed that for ε < 0.87 the nanofluids increased the emissions by ≈16 kg CO₂/year, while at ε ≈ 0.92, a reduction of ≈5% was possible. This paper highlights the need to optimize nanofluid viscosity and exchanger geometry to maximize energy and environmental benefits. Nowadays, due to the growing problems of global warming, the analysis of energy efficiency and carbon footprint related to the functioning of a building seems to be crucial. Full article