Water-based Fluids Research Articles

Intelligent neuron based interpretation of carreau trihybrid nanofluid model with streamline analysis: Configuration of distinct geometries

This current attempt develops a more efficient predictive model that can accurately simulate the behavior of Carreau trihybrid nanofluids in non-trivial configurations. This study interprets thermal behavior of Carreau trihybrid nanofluid model with streamline analysis considering the two geometries wedge and cone. Three nanoparticles are involved in base fluid (water) with physical effects of non-uniform heat sink source and nonlinear thermal radiation are assumed for heat transport and Lorentz forces are considered for velocity inspection. Furthermore, this study employs intelligent neural networks to interpret data for streamline and thermal transport analysis, focusing on the specific cases of wedge and cone geometries. Initial data fetched through bvp4c and further, obtained data trained through supervised neural scheme, Levenberg marquardt neural network (LM-NN) is applied and required predictions are made. Higher “Gc” indicates stronger solutal buoyancy forces, which promote upward fluid movement, thereby increasing the velocity gradient. With increasing (M), the velocity profile decreases. Fluid exhibits enhancing the velocity gradient with higher (n). Higher particle concentration enhances the fluid's viscosity and resistance.

On the thermal performance of a three-dimensional cross-ternary hybrid nanofluid over a wedge using a Bayesian regularization neural network approach

Abstract Significance Studying the flow of ternary nanofluids [Ag, Cu, MoS2] holds significant importance in both science and engineering. Ternary nanofluids are vital in advancing thermal management systems, heat exchangers, aerospace, and materials processing applications. Purpose This study investigates the ternary hybrid Carreau nanofluid numerically for thermal proficiency in the inclined magnetized environment. In this study, three distinct nanoparticles of [Ag, Cu, MoS2] and base fluid water over the wedge are used. The velocity of nanofluids is judged under the influence of an inclined magnetic field, and the thermal performance is scrutinized by incorporating the thermal radiation effect. Methodology The physical problem generates partial differential equations, which are transformed into ordinary differential equations (ODEs) through similarity variables. These ODEs are linearized into a system of ODEs and then passed under the bvp4c Matlab program to get the solution. This solution is again trained by an artificial neural network, and further results are obtained with both schemes and compared. Findings The most rapid heat transport analysis is found for ternary hybrid nanofluids compared to bi-hybrid nanofluids. The thermal radiation parameters and the magnetic environment augment the rate of heat transport.

A Liquid Well Barrier Element for Temporary Plug and Abandonment Operations: A Breakthrough Approach

Plug and abandonment (P&A) operations demand valuable time and resources for operational procedures and materials to establish the well barrier element. This study aims to investigate the application of a water-based fluid as a liquid well barrier element for temporary abandonment, based on estimates of its lifespan and the survival probabilities of downhole temperatures acquired through accelerated life tests. To achieve this, the water-based formulation was tested and exposed to 95, 110, 140, and 150 °C temperatures for time intervals ranging from 1 to 10 days. After the temperature exposure, the fluid properties were verified, and failure was detected by accounting for any deterioration in rheological parameters and/or a substantial increase in filtrate volume. A statistical analysis of the failure data was performed in RStudio 4.1.3 software using the Weibull Model, and the fluid average lifespans and survival probabilities were estimated for the P&A temperatures. The results obtained demonstrate that the degradation of the fluid was only observed for 140 and 150 °C temperatures. According to the results, the fluid is a promising alternative for temporary abandonment until 80 °C, with no need for monitoring once its lifetime expectation exceeds three years at this temperature. For downhole temperatures above 80 °C, the fluid is a possible alternative, however, the operation’s maximum time and monitoring requirements should consider reliability metrics for each temperature.

CMOS-compatible plasmonic magnetic field sensor: An alternative approach using ultra-compact MIM configuration

This paper introduces a novel magnetic field sensor (MFS) that utilizes a metal-insulator-metal (MIM) waveguide integrated with a resonator structure and incorporates water-based Fe3O4 magnetic fluid. The sensor uses titanium nitride (TiN) as the plasmonic material which offers numerous advantages over conventional noble plasmonic materials. The sensor takes advantage of the tunable optical properties of the magnetic fluid and TiN to detect changes in the external magnetic field and quantify the magnetic field strength which has been demonstrated using the Finite Element Method (FEM). Our proposed MFS exhibits a high sensitivity of 11.97 pm/Oe, a narrow-band full-width half maximum of 93.66 nm, and a resolution of 8.36 × 10−4 Oe. The sensor is also compatible with complementary metal oxide semiconductor (CMOS) fabrication techniques, which enables chip-scale integration and low-cost production. The sensor can be used for various applications in navigation, military, space, healthcare, and beyond.

Fracture propagation characteristics of water and CO2 fracturing in continental shale reservoirs

Exploring the adaptability of CO2 and water-based fracturing to shale oil reservoirs is important for efficiently developing shale oil reservoirs. This study conducted fracturing experiments and acoustic emission (AE) monitoring on the Jurassic continental shale. Based on high-precision computed tomography scanning technology, digital reconstruction analysis of fracture morphology was carried out to quantitatively evaluate the complexity of fractures and the stimulation reservoir volume (SRV). The results show that the fracturing ability of a single water-based fracturing fluid is limited. Low-viscosity fracturing fluid tends to activate thin layers and has limited fracture height. High-viscosity fracturing fluid tends to result in a wide and simple fracture. A combination injection of low-viscosity and high-viscosity water-based fracturing fluid can comprehensively utilize the advantages of low-viscosity and high-viscosity fracturing fluids, effectively improving the complexity of fractures. CO2 fracturing is adaptable to Jurassic shale. The breakdown pressure of the supercritical CO2 (SC-CO2) fracturing is low. Branch fractures form, and laminas activate during SC-CO2 fracturing due to its high diffusivity. Under high-temperature and high-pressure conditions, the aqueous solution formed by mixing CO2 with water can promote the formation of complex fractures. Compared with water-based fracturing fluid, the complexity of fractures and effective stimulation reservoir volume (ESRV) increased by 8.7% and 47.6%, respectively. There is a high correlation between SRV and ESRV, and the proportion of AE shear activity is also highly correlated with the complexity of fractures. The results are expected to provide better fracturing schemes and effectiveness for continental shale oil reservoirs.

Modeling drilling fluid density at high-pressure high-temperature conditions using advanced machine-learning techniques

The management of oil and gas wells under high-pressure high-temperature (HPHT) conditions is a significant challenge in the oil industry, leading to increased operational and maintenance costs. One method for calculating the density of drilling fluids involves the use of sensors placed at the bottom of the well, which measure the fluid density in real time. Despite their accuracy, these sensors are costly and perform suboptimal under HPHT conditions. In the present study, seven robust machine-learning models, namely generalized regression neural networks (GRNN), wavelet neural networks (WNN), and cascade forward neural networks (CFNN) combined with two different training algorithms containing Levenberg-Marquardt and Bayesian regression (CFNN-LM and CFNN-BR), respectively, and support vector regression (SVR) models combined with three optimization algorithms containing (farmland fertility algorithm (FFA) grasshopper optimization algorithm (GOA), and particle swarm optimization (PSO) were utilized to predict mud density at HPHT conditions. Moreover, mathematical models were developed using a group data management algorithm (GMDH) to predict this parameter. WNN, SVR model combined with (PSO, GOA, FFA), CFNN, and GRNN algorithms have better adaptability than other artificial intelligence models. These models can improve performance by changing the data and different situations. Also, these models can better resistance against noisy data, which means that they can show good performance compared to uneven and discontinuous data. To this aim, using 986 experimental data of drilling fluid at HPHT, including 117 water-base fluid data, 440 colloidal gas aphron fluid data, 219 oil-base fluid data, and 210 synthetic fluid data the drilling fluid density was modeled. Input parameters including temperature, pressure, initial mud density, and volume fraction of solid content were used for modeling. The results showed that the volume fraction of the solid content as an input variable improves the accuracy of intelligent models in this work. Based on average absolute percent relative error (AAPRE) the best outcomes for predicting the mud density without the input parameter of solid fraction were observed by the PSO-SVR model with an AAPRE of 0.5569%. Following, the GOA-SVR, WNN, and FFA-SVR delivered the next best results with AAPRE of 0.5860%, 0.5786%, and 0.6148%, respectively. Moreover, with the input parameter of solid fraction, the WNN model demonstrated the highest accuracy among all models, achieving an AAPRE of 0.2475%. Also, the correlation developed using the GMDH method based on input parameters of initial mud density, temperature, and pressure yielded satisfactory outcomes and can provide precise and swift estimates. Eventually, Sensitivity analysis revealed that initial mud density had the highest impact on mud density. Lastly, the leverage analysis indicates that most data points are trustworthy based on experimentation, with only a few falling outside the PSO-SVR and WNN models' scope.

Heat transfer enhancement in cylindrical heat pipes with MgO-Al2O3 hybrid nanofluids in water-ethylene glycol mixture: An RSM approach

Heat pipes are passive devices crucial for thermal management in various applications. However, conventional water-based fluids can limit their heat transfer capacity, especially in cold environments where freeze protection is necessary. This study investigates the potential of MgO-Al2O3 hybrid nanofluids to enhance heat transfer performance in cylindrical mesh heat pipes while addressing these limitations. The research demonstrates significant improvements in thermal performance compared to a water-ethylene glycol mixture. The MgO-Al2O3 hybrid nanofluid reduces thermal resistance by enhancing surface wettability in the evaporator section. Additionally, the formation of a nanofluid coating on the evaporator surface leads to a higher heat transfer coefficient. Furthermore, RSM successfully models the relationship between nanoparticle concentration, power input, and key thermal responses (thermal resistance and heat transfer coefficient). These findings highlight the effectiveness of MgO-Al2O3 hybrid nanofluids for augmenting heat transfer in heat pipes and provide valuable RSM models for predicting thermal behavior within the investigated range.

Hybrid Nanofluid Flow in Chamber Containing Heated and Concentrated Internal Source Under the Effectiveness of Applied Magnetic field

Extensive applications of buoyancy-induced flow owing to temperature and concentration gradients have been found exclusively in diversified industrial and technological processes, such as heat exchange systems, solar collectors, heat sinks, and power plants. Additionally, installation of internal sources in confined configurations is essential in operation performance of many devices relevant to transformers, radiators, air cooling engines, fuel cells, semiconductor instruments and so forth. Furthermore, in recent decades, hybridized nanoparticles have been added to different setups to increase their efficiency. Therefore, the premier motive of current work is to examine mass and heat transport mechanisms in water-based fluids encompassed in a corrugated rectangular chamber with the induction of copper (Cu) and alumina (Al2O3) in a hybrid manner. A horizontally oriented magnetic field is assumed, along with the adjustment of a uniformly heated and concentrated source. The thermal conductivity relation proposed by Maxwell is obliged to scrutinize the performance of hybridized nanoparticles in controlling heat exchange in the domain. In this study, a power-law viscosity model is manifested along with utilization of other conservation laws and model structuring in the form of dimensionless expressions is performed by accomplishsing variables. Finite element simulations are executed by utilizing COMSOL Multiphysics 6.0 to solve developed governing equations system. The quantities of interest, such as average heat and mass fluxex, along with presentation of percentage change versus sundry factors, through graphical and tabular display. From a thorough examination, it is inferred that magnetic field plays an effective significance in controlling the excessive thermosolutal gradients. In addition, inudction of hybridized nanoparticles will positively affect thermal exchange in the base liquid compared to the situation when nanoparticles are not added. It is also deduced that average Nusselt and Sherwood numbers decreased up to 3% and 2.4% respectively for hydromagnetic situation (Ha = 0) in comparison to hydrodynamic case (Ha ≠ 0). Decrement in coefficients representing thermosolutal exchange reach up to 55% and 66% approximately against increment in number of corrugations. Induction of hybrid nanoparticles resulted in an increase in the coefficients of thermal and solutal transfer up to 14% and 22%, respectively, compared to the scenario where the base fluid contained no particles. For shear thinning aspects of non-Newtonain liquid, Nusselt and Sherwood numbers exceeds in comparison to Newtonian case and contrary aspects are observed for shear thickening materials.

Molecular dynamics study of the thermal radiatively pressure-driven flow of two immiscible hybrid nanofluids through a curved pipe with viscous dissipation

Hybrid nanomaterials significantly enhance thermal systems through improved thermal conductivity, efficient energy storage, and customized thermomechanical properties. Due to their superior thermophysical characteristics, hybrid nanofluids are crucial in fields such as industry, biomedicine, transportation, and pharmaceuticals. Therefore, the current article scrutinizes the characteristics of heat transfer on the thermally radiative flow of two immiscible hybrid nanofluids through a curved pipe induced due to a pressure gradient in an axial direction. The pipe is divided into two distinct regions: namely, (i) Region 1 (core) is filled with base fluid kerosene oil containing copper (Cu) and carbon nanotube (CNT) nanoparticles, while (ii) Region 2 (peripheral) is filled with base fluid water containing zinc oxide (ZnO) and aluminum oxide (Al2O3) nanoparticles. The complex curvilinear coordinates called toroidal coordinates are used to form a mathematical model for the present problem. The fluid in the core region is assumed to be more viscous than that of the peripheral region as such types of flows are observed in real life (viz. blood flow through arteries). Equations governing the flow are considered without ignoring any curvature ratio terms and are solved analytically by considering the perturbation series. The continuity of velocities and shear stresses at the fluid-fluid interface is taken into account along with no-slip, symmetric, and regularity conditions while solving the two-fluid flow problem under consideration. The effect of various fluid parameters such as curvature ratio, Reynolds number, viscosity ratio, and density ratio on the axial velocity and temperature is studied through 2-dimensional graphs, contour plots, and streamlines. The results show that the axial velocity of immiscible fluids flow decreases with an increase in the concentration of nanomaterials. Heat transport characteristics of nanofluid are increased by the addition of nanomaterials.

Impact of water-rock interaction on natural fractures and shale permeabilities under the closure stress

During the shut-in treatment after hydraulic fracturing, the interaction between the water-based fracturing fluid and the reservoir rock can cause the swell of clay minerals. This can alter the stress condition of the rock, thus activating and expanding the natural fractures in the shale reservoir. However, it is currently controversial whether the effect of water-rock interaction on shale permeability is positive or negative under different closure stresses.To investigate this effect on natural fractures and rock permeabilities under closure stress, imbibition experiments are conducted, and the nuclear magnetic resonance (NMR) T2 spectrum is continually scanned to identify the fracture formation.Results show that the water-rock interaction can activate and expand the natural fractures, resulting in increases of 20.0%–61.6% in rock permeability during the spontaneous imbibition without closure stress; as the percentage of clay minerals increases in shale samples, the degree of rock permeability enhancement also increases. Meanwhile, during the forced imbibition without closure stress (i.e., centrifuge), increases of 1.14–46,912 times in rock permeability are observed, indicating that higher imbibition pressure (or pore pressure) results in a greater degree of rock permeability enhancement. However, when the closure stress exists, the increase of shale permeability during the forced imbibition is only 8.3%–11.5%, even though the activation and expansion of natural fractures are observed; this indicates that the closure stress can hinder the activation and expansion of natural fractures, thus hinders the enhancement of shale permeability by the water-rock interaction.Experimental results of this study indicate the stress condition can significantly affect the impact of water-rock interaction on natural fractures and shale permeability; therefore, operators should consider this effect when designing the shut-in time after hydraulic fracturing to maximize the oil recovery rate.

The Carrying Behavior of Water-Based Fracturing Fluid in Shale Reservoir Fractures and Molecular Dynamics of Sand-Carrying Mechanism

Water-based fracturing fluid has recently garnered increasing attention as an alternative oilfield working fluid for propagating reservoir fractures and transporting sand. However, the low temperature resistance and stability of water-based fracturing fluid is a significant limitation, restricting the fracture propagation and gravel transport. To effectively ameliorate the temperature resistance and sand-carrying capacity, a modified cross-linker with properties adaptable to varying reservoir conditions and functional groups was synthesized and chemically characterized. Meanwhile, a multifunctional collaborative progressive evaluation device was developed to investigate the rheology and sand-carrying capacity of fracturing fluid. Utilizing molecular dynamics simulations, the thickening mechanism of the modified cross-linker and the sand-carrying mechanism of the fracturing fluid were elucidated. Results indicate that the designed cross-linker provided a high viscosity stability of 130 mPa·s and an excellent sand-carrying capacity of 15 cm2 at 0.3 wt% cross-linker content. Additionally, increasing reservoir pressure exhibited enhanced thickening and sand-carrying capacities. However, a significant inverse relationship was observed between reservoir temperature and sand-carrying capacity, attributed to changes in the drag coefficient and thickener adsorption. These results verified the effectiveness of the cross-linker in enhancing fluid viscosity and sand-carrying capacity as a modified cross-linker for water-based fracturing fluid.

Evaluation of heat transfer for unsteady thin film flow of mono and hybrid nanomaterials with five different shape features

Recent advancement in nanotechnology brings the idea of hybrid nanomaterials which offer distinguish applications in thermal reservoirs, cooling systems, energy applications, chemical engineering, vehicle engines etc. The understating of shape features for hybrid nanomaterials is quite essential as such consequences highly influenced various thermal properties like viscosity, thermal conductivity, optical properties, stability etc. The objective of current work is to examine heat transfer analysis due to thin film unsteady flow of hybrid nanofluid. The properties of hybrid nanofluid are justified for entertaining the copper (Cu), aluminium oxide (Al2O3) nanoparticles with water (H2O) base fluid. Additionally, applications of viscous dissipation, heat source and nonlinear radiated effects are attributed to current flow problem. The thermal properties of nanoparticles are examined in presence of five shape features consisting of blades, platelets, cylinders, bricks and spheres. Numerical simulations of problem are performed via Runge-Kutta-Fehlberg method. Comparative heat transfer is performed for mono nanofluid (Cu/H2O) and hybrid nanofluid (Cu−Al2O3)//H2O. It has been observed that heat transfer enhancement is more stable for cylindrical particles as compared to spherical nanoparticles. The skin friction enhances due to Hartmann number for both mono nanofluid (MNF) and hybrid nanofluid (HNF). Current results claim applications in coating thin films, lubrication systems, improving the thermal efficiency in thermal and industrial systems, heat exchangers, cooling systems etc.

Thermal and velocity slip conditions together with thermal radiative effects on Joule heating mixed convection MHD hybrid nanofluid flow induced by an exponentially shrinking or stretching sheet

ABSTRACT The present study analyzes the mixed convection with magnetohydrodynamic (MHD) flow, Joule heating, and heat transfer in hybrid nanofluids on exponentially stretching or shrinking sheets. The study aims to optimize heat transfer and fluid dynamics properties in engineering and industrial applications. The governing partial differential equations are converted into ordinary differential equations by employing appropriate similarity transformations and then solved using the BVP4C MATLAB tool. In this study, the effects of silver volume fraction, velocity slip, thermal slip, magnetic field, heat generation, Eckert number, and thermal radiation on the skin friction coefficient, Nusselt number, velocity profile, and temperature profile are examined and graphically discussed. The base fluid water contained 2% silver (Ag) and 0.1% titanium oxide (TiO2) nano-particles in this hybrid model. The dual solution was present for a shrinking sheet ( λ < 0 ) when λ ≥ λ c . The skin friction coefficient values decreased by approximately 37%, while the local Nusselt number increased by approximately 3.1% when the velocity slip increased in the first solution. The Nusselt number increased by approximately 0.28% and 12% when the Eckert number and radiative parameter increased, respectively, but decreased by approximately 7.5% and 0.20% when the thermal slip and heat generation increased, respectively.

Reservoir Damage Induced by Water-Based Fracturing Fluids in Tight Reservoirs: A Review of Formation Mechanisms and Treatment Methods

Reservoir Damage Induced by Water-Based Fracturing Fluids in Tight Reservoirs: A Review of Formation Mechanisms and Treatment Methods

Influence of Nanofluid Types on the Enhancements of Solar Collector Performance

Nowadays, solar energy is attracting a great deal of research interest. Optimizing solar collectors by incorporating nanoparticles into the base fluid is one area of current research focus. This study investigates how the types of nanoparticles affect the performance of solar collector. We investigated the effects of various nanoparticles types on performance of solar collector. Nanofluid 1 (alumina nanoparticles; Al2O3) and nanofluid 2 (silicon dioxide nanoparticles; SiO2) were combined with water. Nanofluids are made with a constant nanoparticle concentration of 0.5 vol. %. Data collection was conducted under Iraqi environments during the three-month study period (January, February, and March) from 9 a.m. to 3 p.m. In particular, the data for every two-hour period is displayed. The results demonstrated the collector’s efficiency significantly influenced by nanoparticles’ type. At 1 p.m. in February, nanofluid 1 was 4.8% in front of nanofluid 2. The significance of nanoparticle materials in enhancing solar collector efficiency is highlighted by this outcome. When water-based fluid in the solar collector was compared to nanofluids 2 and 1, the latter demonstrated a typically better efficiency. Nanoparticle owns higher thermal conductivity which is one of the reasons behind the increments of the nanofluid's overall conductivity.

Research on the Compounding Scheme of High-Efficiency and Environmentally Friendly Water-Based Well Cleaning Fluid and Analysis of Its Application Effect

In the process of oil well production, the deposition and enrichment of heavy oil and wax are common phenomena. The deposition of these high-viscosity organic substances will not only seriously affect the normal production of oil wells but also cause certain pollution to the environment. Traditional well-washing operations usually rely on high temperatures and strong cleaning chemicals, which have the problems of low cleaning efficiency at low temperatures and poor environmental protection. Therefore, it is particularly important to study a more efficient and environmentally friendly water-based well-washing fluid. At present, most oilfields in China use diesel or organic cleaning agents to clean oil wells, which not only increases the cost of well-washing operations but also may cause pollution to the environment. In response to this problem, the core of this study lies in its innovative compounding scheme. The scheme includes preferred alkaline components, surfactants, emulsifiers, and solubilizers, and introduces glass fiber cutting and erosion technology. This well-washing fluid can effectively remove wax and heavy oil attached to the well wall at a lower temperature, which not only improves cleaning ability but also greatly reduces the dependence on high temperature. The water-based well-washing fluid provided in this study is composed of water, alkaline substances, surfactants, emulsifiers, and glass fibers. Emulsifiers such as Tween 80, OP-20, and polyethers form a stable emulsification system to help disperse and encapsulate oil and grease particles.

Enhanced heat transfer in novel star-shaped enclosure with hybrid nanofluids: A neural network-assisted study

The current research is a numerical investigation of the thermo-fluidic transport trend within a new star-shaped enclosure filled with Fe3O4−MWCNT (Multiwall Carbon Nanotubes) suspended hybrid nanoparticles in a base fluid (water), induced by a horizontal magnetizing field. A rectangular rod inside the cavity positioned at center is kept at a high temperature, as the low temperature is chosen for outer corrugated boundary. Formulation yelled in mathematical principles, along with the boundary conditions and some physical assumptions, are formulated as dimensionless PDEs. The finite element method (FEM) assisted by “COMSOL” software, employed for numerical simulations, and the PARDISO solver is used for solving nonlinear system of equations. Also, an artificial neural network (ANN) model is assembled to analyze the effect of these parameters on thermal and flow transport properties of Hybrid nanofluid. The training of this ANN model is done by a dataset generated from numerical simulations, suggesting predictions regarding heat transport under varying parameters. This approach does not only reduce the effort, but also reduces computing time when exploring the thermal behaviors across various datasets. The visualization of velocity and temperature fields through streamlines and isotherms, along with the evaluation of the Nusselt number, illustrate the enhanced heat transfer facilitated by the incorporation of Fe3O4−MWCNT nanoparticles. The results show that increased magnetic field strength reduces fluid velocity due to the generated Lorentz force, which counteracts convective flow. Percentage analysis indicates that hybrid nanofluids (Fe3O4−MWCNT) significantly improve thermal distribution compared to conventional fluids. This study demonstrates the effectiveness of ANN models in forecasting the influence of diverse factors on the flow and heat transport characteristics of hybrid nanofluids. These insights are essential to the design and optimization of heat transfer systems.

Consequence of Cattaneo-Christov heat and mass flux models on bioconvective flow of dusty hybrid nanofluid over a Riga plate in the presence of gyrotactic microorganisms and Stephan blowing impacts

This study investigates the impact of the Stephan blowing and Cattaneo-Christov flux model on bioconvective flow of dusty hybrid nanofluid over a Riga plate in the presence of gyrotactic microorganisms and variable dust particles volume friction. Mass and heat phenomena are explored in the context of Stefan blowing impacts. The hybrid nanofluid consists of nanoparticles of MgO and Ag base fluid water. The Cattaneo-Christov mass and heat flux model has a significant impact on the bioconvective flow. The model predicts a slower temperature distribution and a higher concentration gradient than the traditional Fick's law and Fourier's law, respectively. The occurrence of gyrotactic microorganisms enhance the flow characteristics. This model is important for optimizing mass and heat transfer in a variety of engineering systems, including heating and cooling technologies, where effective thermal control is essential. It can be used by employing the regulated migration of microbes. By maximizing the removal of impurities, it helps with the design of sophisticated water purification systems in environmental engineering. The amalgamation of gyrotactic microorganisms and Stefan blowing effects augments the comprehension of intricate fluid dynamics, maybe resulting in advancements in microfluidic apparatuses and bio-inspired technologies. In order to transform the controlling PDEs into nonlinear ODEs, a new set of non-dimensional variables is used. The MATLAB (RKF-45th) technique is then used to resolve the ODEs numerically. As the Stephan blowing parameter (0.1≤Sb≤1.9) increases, the outcomes show that the flow distributions upsurge for both the dust and fluid phases, but the dust and fluid phase thermal distributions drop.

Influence of different machining methods on the surface roughness of TC4: dry milling, water-based fluid wet milling, and foam spray milling

The surface quality of workpieces is influenced by various factors, including processing parameters, processing techniques, cutting force, and cutting vibration. Many researchers have studied wet cutting to minimize milling forces and vibrations. We explore a high-density water-based foam milling method. By this method the foam is sprayed onto the surface area of tool and workpiece to absorb vibration energy. Firstly, a milling test system was established to conduct tests on TC4 titanium alloy under three different machining conditions (dry milling, water-based fluid wet milling, and foam spray milling), and the three-dimensional (3D) milling forces and milling vibrations were simultaneously recorded during the milling process, and the workpiece’s surface roughness was measured using a contact roughness measuring instrument. Then principal component analysis method was performed on the 3D milling forces and vibrations to obtain dimensionality-reduced feature values. Subsequently, multi-feature combination prediction models of surface roughness corresponding to the three machining conditions were developed using particle swarm optimization and a generalized regression neural network. Finally, the developed prediction models were compared and analyzed. The research results indicate that high-density water-based foam spray milling can effectively reduce the milling force by 70% and the milling vibration by 85% at most. Foam spray milling reduces the average roughness by up to 49%. The roughness prediction accuracy reaches 95.43%, with an error of less than 0.054 µm.

Exact and fractional solution of MHD generalized Couette hybrid nanofluid flow with Mittag–Leffler and power law kernel

This study investigates the complex behavior of Jeffrey nanofluid flow in a porous oscillating microchannel under the influence of magnetohydrodynamic (MHD) effects. The research explores how magnetic field distortions lead to diverse accumulation patterns of nanofluid particles, a phenomenon attributed to homogeneous magnetization in fluid dynamics. Nanoparticles ranging from 0.25 % to 0.5 % (low and inexpensive concentrations) are remarkably consistent for best results in most machining procedures. Different concentrations of various nanomaterial's is utilized (φ1 = φ2 = 0.01, 0.02, 0.03, 0.04) to make the simple nanfluid and hybrid nanofluid suspensions. By employing fractal-fractional derivatives governed by power law, a mathematical model developed to describe the time-varying, compressible MHD flow of Jeffrey nanofluid. The model incorporates the effects of heat transfer, pressure, and magnetic fields on the fluid dynamics. A novel fractional approach utilizing the Laplace transform is applied to solve the fractal MHD hybrid-fluid model integrated into a porous medium. The study reveals velocity flow decrease with increasing Reynolds numbers but increase with channel inclination. Additionally, both the Darcy number and magnetic field orientation enhance heat transfer rates. In addition, the velocity profile enhanced by the hybrid nanofluid suspension as compared to simple nanofluid flow. The research validates its findings by demonstrating the convergence of fractional and numerical solution methods. Furthermore, the study compares the performance of different hybrid nanofluids, concluding that water-based (H2O + GO + MoS2) hybrid fluids exhibit slightly superior characteristics compared to (CMC + GO + MoS2) hybrid nanofluids.