Aluminum alloys are widely used in the automotive and aerospace industries due to their lower mass-to-strength ratio than other metallic alloys. Apart from their inherent properties, aluminum alloys like other metallic alloys show a significant change in their mechanical properties according to the machining parameters. The research literature on obtaining optimum mechanical properties of aluminum alloys that undergo machining is very limited. Moreover, the combined effect of several parameters on the machinability of aluminum alloys has not yet been explored. In this paper, the effect of three machining parameters (Depth of Cut (DoC)), feed rate (FR), and cutting speed (CS) on the subsurface damage and fatigue life of aerospace-grade aluminum alloy (Al-6082-T6) is observed. Samples are prepared using a full fractional approach to effectively capture the effect of all input parameters. Thereafter, samples were subjected to surface roughness, micro-hardness, and fatigue life tests. Results of surface roughness and micro-hardness tests are compared with fatigue life. The general linear model was employed to capture the percentage effect of each input parameter on the output parameters. The results showed that DoC was the main contributing factor that caused subsurface damage, while surface roughness and fatigue life were mainly affected by FR and CS. Optical microscope images showed a white layer formation that had higher hardness than the base metal. Overall, this research work proposes the input parameters that can be used to achieve minimum surface damage and fatigue life.

The increasing demand for sustainable energy has significantly boosted the requirements for efficient solar-thermal power systems. In this context, ternary nanofluids have the potential to greatly enhance the efficiency of solar-thermal systems by significantly improving heat transport capabilities. This work systematically and extensively analyzes the energy transition to enhance mechanisms of heat transport in renewable energy systems using ternary nanofluid flow comprising iron (III) oxide, gold, and titanium dioxide nanoparticles suspended in polymer. The combined effect of non-Fourier heat flux, multiple slips, and electro-magnetohydrodynamic on slender surfaces is also considered in the flow problem. An appropriate technique is employed to normalize and simplify the governing flow equations into a nonlinear ordinary differential equations system. The Galerkin weighted residual technique is employed to solve the modeled equations through the MATHEMATICA 11.3 software. The findings indicate that increasing radiation and porosity parameters increases fluid temperature. Furthermore, the impact of the exterior concave and inner convex surfaces intensifies, while the electric field effect raises the fluid temperature profile. An increase in the porosity parameter also enhances the micro-rotation profile.

Drilling fluids are important in the extraction of oils and gases through rocks and soil. Clay nanoparticles are essential for enhancing drilling fluid efficiency. The thermal conductivity, viscosity, and boiling point of drilling fluids increase when clay nanoparticles are incorporated, providing resistance to high temperatures and regulating fluid costs. This article illustrates the convection heat transfer in drilling nanofluid while considering the significant presence of clay nanoparticles in the fluid used for drilling. The efficient thermophysical characteristics of clay nanofluid are expressed mathematically using the Maxwell-Garnett and Brinkman formulas. The partial differential equations with physical boundary conditions that control the flow phenomena are predetermined. The similarity technique is used to transmute these partial differential equations into ordinary differential equations, and then an efficient bvp4c solver is used to find dual solutions. The Nusselt number and skin friction are calculated and displayed in tabular and graphical forms, along with the velocity and temperature profiles. Multiple solutions are observed in the cases of shrinkable sheets and buoyancy assisting flow. The findings demonstrate that when volume concentration increases, the Nusselt number rises. In addition, the permeability parameter expands the boundary layer thickness in the lower solution, whereas the opposite behavior is observed in the upper solution.

The study discusses steady‐state double‐diffusive convective heat and mass transfer (HMT) in a square enclosure containing a clockwise rotating adiabatic cylinder located in the center of the enclosure. The model is a 2‐D square enclosure. The left vertical wall of the enclosure is the higher concentrated‐heated wall, the right is the lower concentrated‐cold wall, T while the cylinder and the horizontal walls of the enclosure are thermally insulated with zero‐mass flux. The descriptive equations were solved by the finite element method for . The implications of cylinder size , cylinder rotational‐speed , Lewis number , buoyancy ratio , and Richardson number on concentration, streamlines, and isotherms are reported. Furthermore, the responses of HMT to the parameters of interest were presented in terms of Nusselt and Sherwood numbers. Results showed that while increasing both the cylinder size and the rotational speed of the cylinder were found to augment both heat and mass transfer, the maximum heat and mass transfer occurred at of the concentrated‐heated wall length, and a critical buoyancy ratio of , for which both HMT were maximized was established. Finally, both HMT diminished as Ri increased. This study provides a pathway for performance improvement in micro‐chip‐cooling in the electronic industry, drying technology, and heat exchangers. The impacts of cylinder rotation and size on HMT for the configuration investigated had not been previously investigated. Furthermore, the study established a critical buoyancy ratio value that ensures maximum heat and mass transfer enhancements.

In this research, we conducted a thorough study of Williamson fluid flow in three dimensions over a stretchable sheet with linear permeability, incorporating the influence of heat radiation. The nonlinear governing equalities were figured out utilizing the OHAM. Our findings elucidate the significant impact of various parameters, including the Bioconvection Lewis number, Peclet factor, Williamson, heat radiation and porosity parameters, thermophoresis number, Brownian factor, Prandtl component towards temperature, concentration, and velocity distributions. The study reveals that the velocity in x and y -directions falls with the rise in Williamson and porosity parameters The numerical analysis further demonstrates the influence of these factors on surface drag force, revealing trends such as reduced surface drag force with increasing porosity parameters, and an augmented surface drag force with increasing Williamson parameter. Additionally, enhancement in heat generation and thermal radiation parameters it increases the temperature distribution. Similarly, rise in Brownian number and thermophoresis parameters that lead to enhancement in temperature profile.

The deployment of plant extracts as inhibitors in corrosion protection of carbon steel is widely praised considering their biodegradability, availability and nontoxic attributes. However, their performance as corrosion inhibitors for a wellbore in an unmodified state is limited due to solubility-related issues and thermal degradation in the environments obtainable during oil well acidizing. The present study synthesized a Dillenia suffruticosa leave extract (DSLE)-mediated NiO nanocomposite for a matrix acidizing oil well environment using biosynthesis technique. The biosynthesized DSLE-NiO nanocomposite was intensively characterized using UV-vis, XRD, TEM and SEM/EDX techniques. The corrosion inhibition performance of the synthesized nanocomposite studied by gravimetric and electrochemical characterization (PDP and EIS) revealed that this nanocomposite could moderately inhibit corrosion of C-steel in an oil well acidizing environment up to an average inhibition efficiency (IE) of 65.4 % at an optimum concentration of 1000 ppm. This efficiency was significantly increased by modifying the DSLE-NiO nanocomposite with sodium iodide (NaI) to 95.2 % at the same concentration. The surface assessment using SEM/EDX affirmed that iodide species significantly enhanced the inhibition performance of the DSLE-NiO nanocomposite in corrosion protection of C-steel in the acidizing solution. This investigation promotes the application of carbon steel-made well casing for matrix acidizing oil well operations in the oil and gas industry.

This research explores the dual-mode manifestation within the nonlinear Schrödinger equation, elucidating the amplification or absorption of paired waves. This study delves into the simultaneous generation of two distinct waves associated with the dual-mode phenomenon with three crucial parameters: phase velocity, nonlinearity and dispersive factor. The resulting wave phenomena from these solutions have implications across various fields, including fluid dynamics, water wave mechanics, ocean engineering and scientific inquiry. The study employs the modified Sardar sub-equation method to obtain the optical soliton solutions, encompassing various types such as dark, bright, singular, combo dark–singular, periodic singular and dark–bright solitons. The obtained results highlight the reliability and simplicity of the modified Sardar sub-equation method. Additionally, the paper delves into the parametric conditions crucial for shaping and sustaining these solitons. The research explores the interaction of dual waves and the variation in wave speed. Furthermore, dynamic phenomena are illustrated, and the physical implications of the solutions are interpreted using 3D and 2D plots with different parameter values.

An attempt is made to analyze the effects of micropolar fluid squeezing film implementation between porous elliptical plates under the application of an external magnetic field. The appropriate improved hydromagnetic Reynolds equation governing the pressure distribution is acquired. The Reynolds equation is solved for lubrication features such as pressure distribution, load-carrying capacity and time-height relationships. It is noticed that a micropolar lubricant with a magnetic field may considerably impart a much-appreciated increase in load-carrying capacity, pressure distribution and response time compared to the classical case. Nevertheless, it is found that, as with the magnetic field, the larger values of the fluid gap number and the coupling number increase lubrication characteristics. Also, it is found that such a bearing cannot support a large number of lubrication characteristics because of the pores on the main path of fluid flow.

The purpose of the study is to investigate the thermal proficiency of a trihybrid magnetized water-based cross nanofluid over an inclined shrinking sheet. Cross-fluid is the best model to investigate the fluid flow at a very high and very low share rate. There are three nanoparticles that are added in based fluid (water) to form the requisite posited ternary hybrid nanofluid. Moreover, heat transport analysis is scrutinized by incorporating the melting conditions. The obtained nonlinear system of partial differential equations (PDEs) from assumed physical assumption is converted into the nonlinear setup of ordinary differential equations (ODEs). These ODEs are passed under the boundary value problem of a fourth-order (bvp4c) MATLAB program for numerical results. With the help of bvp4c, data are further trained through an artificial neural network and results are predicted. Results are compared with both techniques and found smooth agreement. The obtained numerical results provide valuable insight for optimizing heat transfer processes involving nanoparticle-enhanced fluid on inclined shrinking sheets. From the results, it is concluded that the inclusion of nanoparticles enhances the viscosity and thermal conductivity of the fluid. High temperatures make rapid heat transfer scenarios.

Aluminium-carbon nanotube (Al-CNT) composites have proven to offer a lot of potential for applications involving friction and wear. This study investigated the wear behaviour of Al-CNT composites with uniformly dispersed CNTs without structural damage at concentrations of 0.5 wt%. The wear behaviour of these composites has been compared with that of pure aluminium, which was fabricated using the identical wet shake mixing, compaction, and extrusion procedure. The study aimed to determine how CNT concentration and applied stress influence the wear behaviour of composite materials. Scanning electron microscope (SEM) was used by the researchers to examine the surface features of the worn samples. The study showed a significant increase in hardness and wear resistance with adding CNT. Compared to pure aluminium, the composite containing 0.5 wt. % CNTs demonstrated a 25% reduction in wear rate, and the average value of the coefficient of friction (mu) also decreased. The wear rate and coefficient of friction increased when samples containing 0.5 wt. % CNTs were subjected to various loading scenarios. The major contribution of CNTs to improving wear characteristics was discovered through SEM investigation of these worn surfaces. Using the ANSYS Workbench, a built finite element model was used to perform contact analysis on the disc and pin. Analytical results and experimental data were contrasted, and the ANSYS data was utilised to assess the process parameters. The comparison confirmed the analysis accuracy and dependability, which showed a consistent agreement between the analytical findings and the experimental results.

PurposeThis study aims to develop and evaluate epoxy coatings enhanced with micaceous iron oxide (MIO) and nanocrystalline aluminum (Al) particles, focusing on improving mechanical and corrosion-resistance properties for extended durability.Design/methodology/approachThree epoxy coatings were fabricated by incorporating 1, 2 and 3 wt% MIO with a fixed 2 wt% of ball-milled Al particles. The coatings were thoroughly characterized using field emission scanning electron microscopy, thermogravimetric analysis, hardness and scratch resistance tests, nanoindentation and electrochemical impedance spectroscopy (EIS). The EIS measurements in 3.5 wt% NaCl solution were performed over various immersion periods (1 h, 7, 14, 21 and 30 days) to study the coatings' corrosion behavior.FindingsThe coating with 2 wt% MIO exhibited superior corrosion resistance across all immersion periods, outperforming the other formulations. Although corrosion resistance initially declined after seven days of immersion, it improved significantly after extended exposure (14-30 days) due to the formation of protective oxide layers on the coating surface. The combination of MIO and nanocrystalline Al also enhanced the mechanical properties of the epoxy coatings, delivering improved hardness, scratch resistance and overall stability.Originality/valueThis study highlights the synergistic effect of MIO and nanocrystalline Al particles in epoxy coatings, demonstrating their potential to enhance both mechanical performance and long-term corrosion resistance. The research offers valuable insights into the formulation of advanced epoxy coatings for applications requiring durability under harsh conditions.

In this work, the powder metallurgy technique was employed to manufacture pure titanium (Ti) and 88% titanium–12% zirconium (TiZr) alloy. The electrochemical corrosion investigations for pure Ti and the TiZr alloy were carried out after exposure for 30 min and 3 days in 3.5% NaCl solutions. The Nyquist and Bode plots obtained from the electrochemical impedance spectroscopy experiments revealed that the presence of Zr remarkably magnifies the corrosion resistance of Ti via increasing the impedance and degree of the phase angle, as well as the polarization and solution resistances. The potentiodynamic cyclic polarization measurements revealed that the presence of 12% Zr highly enhances the corrosion resistance of Ti. These polarization results showed that Zr addition reduces the corrosion of Ti via decreasing its corrosion rate. The intensity of the current when measured with increasing time of the experiment at −0.10 mV (Ag/AgCl) indicated that the addition of 12% Zr greatly decreases the absolute current, which indicates that alloying Zr within Ti reduces the severity of its corrosion in the chloride electrolyte. The morphology of the surfaces and the possible surface layer(s) for the corroded Ti and TiZr samples were analyzed using a scanning electron microscope and energy dispersive x rays. Results collectively depicted that the presence of Zr increases the corrosion resistance when alloyed with Ti.

According to a remarkable result, the polarization diffusion coefficient of drugs, such as those that slow down and ease the body, may be determined based on the rise in magnetism that follows. This research examines how a magnetic field impacts the Casson-type flow of a viscous, incompressible hybrid nanofluid that naturally flows across two parallel plates. Copper (Cu) and aluminum-oxide (Al 2 O[Formula: see text] are the two nanoparticles and their physical properties are intended to be the foundation fluids, together with water and sodium alginate as based fluids. The revised fractional model is investigated using the Laplace transformation using the most current and updated definition of the fractional-order derivative with memory effect, i.e. Prabhakar fractional derivative. The impacts of various constraints on distinct nanoparticles are investigated and visually depicted. As a result, we have concluded that a drop in volumetric percentage reduces fluid velocity. The water-based hybrid nanofluid (HNF) has a more significant influence on the temperature and momentum profile than the sodium alginate-based HNF due to the physical appearances of the investigated nanoparticles. The Casson fluid parameter augmentation also regulates the velocity profile by decreasing the velocity field.

The growing demand for fiber-reinforced polymer (FRP) in industrial applications has prompted the exploration of natural fiber-based composites as a viable alternative to synthetic fibers. Using jute–rattan fiber-reinforced composite offers the potential for environmentally sustainable waste material decomposition and cost reduction compared to conventional fiber materials. This article focuses on the impact of different machining constraints on surface roughness and delamination during the drilling process of the jute–rattan FRP composite. Inspired by this unexplored research area, this article emphasizes the influence of various machining constraints on surface roughness and delamination in drilling jute–rattan FRP composite. Response surface methodology designs the experiment using drill bit material, spindle speed, and feed rate as input variables to measure surface roughness and delamination factors. The technique of order of preference by similarity to the ideal solution method is used to optimize the machining parameters, and for predicting surface roughness and delamination, two machine learning-based models named random forest (RF) and support vector machine (SVM) are utilized. To evaluate the accuracy of the predicted values, the correlation coefficient (R2), mean absolute percentage error, and mean squared error were used. RF performed better in comparison with SVM, with a higher value of R2 for both testing and training datasets, which is 0.997, 0.981, and 0.985 for surface roughness, entry delamination, and exit delamination, respectively. Hence, this study presents an innovative methodology for predicting surface roughness and delamination through machine learning techniques.

Sulfur dioxide (SO 2 ) belongs to the highly reactive group of gases familiar as “Oxides of Sulfur”. SO 2 has lots of adverse effects on plants, respiratory system and many other environmental issues. Sulfur dioxide is a primary pollutant which is regulated worldwide, due to the combustion of fuel. Different approaches are adopted to economically control the SO 2 in the environment which causes the production of sulfuric acid that is reflected in acid rain. The aim of this study is to investigate the invariant regions and solution pathways for the formation of H 2 SO 4 in a multi-step reaction mechanism. The employed Model Reduction Techniques (MRTs) such as Spectral Quasi Equilibrium Manifold (SQEM) and Intrinsic Low Dimensional Manifold (ILDM) give the solution curves, which functions as a primary approximation to invariant manifold. It is achieved that each chemical specie can be assessed rather than taking the overall mechanism. The new discovery suggests that we could achieve the invariant regions for SO 2 and H 2 SO 4 . SO 2 emissions, along with emission norms, will be disclosed. The comparison of MRTs is depicted through tabular and graphical representations, while theoretical results are demonstrated through computer simulations using MATLAB.

Climate change has emerged as one of the most significant challenges in modern agriculture, with potential implications for global food security. The impact of changing climatic conditions on crop yield, particularly for staple crops like wheat, has raised concerns about future food production. By integrating historical climate data, GCM (CMIP3) projections, and wheat-yield records, our analysis aims to provide significant insights into how climate change may affect wheat output. This research uses advanced machine learning models to explore the intricate relationship between climate change and wheat-yield prediction. Machine learning models used include multiple linear regression (MLR), boosted tree, random forest, ensemble models, and several types of ANNs: ANN (multi-layer perceptron), ANN (probabilistic neural network), ANN (generalized feed-forward), and ANN (linear regression). The model was evaluated and validated against yield and weather data from three Punjab, Pakistan, regions (1991–2021). The calibrated yield response model used downscaled global climate model (GCM) outputs for the SRA2, B1, and A1B average collective CO2 emissions scenarios to anticipate yield changes through 2052. Results showed that maximum temperature (R = 0.116) was the primary climate factor affecting wheat yield in Punjab, preceding the Tmin (R = 0.114), while rainfall had a negligible impact (R = 0.000). The ensemble model (R = 0.988, nRMSE= 8.0%, MAE = 0.090) demonstrated outstanding yield performance, outperforming Random Forest Regression (R = 0.909, nRMSE = 18%, MAE = 0.182), ANN(MLP) (R = 0.902, MAE = 0.238, nRMSE = 17.0%), and boosting tree (R = 0.902, nRMSE = 20%, MAE = 0.198). ANN(PNN) performed inadequately. The ensemble model and RF showed better yield results with R2 = 0.953, 0.791. The expected yield is 5.5% lower than the greatest average yield reported at the site in 2052. The study predicts that site-specific wheat output will experience a significant loss due to climate change. This decrease, which is anticipated to be 5.5% lower than the highest yield ever recorded, points to a potential future loss in wheat output that might worsen food insecurity. Additionally, our findings highlighted that ensemble approaches leveraging multiple model strengths could offer more accurate and reliable predictions under varying climate scenarios. This suggests a significant potential for integrating machine learning in developing climate-resilient agricultural practices, paving the way for future sustainable food security solutions.

In the growing need for energy and heightened environmental considerations, the effective control and optimization of thermal processes and transferring mass are of utmost importance. Engine oil and water-based nanofluids have emerged as potential solutions for various industrial applications, from energy generation to sophisticated manufacturing. Because of these demands, the current work aims to explore the need and significance of its research field, providing insights into crucial elements of heat and mass transport properties in the presence of hybrid nanofluids. This work investigates the impact of an induced magnetic field, including hybrid nanoparticle circulation across a stretched surface with an endo/exothermic chemical reaction and the concentration of waste discharge effects. The acquired ordinary differential equations (ODEs) were solved using the Runge Kutta Fehlberg 45 technique. The findings show that engine oil leads to effectiveness in heat transfer, while water-based hybrid nanofluid performs better mass transfer. While motor oil works well in an endothermic situation, water-based hybrid nano liquid has a noticeable effect on heat transfer over the activation energy component in an exothermic chemical reaction process. Further, water-based nanofluids exhibit lower pollutant levels than engine oil when exposed to local pollutant external source parameter. These results provide essential guidance for choosing the best nanofluid for a given engineering problem, leading to greater effectiveness and productivity in various applications, including advanced cooling systems, chemical manufacturing, pharmaceuticals, waste treatment, and pollutant dispersion control.

The incorporation of three distinct nanoparticles in blood within the context of cubic autocatalysis holds significant potential for enhancing biomedical applications, particularly in targeted drug delivery and therapeutic interventions. The increased reaction rate improves the efficiency of catalytic processes within the bloodstream. This research investigates the thermal transport characteristics of a trihybrid Carreau nanofluid (blood) containing copper (Cu), titanium dioxide (TiO2), and aluminum oxide (Al2O3), nanoparticles in the context of a wedge-shaped artery under the influence of autocatalytic cubic autocatalysis. Effects of thermal radiation, and heat generation are used for heat transport analysis, heterogeneous-homogeneous chemical process included for blood concentration, and an inclined magnetic field is imposed for securitization of blood velocity. Also, the generated PDEs from the physical model are handled through similarity transformations and converted into ODEs. Bvp4c, a numerical technique is used to get the solution and then Levenberg-Marquardt neural network (LM-NN), a multilayer neural network scheme is used to train and predict the solution for each parameter. In addition, the numerical values of volumetric friction of coefficients enhance the thermal conductivity, and the heat transport rate is increased. The magnetic parameter, radiation and chemical processes enhance the rate of heat transport while the Weissenberg number reduces the velocity profile.

The aim of the current study to inspect the magnetic flow properties and heat transport features near an oblique stagnation point of a nanofluid with mixed convection through a vertical Riga plate are examined. The Riga plate is a familiar actuator made out of permanently fixed electrodes and magnets that moved away from the plate due to an exponential decline in the Lorentz force. Nanofluid is taken into consideration due to its peculiar properties, such as remarkable thermal conductivity is the significance of the study. These properties are important in heat exchangers, electronics, advanced nanotechnology, and material sciences. Using usual similarity transformations, the group of leading partial differential equations is distorted into a group of nonlinear ordinary differential equations. Then, a very proficient procedure namely bvp4c is utilized to discover the solution. For particular values of the different influential fluid parameters, the characteristics of the dimensionless temperature and velocity along with drag force and heat transfer are investigated graphically. In addition, the symmetrical results were initiated for both cases of the slip and without slip parameters. It is demonstrated that for greater influence of the volume fraction of nanoparticles, the normal and tangential velocity profile drops down for the instances of aiding and opposing flows due to fluctuations in the presence of slip factor, and absence of slip factor. In contrast, the temperature profile intensifies in both the cases of slip parameters and without the slip parameter owing to the phenomenon of assisting flow and opposing flow subject to superior impressions of the nanoparticle volume fractions. It is also observed that depending on the equilibrium between buoyancy effects, obliqueness, velocity slip parameters, and straining motion, the location of the point xbs${x}_{bs}$ of zero shear stress (friction factor on the surface of the wall) is displaced to the right or the left of the origin.