Compared with traditional fluids, ternary nanofluids have been demonstrated to considerably increase the thermal conductance and thermal transfer properties of base fluids. Their benefits include cooling, thermal management, and other uses for effective heat transmission. This study considers the heating effect of the Prandtl fluid while analyzing the flow of ternary nanofluids over a Riga plate. The hybrid ternary nanoparticles, consisting of titanium dioxide (TiO2) and aluminum alloys, are suspended in engine oil which is used as base fluid. Brownian and thermophoretic features are incorporated into the mass and energy equations to improve the thermal characteristics of the new composition and stabilize the flow. Adding thermal radiation to the energy equation strengthens it even further. To evaluate the features of flow, the mathematical model involves the modified Buongiorno's model. This framework aims to represent the influence of thermophoresis and Brownian motion on the system under consideration. The controlling differential equations are converted to ordinary differential equations (ODEs) using the appropriate similarity variables. Then, the Bvp4c algorithm is used to analyze ODEs. The effects of several factors on the temperature, concentration, and velocity profiles are discussed, and the results are illustrated using graphs and tables. Furthermore, skin friction and Nusselt numbers are calculated to evaluate other factors. This paper presented an innovative method using artificial neural networks (ANNs). A reliable data set is systematically collected and processed to guarantee precise testing, validation, and training of the ANN model. A comparison analysis has validated the findings of the study with previous literature. An increase in the Prandtl fluid parameters increases the velocity profile. The trend of increasing temperature in the ternary nanoliquid is attributed to the increasing values of the thermal heat generation/absorption factor. Additionally, a significant increase in the temperature of the ternary nanofluid is attained for the larger dimension and shape factors of the nanoparticle. The heat transmission rates of Rd in 0.8,1.0,1.2, and 1.4, respectively, are 25.67%,33.86%,36.29%, and 40.13%. As the value of Rd increases from 0.8 to 1.4, the rate of heat transmission is increased by 7.91%.

The elevated water absorption and reduced strength of recycled concrete aggregates constrain their utilization in building materials. Bacillus megaterium (MTCC-1684) exhibits significant potential for microbial-induced calcium carbonate precipitation (MICP), an eco-friendly technique that improves the weaker regions of recycled coarse aggregates (RCA), reduces hydrophilicity and enhances the tensile strength of banana fibre (BF). The primary objective is to investigate the efficiency of bacterially treated banana fibre (TBF) and bacterially treated RCA as carriers for bacterial spores in banana fibre recycled aggregate self-healing concrete (BFR-SHC). The results revealed that MICP significantly improved water absorption and bulk density of 50 % RCA by 46.8 % and 9.8 %, respectively, through calcite deposition on surfaces and pores > 0.2 mm. The healing efficiency with 50 % TRCA and 2 % TBF was more pronounced in the recovery of flexural properties and the trends were found as BF2R50 > , BF1R50 > BF1R100 > , BF2R100. Notably, the maximum flexural toughness efficiency index (eta) increased from 1.04 to 1.09 in BF2R50. The incorporation of bio-treated RCA and BF accelerated fibre-matrix regeneration and crack sealing through calcite precipitation, restoring flexural properties. Morphological analysis of BFR-SHC showed that regenerated calcite at the fibre-matrix interface consisted of rhombohedral and vaterite formed after a 56-day healing incubation period. The findings encourage concrete engineers to adopt bacterial self-healing mechanisms for the development of eco-friendly solutions for sustainable construction practices.

This study investigates the interplay between micropolar tetra-hybrid nanofluids and localized magnetic fields within a rectangular cavity, utilizing Eringen's model to explore enhanced thermal management strategies. Emphasizing the novel integration of nanoparticles - silver (Ag), single-walled carbon nanotubes (SWCNT), titanium dioxide (TiO2), and copper (Cu) - this research aims to augment heat transfer efficiency and understand fluid dynamics under magnetic influence. The equations are modeled using a single-phase approach, and the stream- vorticity formulation is applied to address the dimensionless governing partial differential equations. Instead of a uniform magnetic field, a confined magnetic field with vertical strips was introduced throughout the flow domain. MATLAB codes, developed by the authors, were used to explore the impact of these parameters on nanofluid flow and thermal properties. Through numerical analysis, the study elucidates the impact of magnetic field configurations, particularly strip arrangements, on flow patterns, thermal distributions, and vortex formation. It demonstrates that higher magnetic numbers significantly modify flow complexity and thermal behavior, indicating magnetic

This research presents an experimental investigation on the thermal management and improvement of electrical efficiency of photovoltaic (PV) systems employing a phase change material (PCM) and water combination technique as heat dissipation systems through an improved design. This experiment was conducted in the semi-arid environment of Aligarh, India. The experiment utilised an aluminium enclosure that housed PCM with a melting point of 30 °C (OM 30), along with integrated water circulation pipes. The study involved the evaluation and comparison of three types of PV systems: a PV system with an empty casing attached to the rear surface, a PV system with a casing filled with PCM, and a PV system with a casing filled with a combination of PCM and water circulated using embedded serpentine pipes (without making contact with the casing walls). The water flow rates used in the study were 0.0027 kg/s and 0.0034 kg/s. Power generation, exergy analysis, power efficiency, and performance metrics, specifically the power increase performance variable of the system were compared. The average increases in electrical efficiency, power output, power enhancement, and maximum average temperature reduction, were found to be 13.75%, 24.14 W, 27.6%, and 5.6 °C, respectively at water flowrate of 0.0034 kg/s (best case). The findings of the study indicate that the improvement in the design concept of the PCM and water combination system results in superior performance of the PV panel compared to other approaches.

A strategic plan for manufacturing systems in the fourth industrial revolution requires smart manufacturing to enhance the performance of manufacturing technologies. Consequently, the machine learning technique is one of the major techniques to meet these requirements. This research aims to propose optimization methods including a hybrid artificial neural network (ANN) with genetic algorithms (GA) particle swarm optimization (PSO), and Adaptive Neuro-Fuzzy Inference System (ANFIS) to predict and optimize surface roughness during dry machining of AISI 1045 steel. To achieve this experimentally investigates and statistical analyses on how different cutting parameters affect the performance of machining AISI 1045 steel were performed. Experimental trials were conducted using a full factorial design, and an analysis of variance (ANOVA) was used to determine the impact of each variable on the outcomes of the process. ANN-GA, ANFIS, and ANN-PSO techniques were used to develop a prediction model of surface roughness. In addition, The ANN-GA, ANFIS, and ANN-PSO algorithms are used for determining the optimal process parameters for the turning of AISI 1045 steel. The results show that ANFIS, ANN-PSO algorithm, and ANN-GA presented a high-accuracy model for predicting surface roughness. In addition, the ANFIS model demonstrates superior performance compared to the ANN-PSO algorithm, and ANN- GA. Ra of 0.202 μm was achieved by using tool-type wiper carbide inserts, a speed of 80 m min ^−1 , a depth of cut of 0.5 mm, and a feed rate of 0.045 mm rev ^−1 as the input process parameters with a relative error of less than 3%.

The present study deals with the development of a new separator for the separation of iron ore and coal of a size fraction of -4 + 0 mm individually. Particles of iron ore with size fraction - 2 + 0 mm and finer coal were separated separately using screen mesh with an aperture size of 2 mm. The operating characteristics of the screen's upward slope and the screen's vibration frequency of the new separation equipment can be easily modified. In this study, moist iron ore and coal segregation have been carried out for various separation angles and frequencies, and test results of moist iron ore and coal were compared based on their moisture content and density. Also, for the prediction of results, artificial neural network (ANN) modeling and regression analysis were implemented. The R-square value for regression analysis of experimental results was found higher than 85.60% and 88.50% for coal and iron ore respectively. The R-square value for the ANN mathematical model of experimental results was found higher than 99.10% and 98.24% for coal and iron ore respectively. The comparison of the regression model, the ANN model's mathematical modeling results, and the test results for the separation of moist iron ore and coal hold a strong correlation. For validation, residual analysis was also performed on the separation of moist iron ore and coal regression and ANN models. Including improved accuracy, reduced computational time, and enhanced predictive capabilities. The residual probability plot's results for homoscedasticity, low standard deviation, normality, and independence demonstrate that, under all experimental settings for iron ore and coal, the developed artificial neural network (ANN) model outperforms the regression model in terms of prediction accuracy.

With the advancements in IIoT tools and applications, real-time monitoring systems with remote access capabilities are gaining great interest. The use of these instruments in monitoring manufacturing processes can lead to enhanced performance and increased efficiency. In this paper, a real-time monitoring system using Raspberry Pi is developed for monitoring the maximum temperature and axial force during friction stir spot welding (FSSW). There are two sensors used in this experiment: a thermocouple and a load cell connected to a microcontroller via Node-RED. Through the use of the system and response surface methodology (RSM), three factors (tool rotation speed, plunge rate, and dwell time) were examined in relation to maximum temperatures and axial forces during FSSW. Models of maximum welding temperature and force were constructed based on multivariate regression and adaptive network fuzzy inference system (ANFIS). The monitoring system proved effective at measuring and tracking temperature and axial force in real time across multiple platforms.

Rapid industrialization and population growth have placed increasing pressure on conventional energy resources, prompting the need for reliable, low-emission alternatives. Geothermal energy, with its capacity for both thermal and electrical production, remains largely untapped in Pakistan despite abundant thermal springs and favorable tectonic settings. In this study, we evaluate the potential of the Tattapani thermal spring, located within the Hazara-Kashmir syntaxis of the Sub-Himalayan fold-thrust belt, using an integrated ground magnetic approach. A grid of 1,107 measurements spaced at 50 × 50 m was collected with a proton precession magnetometer. Hierarchical statistical analysis of total magnetic intensity and residual magnetic data delineated five distinct magnetic zones, ranging from low-intensity magnetic thermal zone to high-intensity backgrounds each corresponding to different lithologies confirmed by X-ray diffraction. The Tattapani thermal spring exhibits a total magnetic anomaly of about 500nT, which more clearly emphasizes local variations than absolute IGRF-dependent field values. First-order derivative filters (dx, dy, dz) and downward continuation to 1,000 m sharpened fault-controlled alterations, while Euler deconvolution (structural index 0–1) revealed that over 70

The use of renewable energy sources is leading the charge to solve the world’s energy problems, and non-Newtonian nanofluid dynamics play a significant role in applications such as expanding solar sheets, which are examined in this paper, along with the impacts of activation energy and solar radiation. We solve physical flow issues using partial differential equations and models like Casson, Williamson, and Prandtl. To get numerical solutions, we first apply a transformation to make these equations ordinary differential equations, and then we use the MATLAB-integrated bvp4c methodology. Through the examination of dimensionless velocity, concentration, and temperature functions under varied parameters, our work explores the physical properties of nanofluids. In addition to numerical and tabular studies of the skin friction coefficient, Sherwood number, and local Nusselt number, important components of the flow field are graphically shown and analyzed. Consistent with previous research, this work adds important new information to the continuing conversation in this area. Through the examination of dimensionless velocity, concentration, and temperature functions under varied parameters, our work explores the physical properties of nanofluids. Comparing the Casson nanofluid to the Williamson and Prandtl nanofluids, it is found that the former has a lower velocity. Compared to Casson and Williamson nanofluid, Prandtl nanofluid advanced in heat flux more quickly. The transfer of heat rates are 25.87% , 33.61% and 40.52% at Rd =0.5, Rd=1.0 , and Rd=1.5 , respectively. The heat transfer rate is increased by 6.91% as the value of Rd rises from 1.0 to 1.5. This study is further strengthened by a comparative analysis with previous research, which is complemented by an extensive table of comparisons for a full evaluation.

The main objective of the present study was to investigate the influence of graphene nanoplatelets (GnPs), Titanium dioxide (TiO2), and its hybrid filler on the mechanical, thermal, and morphological characteristics of polypropylene (PP) based hybrid composites for structural applications. The PP-based hybrid composites were prepared using a twin-screw extruder for different filler compositions and then molded into tensile specimens with a mini-jet injection molding setup. Tensile, thermal, and morphological analyses were performed to determine the hybrid capability of the PP-based composites. The results showed that the values of tensile modulus and tensile yield strength show significant improvement at about ∼24% and ∼17%, while the values of elongation at break show a slightly decreasing trend for the hybrid composites compared to PP composites. The thermal stability analysis shows that the higher the hybrid filler content, the higher the temperature. Further, the SEM micrographs for the hybrid fillers show good dispersion onto the PP matrix, which may increase the interfacial adhesion between PP and fillers, thus enhancing the mechanical and thermal properties of PP-based hybrid composites.

The Sharma–Tasso–Olver–Burgers (STOB) equation is a nonlinear partial differential equation that appears in many branches of science, engineering and describes significant phenomena including wave propagation and fluid dynamics. The STOB equation characteristics and solutions for the nonlinear situation are thoroughly examined in this research work. The analytical techniques, namely the extended (G'/G^2) -expansion method, stability analysis, and sensitivity analysis, are used to determine the solitons solution of STOB equation. The study begins with an overview of the equation, highlighting its significance in modeling. The nonlinear nature of the equation leads to interesting dynamics and challenges in its analysis and solution. The research paper focuses on understanding the behavior of solutions and identifying the solution with the help of graphical interpretation. Numerous of the identified solutions are depicted in figures to provide a physical comprehension. Because it is critical, we use appropriate values of parameters to highlight the physical aspects of the supplied data using 3D, 2D, and contour charts. The proposed techniques are valuable and contribute to the field of nonlinear sciences. Various nonlinear evolutionary equations are employed to represent models of nonlinear physical phenomena

Localized surface plasmonic resonance (LSPR) biosensing using optical fibers has gained popularity due to its label-free approach and high sensitivity to changes in the nanoparticle surface's local index of refraction. However, improving sensitivity remains a challenge. In this study, a two-step approach was employed to fabricate a composite structure using gold nanoparticles and monolayer graphene (Gr-AuNPs). The combination of AuNPs and graphene membrane demonstrated high potential for Surface-enhanced Raman scattering (SERS) and surface plasmonic resonance (SPR) fiber sensors. The Gr-AuNPs sensor successfully detected R6G molecules with a low detection limit of 10-12 M, indicating promising SERS activity. Numerical simulations confirmed that the graphene generated densely hot spots in the nanogap region between plasmonic layers. It's interesting that the proposed SPR-SERS Sensor can detect both glucose and thiram. This demonstrates the sensors practicality and can help with a basic environmental need to find leftover pesticides in the soil. The combination of SPR-SERS dual-mode detection provides more options for detecting and verifying data, increasing the precision and repeatability of experiments.

Concrete buildings constructed globally are susceptible to diverse circumstances of use and exposure to environmental elements. Diverse varieties of construction materials are used in the building sector to fabricate edifices and constructions. Architects and construction project managers use these classifications of materials and products to precisely determine the materials and techniques employed in building projects. This study presents a simulation of elastic guided waves in concrete structures, which is motivated by the vast variety of uses of such structures. The elastic-guided wave technique is a technology used for nondestructive assessment. This approach utilizes acoustic waves that travel along an extended structure, directed by its limits. This enables the propagation of waves over a significant distance while minimizing energy dissipation. Currently, the Elastic guided wave method is extensively used for the examination and evaluation of various engineering structures, namely for inspecting metallic pipelines globally. From a single place, it is possible to investigate distances of several hundred meters in some instances. Additionally, there are apps available for the examination of rail tracks, rods, and metal plate constructions. In this research, a novel mathematical model is introduced for accurately replicating elastic guided waves in concrete structures in practical applications. To reinforce the current concrete structure using mathematics, graphene oxide powders with improved mechanical properties are obtained using the role of the mixture, and the Halpin-Tsai micromechanical model is used. After obtaining the mathematical modeling of the current concrete structure using the theory of shear deformation with four unknown high-order terms and Hamilton's principle, the equations are solved using free and forced wave propagation approaches. Finally, some applicable suggestions for improving the efficiency and dynamic stability of the presented concrete structure reinforced by graphene oxide powders are presented in detail in the results section.

This manuscript investigates the comparative analysis of SiC and SiC-Ag with methanol-base fluid. The nano species are more important in many fields such as medicine, catalysis, energy-based research, imaging, and environmental sciences. The present study includes theoretical and numerical studies of the Prandtl hybrid nanofluid flow model, which takes into account the effects of heat radiation across a stretched cylinder, inclined magnetohydrodynamics (MHD), and Darcy-Forchheimer. The cylinder's surface is subjected to the convective slip boundary condition as part of the study. The main aim of this study is to enhance heat transformation. The flow problem is formulated in a system of nonlinear partial differential equations. By employing a similarity transformation, a nonlinear system of partial differential equations can be converted into a linear system of ODEs. A feasible numerical technique Bvp4c is used to solve the reduced system of ODEs through MATLAB software. The results of temperature, concentration, and velocity profiles are discussed graphically. In section and injection scenarios, the velocity profile of both fluids decreased with increasing inputs of magnetic and Darcy-Forchheimer parameters. The heat transmission improved for higher inputs of Prandtl and thermal radiation parameters for both section and injection cases. Furthermore, our findings align with the body of current literature. The conclusions are supported by a careful comparison with pertinent research that has been published in earlier publications.

The objective of this manuscript is to examine the nonlinear characteristics of the modified equal width equation that is used to simulate the one-dimensional wave propagation nonlinear media, incorporating the dispersion process. Utilizing the traveling wave transformations, we are able to convert the nonlinear partial differential equations (NLPDES) into ordinary differential equations (NLODEs). In this study, an analytical technique is used to utilize the exact soliton solutions of this proposed model. This efficient method is known as the modified auxiliary equation method. This extraction of soliton solutions contains various types of solutions such as trigonometric, hyperbolic, and rational solutions. For a graphical representation, we utilize Mathematica and Maple software to depict the solutions in 3 D , 2 D , contour plots, and density plots. The main novelty of this paper is to explore the qualitative study, which includes the chaotic behavior, bifurcation, sensitivity, and stability analysis of this problem. For this, first, we apply the Galilean transformation, we convert the NLODEs into two systems of equations. Moreover, the qualitative dynamics of the time-varying dynamical system are examined by employing chaos theory. We explore the intricacies of 3 D and 2 D phase portraits, time series, and Poincar & eacute; maps as powerful tools for detecting the elusive nature of chaos in selfgoverning dynamic systems. Sensitivity and stability analysis is also studied by using the various initial conditions, revealing the remarkable stability of the system under investigation. The system's stability is confirmed by the fact that even small changes to the initial conditions have no appreciable effect on the solutions. The results of this study are novel and valuable for further investigation of equations which are helpful for the incoming researchers.

Transportation relies heavily on petroleum products, forcing the adoption of alternative energy sources like hydrogen. Hydrogen is considered the cleanest fuel for the twenty-first century due to its water-based combustion and no CO2 emissions. However, challenges persist in production, utilization, and storage; employing composite material-based high-pressure storage vessels is increasing in the hydrogen storage sector. The paper analyzes the impact of the winding angles on the mechanical performance of the filament wound Type 4 composite pressure vessels (CPVs) for compressed hydrogen gas storage at 70 MPa. This work examines the individual winding angles and combined angles winding patterns to promote the efficiency of Type 4 CPVs by achieving maximum burst pressure, ensuring safe burst mode, and reducing CPV weight by applying maximum principal stress theory with the aid of the Ansys ACP Prep/Post and static modules. The weight and burst pressure of CPVs are significantly influenced by fiber orientation; a combination of positive and negative helical winding angles promotes higher burst pressure at a lower weight. A hoop angle and intermediate helical angles can be combined to create high-efficiency CPVs that provide mechanical performance comparable to that of a combination of high and low helical angles. Finally, a one-factor-at-a-time (OAT) sensitivity analysis was performed to determine how the winding angle and the thicknesses of layers affect the CPVs' performance. It was found that the performance of the CPVs is significantly influenced by the thicknesses of the wound layers.

Facing the dual challenges of environmental impact and the finite nature of fossil fuels, the shift toward sustainable energy sources is imperative. Geothermal energy, a renewable and underutilized resource, offers a promising alternative. This study ventures into a novel domain, exploring the integration of trans-critical CO2 (tCO(2)) cycle with a double-flash geothermal (DFG) cycle, an area not extensively covered in existing research. This study aims to develop a recovery system that utilizes a tCO(2) cycle powered by a DFG cycle. A crucial part of this study involves conducting a sensitivity analysis to evaluate the system's energy and exergy performance. This analysis focuses on understanding the impact of changes in key system design parameters, such as energy efficiency, exergy efficiency, net power output, and total exergy destruction rate, to determine the optimal values for these parameters. The results indicate that the recovery system shows a significant improvement of 22% in energy efficiency over the basic cycle. However, there is a decrease in exergy efficiency in the recovery system, which represents a 5.26% decline. As a result, this study paves the way for innovative approaches in geothermal energy use, suggesting potential breakthroughs in renewable technologies.

The current paradigm for the transmission expansion planning (TEP) problem follows an open-loop (OL) approach. This approach first executes an accuracy-oriented forecasting method to predict the available wind power, demand, and spinning and non-spinning reserve (SR and NSR) requirements. Afterward, the TEP problem is solved based on these predictions. However, this OL model does not necessarily obtain a better expansion plan in terms of the real system cost (RSC) against the actual realization of wind and demand. This article contributes to the existing literature by developing a closed-loop (CL) strategy for the TEP problem using a cost-oriented prediction method. In this novel cost-oriented methodology, the RSC assesses the forecasting data quality rather than the statistical indices (accuracy-oriented). In this regard, a bilevel programming model is established to train the predicted data to minimize the system RSC. The upper level trains the predictors for wind power and SR/NSR requirements, while the two lower-level problems formulate the unit commitment (UC)-based TEP and the economic redispatch (ERD) problem. The bilevel programming that contains integer variables at both levels is solved by the reformulation and decomposition (R&D) technique. The numerical results on the IEEE 118-bus grid illustrate the cost-effective advantages of the CL-TEP method.