Enhanced thermal conductivity of plasma generated ZnO-MgO based hybrid nanofluids: An experimental study.

Hybrid nanofluids (HNFs) of metallic oxide-based nanoparticles (NPs) have been prepared in different basefluids (BFs) employing the thermal plasma technique. NPs of ZnO-MgO were directly dispersed into pristine coolant, engine oil, distilled water (DW), and coconut oil. Plasma was generated between two identical electrodes applying 8.0 kV at the ambient conditions and proved economically viable in preparing stable HNFs. X-ray Diffractometry (XRD) showed ZnO and MgO NPs possessed hexagonal and cubic crystal structures, respectively. The band gap is calculated through UV-visible spectroscopy. The thermal conductivity (TC) of the HNFs has been measured using a thermal conductivity analyzer based on the transient hot wire method. The band gaps of pristine coolant and its HNFs were obtained to be 3.35 eV and 3.33 eV, respectively. In engine oil and its HNFs, band gaps of 3.16 eV and 3.02 eV have been extracted. There appears to be a slight reduction in band gap for coolant and engine oil-based HNFs. The band gap value of coconut oil-based HNFs was 4.05 eV, which showed a higher value than the pristine coconut oil-based HNFs (3.95 eV). The band gap calculated in the case of DW-based HNFs was 3.79 eV. TC of HNFs with volume concentration of 0.019 % for DW, 0.020 % for coolant, 0.016 % for engine oil, and 0.017 % for coconut oil were tested between 20 and 60 °C. An increase in TC was observed with the rise in temperature of the HNFs. Maximum increment in TC was observed at 60 °C for coolant-based HNFs, which was 19 %, followed by DW (18%), coconut oil (18%), and engine oil (16%), respectively. DW-based HNFs can be used as a coolant and optical filter for optoelectronics devices like photovoltaic cells for better performance. The study underscores precise control of NPs size as pivotal for band gap influence. HNFs hold promise as the next-gen heat transfer fluids (HTFs), revolutionizing thermal conductivity across industries. This research lays a firm foundation for plasma-synthesized HNFs' application in enhanced heat transfer and optoelectronic devices. Coolant-based HNFs excel in thermal conductivity, addressing heat transfer challenges.

Maximizing biodiesel yield of a non-edible chinaberry seed oil via microwave assisted transesterification process using response surface methodology and artificial neural network techniques.

In this study, the non-edible Chinaberry Seed Oil (CBO) is converted into biodiesel using microwave assisted transesterification. The objective of this effort is to maximize the biodiesel yield by optimizing the operating parameters, such as catalyst concentration, methanol-oil ratio, reaction speed, and reaction time. The designed setup provides a controlled and effective approach for turning CBO into biodiesel, resulting in encouraging yields and reduced reaction times. The experimental findings reveal the optimal parameters for the highest biodiesel yield (95 %) are a catalyst concentration of 1.5 w/w, a methanol-oil ratio of 6:1 v/v, a reaction speed of 400 RPM, and a reaction period of 3 min. The interaction of the several operating parameters on biodiesel yield has been investigated using two methodologies: Response Surface Methodology (RSM) and Artificial Neural Network (ANN). RSM provides better modeling of parameter interaction, while ANN exhibits lower comparative error when predicting biodiesel yield based on the reaction parameters. The percentage improvement in prediction of biodiesel yield by ANN is found to be 12 % as compared to RSM. This study emphasizes the merits of both the approaches for biodiesel yield optimization. Furthermore, the scaling up this microwave-assisted transesterification system for industrial biodiesel production has been proposes with focus on its economic viability and environmental effects.

Assessing the potential of GHG emissions for the textile sector: A baseline study.

The carbon footprint (CFP) is a measure of greenhouse gases (GHGs) emitted throughout the lifecycle of a product or activity, while the energy footprint (EFP) and water footprint (WFP) measure energy and water consumption, respectively. These footprints are essential for managing emissions and consumption and promoting low-carbon consumption. A carbon labeling scheme could help consumers make informed choices. Asia is a major textile producer and consumer, so studying textiles' carbon, energy, and water footprints is essential for managing domestic emissions, energy and water consumption, and international trade negotiations. This paper presents a method and framework for assessing CFP, EFP, and WFP at the product level and calculates the footprints for textile products. The results show that the total CFP of all textile products produced is 42,624.12 MT CO2e, with indirect emissions contributing significantly more than direct emissions. The total EFP is 248.38 PJ, with electricity consumption being the main contributor, while the total WFP is 80.71 billion liters. The spinning stage of production has the highest CFP and EFP, and energy consumption is the main contributor to all footprints. These results can help compare different products and reduce the footprints of the textile sector.

Cattaneo-Christov heat flow model at mixed impulse stagnation point past a Riga plate: Levenberg-Marquardt backpropagation method.

Applications of artificial intelligence (AI) via soft computing procedures have attracted the attention of researchers due to their effective modeling, simulation procedures, and detailed analysis. In this article, the designing of intelligence computing through a neural network that is backpropagated with the Levenberg-Marquardt method (NN-BLMM) to study the Cattaneo-Christov heat flow model at the mixed impulse stagnation point (CCHFM-MISP) past a Riga plate is investigated. The original model CCHFM-MISP in terms of PDEs is converted into non-linear ODEs through suitable similarity variables. A data set is generated for all scenarios of CCHFM-MISP through Lobatto IIIA numerical solver by varying Hartman number, velocity ratio parameter, inverse Darcy number, mixed impulse variable, non-dimensional constraint, Eckert number, heat generation variable, Prandtl number, thermal relaxation variable. To find the physical impacts of parameters of interest associated with the presented fluidic system CCHFM-MISP, the approximate solution of NN-BLMM is carried out by performing training (80 %), testing (10 %), and validation (10 %), and then the results are equated with the reference data to ensure the perfection of the proposed model. Through MSE, state transition, error histogram, and regression analysis, the outcomes of NN-BLMM are presented and analyzed. The graphical illustration and numerical outcomes confirm the authentication and effectiveness of the solver. Moreover, mean square errors for validation, training and testing data points along with performance measures lie around 10-10 and the solution plots generated through deterministic (Lobatto IIIA) approach and stochastic numerical solver are matching up to 10-6, which surely validate the solver NN-BLMM. The outcomes of M and B on velocity present the similar impacts. The velocity of material particles decreases under Da while, it increases through velocity ratio and magnetic parameters.

Developments in mineral carbonation for Carbon sequestration.

Mineral technology has attracted significant attention in recent decades. Mineral carbonation technology is being used for permanent sequestration of CO2 (greenhouse gas). Temperature programmed desorption studies showed interaction of CO2 with Mg indicating possibility of using natural feedstocks for mineral carbonation. Soaking is effective to increase yields of heat-activated materials. This review covers the latest developments in mineral carbonation technology. In this review, development in carbonation of natural minerals, effect of soaking on raw and heat-activated dunite, increasing reactivity of minerals, thermal activation, carbonations of waste materials, increasing efficiency of carbonation process and pilot plants on mineral carbonation are discussed. Developments in carbonation processes (single-stage carbonation, two-stage carbonation, acid dissolution, ph swing process) and pre-process and concurrent grinding are elaborated. This review also highlights future research required in mineral carbonation technology.

A comprehensive study on the performance and emission analysis in diesel engine via optimization of novel ternary fuel blends: Diesel, manganese, and diethyl ether.

Ecosystem degradation and fossil fuel depletion are the two foremost concerns to look for alternative fuels. Rapid population growth is primarily accountable for higher consumption of fossil fuel sources, although engine technology is achieving milestones in terms of fuel efficiency and lower exhaust emissions in order to contribute towards a sustainable environment. The main root cause of global warming is carbon dioxide emissions; therefore, it is imperative to assess the impact of alternative fuels in diesel engines with an aim to minimize carbon emissions. A current study deals with the reduction of carbon emissions and improvement of efficiency through addition of manganese nano-additive to di-ethyl ether and diesel fuel blend in particulate form. Fuel blends were formed by adding various proportions of manganese to high-speed diesel fuel and stirring the mixture while heating it for 10 min. The blends were then tested in diesel engines at two distinct loads and five engine speed ranges. Emission analyzer was used to ascertain the CO2 output of engine. At higher loads for 10 % diethyl ether in diesel, the increase in brake thermal efficiency was 24.19, 28.17 and 26.86 % when the manganese amount in blend was changed as 250 mg, 375 mg and 500 mg respectively. On the other side CO2 emissions increase by 11.57, 30.52 and 20.33 % for manganese concentrations of 250 mg, 375 mg and 500 mg respectively. Analysis performed with Design Expert 13 showed that the desirability was 0.796 for a blend of 375 mg manganese at 1300 rpm and 4500 W load with 33.0611 % BTE, 334.011kg/kWh BSFC, 67.8821Nm torque, and 6.072 % CO2. Therefore, it can be deduced that manganese nanoparticle blends improved engine performance but CO2 emissions also increase which can be responsible for global warming and it should be reduced through catalytic converters.

Investigation of tribo-mechanical performance of alkali treated rice-husk and polypropylene-random-copolymer based biocomposites.

This study was based on the experimental performance evaluation of a wood polymer composite (WPC) that was synthesized by incorporating untreated and treated rice husk (RH) fibers into a polypropylene random copolymer matrix. The submicron-scale RH fibers were alkali-treated to modify the surface and introduce new functional groups in the WPC. A compatibilizer (maleic anhydride) and a thermos-mechanical properties modifier (polypropylene grafted with 30 % glass fiber) were used in the WPC. The effects of untreated and treated RH on the WPC panels were studied using FESEM, FTIR, and microscope images. A pin-on-disk setup was used to investigate the bulk tribological properties of PPRC and WPC. The complex relationship between the friction coefficient of different loading of RH fibers in the WPC, as a function of sliding distance, was analyzed along with the temperature and morphology of the surface. It was observed that untreated RH acted as a friction modifier, while treated RH acted as a solid lubricant. Microhardness was calculated using the QCSM module on nanoindentation. It was found that untreated RH led to an increase in microhardness, while treated RH caused a decrease in hardness compared to PPRC.

Unsteady ternary hybrid-nanofluid flow over an expanding/shrinking cylinder with multiple slips: a Yamada-Ota model implementation.

The primary objective of this investigation is to examine the thermal state of an unsteady ternary hybrid-nanofluid flow over an expanding/shrinking cylinder. The influence of radiation along with a non-uniform thermal source/sink is taken into account to expedite heat distribution. Multiple slips are considered at the cylinder interface. The mathematical model is simplified by incorporating appropriate transformations. A numerical solution is obtained using the bvp4c algorithm. The flow characteristics and behavior of the trihybrid nanoliquid exhibit significant changes when the cylinder expands or contracts. The effects of various emerging parameters are analyzed using graphical representations. The velocity field shows an opposite trend when the unsteadiness and mass transfer parameters are increased. The thermal field improves with higher values of the non-uniform source/sink parameter but deteriorates with an increase in the thermal slip parameter. The drag force increases with higher values of the unsteadiness parameter, while it decreases with amplified values of the mass suction and velocity slip parameters. A strong correlation is observed with previous studies which validates and strengthens the credibility of the present analysis.

Comparative Study of Soft Computing and Metaheuristic Models in Developing Reduced Exhaust Emission Characteristics for Diesel Engine Fueled with Various Blends of Biodiesel and Metallic Nanoadditive Mixtures: An ANFIS-GA-HSA Approach.

Since the discovery of petrol-based products, a surge in energy-requiring equipment has been established across the world. Recent depletion of the existing crude oil resources has motivated researchers to opt for and analyze potential fuels that could potentially provide a cost-effective and sustainable solution. The current study selects a waste plant known as Eichhornia crassipes through which biodiesel is generated, and its blends are tested in diesel engines for feasibility. Different models using soft computing and metaheuristic techniques are employed for the accurate prediction of performance and exhaust characteristics. The blends are further mixed with nanoadditives, thereby exploring and comparing the changes in performance characteristics. The input attributes considered in the study comprise engine load, blend percentage, nanoparticle concentration, and injection pressure, while the outcomes are brake thermal efficiency, brake specific energy consumption, carbon monoxide, unburnt hydrocarbon, and oxides of nitrogen. Models were further ranked and chosen based on their set of attributes using the ranking technique. The ranking criteria for models were based on cost, accuracy, and skill requirement. The ANFIS harmony search algorithm (HSA) reported a lower error rate, while the ANFIS model reported the lowest cost. The optimal combination achieved was 20.80 kW, 2.48047, 150.501 ppm, 4.05025 ppm, and 0.018326% for brake thermal efficiency (BTE), brake specific energy consumption (BSEC), oxides of nitrogen (NOx), unburnt hydrocarbons (UBHC), and carbon monoxide (CO), respectively, thereby furnishing better results than the adaptive neuro-fuzzy interface system (ANFIS) and the ANFIS-genetic algorithm model. Henceforth, integrating the results of ANFIS with an optimization technique with the harmony search algorithm (HSA) yields accurate results but at a comparatively higher cost.

Response Surface Methodology Based Optimization of Test Parameter in Glass Fiber Reinforced Polyamide 66 for Dry Sliding, Tribological Performance.

The tribological performance of a glass fiber reinforced polyamide66 (GFRPA66) composite with varying fiber weight percentage (wt.%) [30 wt.% and 35 wt.%] is investigated in this study using a pin-on-disc tribometer. GFRPA66 composite specimens in the form of pins with varying percentages of fiber viz., 30 wt.% and 35 wt.% are fabricated by an injection molding process. Tribological performances, such as coefficient of friction (COF) and the specific wear rate (SWR), are investigated. The factors affecting the wear of GFRPA66 composites [with 30 wt.% and 35 wt.% reinforcements] are identified based on the process parameters such as load, sliding velocity, and sliding distance. Design Expert 13.0 software is used for the experimental data analysis, based on the design of experiments planned in accordance with the central composite design (CCD) of the response surface methodology (RSM) technique. The significance of the obtained results are analyzed using analysis of variance (ANOVA) techniques. To attain minimum SWR and COF, the wear performance is optimized in dry sliding conditions. The analysis of experimental data revealed that SWR and COF increased with increasing load, sliding velocity, and sliding distance for GFRPA66 [30 wt.%], but decreased with increasing polyamide weight percentage. The SWR for a maximum load of 80 N, and for a sliding velocity of 0.22 m/s, and a sliding distance of 3500 m for GFRPA66 composite specimens with 30 wt.% reinforcements are found to be 0.0121 m3/Nm, while the SWR for the same set of parameters for GFRPA66 composite specimens with 35 wt.% reinforcements are found to be 0.0102 m3/Nm. The COF for the GFRPA66 composite specimens with 30 wt.% reinforcements for the above set of parameters is found to be 0.37, while the GFRPA66 composite specimens with 35 wt.% reinforcements showed significant improvement in wear performance with a reduction in COF to 0.25. Finally, using a scanning electron microscope (SEM), the worn surfaces of the GFRPA66 are examined and interpreted.

Magnetic Dipole and Thermophoretic Particle Deposition Impact on Bioconvective Oldroyd-B Fluid Flow over a Stretching Surface with Cattaneo-Christov Heat Flux.

This study emphasizes the performance of two-dimensional electrically non-conducting Oldroyd-B fluid flowing across a stretching sheet with thermophoretic particle deposition. The heat and mass transfer mechanisms are elaborated in the presence of a magnetic dipole, which acts as an external magnetic field. The fluid possesses magnetic characteristics due to the presence of ferrite particles. The gyrotactic microorganisms are considered to keep the suspended ferromagnetic particles stable. Cattaneo-Christov heat flux is cogitated instead of the conventional Fourier law. Further, to strengthen the heat transfer and mass transfer processes, thermal stratification and chemical reaction are employed. Appropriate similarity transformations are applied to convert highly nonlinear coupled partial differential equations into non-linear ordinary differential equations (ODEs). To numerically solve these ODEs, an excellent MATLAB bvp4c approach is used. The physical behavior of important parameters and their graphical representations are thoroughly examined. The tables are presented to address the thermophoretic particle velocity deposition, rate of heat flux, and motile microorganisms' density number. The results show that the rate of heat transfer decreases as the value of the thermal relaxation time parameter surges. Furthermore, when the thermophoretic coefficient increases, the velocity of thermophoretic deposition decreases.

Entropy Generation Analysis of Peristaltic Flow of Nanomaterial in a Rotating Medium through Generalized Complaint Walls of Micro-Channel with Radiation and Heat Flux Effects.

This study discusses entropy generation analysis for a peristaltic flow in a rotating medium with generalized complaint walls. The goal of the current analysis is to understand the fluid flow phenomena particular to micro devices. Nano materials with a size less than 100 nm have applications in micro heat exchangers to cool electronic circuits, blood analyzers, biological cell separations, etc. For this study, we considered the effects of radiation, viscous dissipation and heat flux on the flow of nanomaterial inside a cylindrical micro-channel. To investigate the slip effects on the flow, the second order slip condition for axial velocity, the first order slip condition for secondary velocity and the thermal slip conditions were used. The flow was governed by partial differential equations (PDE's), which were turned into a system of coupled ordinary differential equations (ODE's) that were highly non-linear and numerically solved using the NDSolve command in Mathematica. The impacts of different involved parameters on the flow field were investigated with the aid of graphical illustrations. Entropy generation and the Bejan number were given special attention, and it was found that they decreased as the Hartman number, rotation, and radiation parameters increased.

Merits and Limitations of Mathematical Modeling and Computational Simulations in Mitigation of COVID-19 Pandemic: A Comprehensive Review.

Mathematical models have assisted in describing the transmission and propagation dynamics of various viral diseases like MERS, measles, SARS, and Influenza; while the advanced computational technique is utilized in the epidemiology of viral diseases to examine and estimate the influences of interventions and vaccinations. In March 2020, the World Health Organization (WHO) has declared the COVID-19 as a global pandemic and the rate of morbidity and mortality triggers unprecedented public health crises throughout the world. The mathematical models can assist in improving the interventions, key transmission parameters, public health agencies, and countermeasures to mitigate this pandemic. Besides, the mathematical models were also used to examine the characteristics of epidemiological and the understanding of the complex transmission mechanism. Our literature study found that there were still some challenges in mathematical modeling for the case of ecology, genetics, microbiology, and pathology pose; also, some aspects like political and societal issues and cultural and ethical standards are hard to be characterized. Here, the recent mathematical models about COVID-19 and their prominent features, applications, limitations, and future perspective are discussed and reviewed. This review can assist in further improvement of mathematical models that will consider the current challenges of viral diseases.

Optimization of Thermal and Structural Design in Lithium-Ion Batteries to Obtain Energy Efficient Battery Thermal Management System (BTMS): A Critical Review.

Covid-19 has given one positive perspective to look at our planet earth in terms of reducing the air and noise pollution thus improving the environmental conditions globally. This positive outcome of pandemic has given the indication that the future of energy belong to green energy and one of the emerging source of green energy is Lithium-ion batteries (LIBs). LIBs are the backbone of the electric vehicles but there are some major issues faced by the them like poor thermal performance, thermal runaway, fire hazards and faster rate of discharge under low and high temperature environment,. Therefore to overcome these problems most of the researchers have come up with new methods of controlling and maintaining the overall thermal performance of the LIBs. The present review paper mainly is focused on optimization of thermal and structural design parameters of the LIBs under different BTMSs. The optimized BTMS generally demonstrated in this paper are maximum temperature of battery cell, battery pack or battery module, temperature uniformity, maximum or average temperature difference, inlet temperature of coolant, flow velocity, and pressure drop. Whereas the major structural design optimization parameters highlighted in this paper are type of flow channel, number of channels, length of channel, diameter of channel, cell to cell spacing, inlet and outlet plenum angle and arrangement of channels. These optimized parameters investigated under different BTMS heads such as air, PCM (phase change material), mini-channel, heat pipe, and water cooling are reported profoundly in this review article. The data are categorized and the results of the recent studies are summarized for each method. Critical review on use of various optimization algorithms (like ant colony, genetic, particle swarm, response surface, NSGA-II, etc.) for design parameter optimization are presented and categorized for different BTMS to boost their objectives. The single objective optimization techniques helps in obtaining the optimal value of important design parameters related to the thermal performance of battery cooling systems. Finally, multi-objective optimization technique is also discussed to get an idea of how to get the trade-off between the various conflicting parameters of interest such as energy, cost, pressure drop, size, arrangement, etc. which is related to minimization and thermal efficiency/performance of the battery system related to maximization. This review will be very helpful for researchers working with an objective of improving the thermal performance and life span of the LIBs.

Heat Transfer Characteristics of Fullerene and Titania Nanotube Nanofluids under Agitated Quench Conditions.

Distilled water and aqueous fullerene nanofluids having concentrations of 0.02, 0.2, and 0.4 vol % and titania (titanium dioxide, TiO2) nanofluids of 0.0002, 0.002, and 0.02 vol % were analyzed for heat transfer characteristics. Quenching mediums were stirred at impeller speeds of 0, 500, 1,000, and 1,500 RPMs in a typical Tensi agitation system. During the quenching process, a metal probe made of ISO 9950 Inconel was used to record the temperature history. The inverse heat conduction method was used to calculate the spatial and temporal heat flux. The nanofluid rewetting properties were measured and matched to those of distilled water. The maximum mean heat flux was 3.26 MW/m2, and the quickest heat extraction was 0.2 vol % fullerene nanofluid, according to the results of the heat transfer investigation.

Shrinkage Study and Strength Aspects of Concrete with Foundry Sand and Coconut Shell as a Partial Replacement for Coarse and Fine Aggregate.

The demand for natural aggregates (river sand) is increasing day by day, leading to the destruction of the environment, a burden that will be passed on to young people. Further, wastes from various industries are being dumped in landfills, which poses serious environmental problems. In order to ensure sustainability, both the issues mentioned above can be solved by utilizing industrial waste as aggregate replacement in the concrete construction industry. This research is done to find out the results using two substances viz., waste foundry sand (WFS) and coconut shell (CS) substitute for river sand and coarse aggregate. Many researchers have found the maximum benefits of substituted substances used in cement, which has material consistency. This current observation explores these strong waste properties of waste-infused concrete and cement, which experience shrinkage from drying out. The replacement levels for waste foundry sand were varied, between 10%, 20%, and 30%, and for CS, it was 10% and 20%. The experimental outcomes are evident for the strength, which increases by using WFS, whereas the strength decreases by increasing the CS level. The concrete that experiences shrinkage from drying out is included in the waste material, showing a higher magnitude of drying shrinkage than conventional concrete.

Partial velocity slip effect on working magneto non-Newtonian nanofluids flow in solar collectors subject to change viscosity and thermal conductivity with temperature.

Solar thermal collectors distribute, capture, and transform the solar energy into a solar thermal concentration device. The present paper provides a mathematical model for analyzing the flow characteristics and transport of heat to solar collectors (SCs) from non-Newtonian nanofluids. The non-Newtonian power-law scheme is considered for the nanofluid through partial slip constraints at the boundary of a porous flat surface. The nanofluid is assumed to differ in viscosity and thermal conductivity linearly with temperature changes and the magnetic field is appliqued to the stream in the transverse direction. The method of similarity conversion is used to convert the governing structure of partial differential formulas into the system of ordinary differential ones. Using the Keller box procedure, the outcoming ordinary differential formulas along with partial slip constraints are numerically resolved. A discussion on the flowing and heat transport characteristics of nanofluid influenced by power law index, Joule heating parameter, MHD parameter and slip parameters are included from a physical point of view. Comparison of temperature profiles showed a marked temperature increase in the boundary layer due to Joule heating. The thickness of the motion boundary-layer is minimized and the transport of heat through boundary-layer is improved with the partial slip velocity and magnetic parameters rising. Finally, With an increase in the Eckert number, the distribution of temperature within boundary layer is increased.

Features of Cu and TiO2 in the flow of engine oil subject to thermal jump conditions.

The recent work investigates the heat transfer attributes in the flow of engine oil which comprises of nano-particles such as Cu and TiO2. The performance of Copper and Titanium oxide is over looked in the flow of engine oil. The energy equation is amended by the features of thermal radiation, viscous dissipation, and heat generation. The mathematical model signifies the porosity, entropy generation and moving flat horizontal surface with the non-uniform stretching velocity. Quasi-linearization, which is a persuasive numerical technique to solve the complex coupled differential equations, is used to acquire the numerical solution of the problem. Flow and heat transfer aspects of Cu-TiO2 in the flow are examined against the preeminent parameters. The flow is significantly affected by the thermal jump conditions and porous media. It is observed here that the temperature as well as heat transport rate is reduced with the effect of involved preeminent parameters. However, such fluids must be used with caution in applications where a control on the heat transfer is required. We may conclude that the recent study will provide assistance in thermal cooling systems such as engine and generator cooling, nuclear system cooling, aircraft refrigeration system, and so forth.

The intelligent networks for double-diffusion and MHD analysis of thin film flow over a stretched surface.

This study presents a novel application of soft-computing through intelligent, neural networks backpropagated by Levenberg-Marquardt scheme (NNs-BLMS) to solve the mathematical model of unsteady thin film flow of magnetized Maxwell fluid with thermo-diffusion effects and chemical reaction (TFFMFTDECR) over a horizontal rotating disk. The expression for thermophoretic velocity is accounted. Energy expression is deliberated with the addition of non-uniform heat source. The PDEs of mathematical model of TFFMFTDECR are transformed to ODEs by the application of similarity transformations. A dataset is generated through Adams method for the proposed NNs-BLMS in case of various scenarios of TFFMFTDECR model by variation of rotation parameter, magnetic parameter, space dependent heat sink/source parameter, temperature dependent heat sink/source parameter and chemical reaction controlling parameter. The designed computational solver NNs-BLMS is implemented by performing training, testing and validation for the solution of TFFMFTDECR system for different variants. Variation of various physical parameters are designed via plots and explain in details. It is depicted that thin film thickness increases for higher values of disk rotation parameter, while it diminishes for higher magnetic parameter. Furthermore, higher values of Dufour number and the corresponding diminishing values of Soret number causes enhancement in fluid temperature profile. Further the effectiveness of NNs-BLMS is validated by comparing the results of the proposed solver and the standard solution of TFFMFTDECR model through error analyses, histogram representations and regression analyses.

Influence of Heat Treatment and Reinforcements on Tensile Characteristics of Aluminium AA 5083/Silicon Carbide/Fly Ash Composites.

The effect of reinforcements and thermal exposure on the tensile properties of aluminium AA 5083-silicon carbide (SiC)-fly ash composites were studied in the present work. The specimens were fabricated with varying wt.% of fly ash and silicon carbide and subjected to T6 thermal cycle conditions to enhance the properties through "precipitation hardening". The analyses of the microstructure and the elemental distribution were carried out using scanning electron microscopic (SEM) images and energy dispersive spectroscopy (EDS). The composite specimens thus subjected to thermal treatment exhibit uniform distribution of the reinforcements, and the energy dispersive spectrum exhibit the presence of Al, Si, Mg, O elements, along with the traces of few other elements. The effects of reinforcements and heat treatment on the tensile properties were investigated through a set of scientifically designed experimental trials. From the investigations, it is observed that the tensile and yield strength increases up to 160 °C, beyond which there is a slight reduction in the tensile and yield strength with an increase in temperature (i.e., 200 °C). Additionally, the % elongation of the composites decreases substantially with the inclusion of the reinforcements and thermal exposure, leading to an increase in stiffness and elastic modulus of the specimens. The improvement in the strength and elastic modulus of the composites is attributed to a number of factors, i.e., the diffusion mechanism, composition of the reinforcements, heat treatment temperatures, and grain refinement. Further, the optimisation studies and ANN modelling validated the experimental outcomes and provided the training models for the test data with the correlation coefficients for interpolating the results for different sets of parameters, thereby facilitating the fabrication of hybrid composite components for various automotive and aerospace applications.