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Abstract The Ilesha and Ado-Ekiti areas are characterized by extensive mineral potentials, including both metallic and non-metallic resources. To detect the probable zones of mineralization, airborne magnetic and radiometric data were applied to map lineaments and lithological components. o generate comprehensive structural maps, the Centre for Exploration Targeting (CET) grid analysis tool was applied to total magnetic intensity data upward continued to 100 m, First Vertical Derivative (FVD), Second Vertical Derivative (SVD), Analytic Signal (ASIG), Tilt Angle Derivative (TAD), and Total Horizontal Derivative (THD) grids. The structural maps strongly correlated with each other were observed to be characterized by polyphase deformations, and oriented in the NE–SW, N–S, and E–W directions. The dominant NE–SW trend is related to the Ifewara-Zungeru zone caused by the Pan-African Orogeny and other tectonic events. Distinct lithological areas were mapped using radiometric anomalies. A major lineament that falls within the potassium-rich and magnetically concentrated zone indicated possible igneous related gold site. In general, the lineaments that have been detected are probable hosts for metallogenic minerals. Furthermore, the depth estimate involving the Source Parameter Imaging (SPI) and Standard Euler Deconvolution (SED) detected thin sedimentation (≤ 482 m).

Convexity and fractional integral operators are closely related due to their fascinating properties in the mathematical sciences. In this article, we first establish an identity for the modified Atangana-Baleanu (MAB) fractional integral operators. Using this identity, we then apply Jensen integral inequality, Young's inequality, power-mean inequality, and Hölder inequality to prove several new generalizations of Ostrowski type inequality for the convexity of |ℵ|. From the primary findings, we also deduced a few new special cases. The results of this investigation are expected to indicate new advances in the study of fractional integral inequalities.

This study delineates the development of a solar energy system that leverages concentrated solar power (CSP) technology to supply both electricity and potable water for residential applications. The proposed thermal architecture uniquely integrates heliostat solar fields with a dual-loop power generation cycle, augmented by a seawater desalination system that employs reverse osmosis (RO) membranes. To bolster electricity production, a thermoelectric generator (TEG) has been incorporated into the system's design framework. A comprehensive analysis of the system has been performed, encompassing thermodynamic and economic evaluations. Furthermore, a parametric analysis has been executed to investigate the effects of critical parameters on the system's operational efficiency. The efficacy of the system was rigorously assessed through a case study that examined its capabilities for daily production outputs. This research, grounded in the analytical projections from Saudi Arabia and the favorable environmental conditions characteristic of the region, explores the operational performance of the system within this specific geographical context. The primary objective of this inquiry is to determine the ideal operational parameters by employing multi-criteria optimization methods tailored to the established system. Variations in compressor pressure ratios were found to significantly affect the performance of the Brayton cycle and the exergetic efficiency of the system, with optimal economic efficiency being realized at a specific pressure ratio. Furthermore, increasing the inlet temperatures in the organic Rankine cycle has been shown to improve system efficiency up to a certain limit, beyond which potential reliability issues could arise. The case study demonstrated that electricity generation peaks during the summer months, particularly in June, aligning with a high volume of freshwater production totaling 264,530 m³. The optimization efforts achieved an exergetic efficiency of 17.69 % and an overall cost of $359.58 per hour.

This study investigates tumor density dynamics through computational modeling, focusing on key parameters: proliferation rate m, diffusion coefficient τ, and additional factors c and κ. Systematic exploration reveals nuanced interactions shaping tumor growth, regression, and dispersal. Higher m values accelerate proliferation, while increased τ facilitates dispersal. Parameters c and κ further refine our understanding of tumor behavior. These insights inform the development of computational models for oncological research, with implications for targeted therapeutic interventions. The aim can be realized by utilizing the propose Finite Volume Method to develop a representation of cancer models and improve the efficiency of their simulations. Furthermore, the numerical results will be validated by comparing them with the Tanh-Coth method formulation to assess the model's accuracy and efficiency. The findings suggest that accurate modeling of tumor dynamics can aid in predicting tumor progression and response to treatments, ultimately contributing to personalized medicine and more effective therapeutic strategies. Phase plane analysis shows the schemes are conditionally stable and accurate to the fourth order in time. The method demonstrates exceptional agreement and error-free findings compared to 3D plots, underscoring its potential utility in clinical and research settings.

The Deviation Closeness Analysis (DCA) quantifies the disparities between approximated and the invariant curves, using advanced Model Reduction Techniques (MRTs). The MRTs, Spectral Quasi Equilibrium Manifold (SQEM) and Intrinsic Low Dimensional Manifold (ILDM) are focusing on assessing the closeness between model predictions and actual data points applied to Nitrogen Oxides (NOx) and Ozone (O3) simulation in the atmosphere, crucial for air quality regulation and smog formation. The novel contribution in this article is to perform the comparative analysis between the reduced model predictions and actual data points through DCA for NOx and O3 within complex reaction mechanisms impacting the ozone layer. Further, Local sensitivity analysis identifies influence of species and parameters in given chemical model. Findings indicate achievable invariant regions and influential parameters for NOx and O3, presented through simulations and tabular comparisons.

As the global energy demand continues to rise, there is an urgent need to improve the efficiency and sustainability of power generation systems. This study integrated a modified supercritical carbon dioxide (S-CO2) and multi-effect desalination (MED) units to recover residual heat from a gas turbine cycle (GTC) in two stages, significantly enhancing electricity production while reducing the environmental footprint of the GTC. The significance of this study lies in its comprehensive approach, combining thermodynamic, environmental, and thermoeconomic analyses alongside thorough sensitivity evaluations. A triple optimization framework was implemented to optimize the system's performance, focusing on key metrics such as exergy efficiency, CO2 reduction rates, and levelized energy cost, utilizing the NSGA-II and the TOPSIS decision-making method in MATLAB software. Economic viability was assessed through a net present value (NPV) analysis, demonstrating substantial profitability. Finally, a comparison study of the devised system CO2 emissions rate was performed for different renewable energy sources. A specific application of the devised system is its capacity to generate 1.415 m³/h of distilled water while generating 1441 kW of electricity. Sensitivity analysis identified the combustion chamber temperature as the most critical design parameter, with a sensitivity index of 0.328. The optimum economic indicators showed marked improvement, with the NPV increasing from 2.371 M$ to 10.75 M$ and the payback period decreasing from 13.28 years to 7.18 years.

This research investigates the thermohydraulic performance and exergy destruction associated with the flow of supercritical carbon dioxide (sCO2) within spirally coiled mini tubes. The study examines the impact of different cross-sectional geometries. The primary objective of the study is to examine the impact of critical parameters, including shape, hydraulic diameter, inlet temperature, mass flux, and operating pressure, on important variables such as friction factor, heat transfer coefficient, and exergy efficiency. The computational simulation employs the RNG k−ε model. The Coupled algorithm was utilized for the determination of velocity and pressure fields, utilizing second-order discretization for domain partitioning and first-order discretization for other terms. The carbon dioxide (CO2) was conceptualized as a compressible gas with complex thermophysical attributes that are contingent upon variations in temperature and pressure. The thermophysical properties of carbon dioxide are evaluated within a defined range of operating conditions (298. 15 K

This study offers an in-depth thermodynamic analysis and optimization of an integrated renewable energy system that merges a double-flash geothermal system with a transcritical carbon dioxide Rankine cycle, utilizing machine learning algorithms. The innovative design aims to maximize the concurrent generation of heat and electricity, ultimately benefiting environmental sustainability and energy security. By employing regression machine learning algorithms, the research evaluates and enhances system performance, achieving remarkable R-squared accuracy levels of 98.86 % for heating output and 99.89 % for power output predictions. The thermodynamic modeling, which has been validated against recognized benchmarks, confirms the accuracy of the system's design. Optimization findings indicate that operating pressures between 840 and 870 kPa and pressure ratios of 1.56–1.60 deliver optimal outputs, with power production between 2582 and 2585 kW and heating output ranging from 12260 to 12280 kW. The system reaches its maximum performance at a pressure of 850 kPa and a pressure ratio of 1.57, resulting in a power output of 2583.97 kW and a heating output of 12279.3 kW. These results highlight the potential of combining advanced thermodynamic systems with machine learning methodologies to improve the efficiency and effectiveness of renewable energy sources.

This study aims to develop an environmentally friendly multi-energy system for sustainable production of electricity, cooling and hydrogen. The study introduces a pioneering geothermal-based system, integrating an ejector refrigeration cycle, a dual-loop organic Rankine cycle, and a hydrogen production unit with proton exchange membrane electrolyzers. The study provides a thorough analysis of the system's energy and exergy performance, as well as its economic feasibility. Through sensitivity and parametric analyses, the research identifies key parameters that significantly influence system performance. The system's innovative design promises minimal environmental impact while delivering multifaceted performance: generating 1.38 MW of electricity, supplying 436 kW of cooling load, and producing 5.39 kg/h of hydrogen. In the exergy analysis, Evaporator1 is identified as the primary contributor to exergy loss, representing 34 % of the total exergy destruction. This is followed by the electrolysis unit, the condenser, and the ejector refrigeration cycle, which contribute 18 %, 14 %, and 12 %, respectively. The system achieves optimal efficiency at an organic Rankine cycle turbine1 inlet temperature of 387 K, yielding a power generation of 885.4 kW and an exergy efficiency of 26.7 %. Beyond this temperature, any further increase leads to a decline in power output due to operational disturbances. A multi-criteria optimization using genetic algorithm is applied, resulting in an optimized system with a cost rate of 18.13 $/h and an exergy efficiency of 38.96 %.

The proposed system uses a dual-loop organic Rankine cycle, a reverse osmosis desalination unit, an absorption cooling unit, and a thermoelectric generator to produce electricity and freshwater for urban areas. A thorough assessment of the system's thermodynamic and economic performance has been conducted, with a parameter-based investigation to assess the effect of key variables on the system performance. The parametric study indicates that rising the geothermal mass flow rate enhances the energy efficiency, but lowers the energy efficiency and affects the cooling requirements. Moreover, the optimum inlet temperature in turbine 1 increases the desalination efficiency up to 105.02 kg/s at 115 °C, and higher temperatures reduce the performance and system efficiency. Adjusting the temperature difference at the pinch point at Evaporator1 is crucial for system efficiency, with trade-offs between freshwater output, expenses, and exergy efficiency. The capability of the system to produce up to 6,048,000 L of potable water daily signifies a monumental leap towards meeting the water demands of nearly 42,000 individuals, based on European consumption standards. Lastly, the application of genetic algorithms in the optimization process results in an exergetic efficiency of 32.79 % and a cost rate of 58.05 $/h, demonstrating the system's enhanced operational effectiveness.

The principal aim of this article is to optimize the thermal and electrical efficiency of a geothermal combined heat and power system through metaheuristic particle swarm optimization (PSO) method. The objective of this research is to conduct a thorough analysis of the incorporation of metaheuristic PSO technique, with a specific emphasis on the potential advantages and obstacles associated with the utilization of metaheuristic approaches in improving the effectiveness of geothermal energy systems. The utilization of a double-flash geothermal system in conjunction with a transcritical carbon dioxide Rankine cycle is utilized for the co-generation of electricity and thermal energy. The research utilized a PSO method to enhance power generation, heating capacity, and overall system efficiency. The PSO algorithm was employed to determine the optimum operational parameters for a pressure level of 820 kPa and a pressure ratio of 1.59, leading to the maximization of power output to 2591.4 kW The PSO algorithm effectively identified the optimal operational parameters as a pressure of 820 kPa and a pressure ratio of 1.59, resulting in the achievement of a peak power output of 2591.4 kW. The methodology has determined that a pressure of 916.4 kPa and a pressure ratio of 1.5 represent the optimal parameters for achieving a maximum heating capacity of 12329.1 kW.

This study presents a comprehensive technical, environmental, and economic analysis of a thermal power plant utilizing solid oxide fuel cells (SOFC) to meet urban demands for electrical power, fresh water, and hydrogen. The integrated system includes SOFC with anode and cathode recycling, multi-effect desalination, a power generation cycle with a heat recovery unit using a thermoelectric generator, and a hydrogen compression unit. A detailed parametric analysis was conducted to identify optimal conditions for key outputs such as total cost rate and exergy efficiency, employing genetic algorithms and artificial neural networks. According to the economic evaluation, the SOFC stack accounts for 65.11 % of total costs at 139.8 $/h, with the inverter contributing 11.9 %. The environmental analysis shows that the proposed system emits the least CO2 per energy unit compared to SOFC/GT and SOFC/GT/RC systems. The parametric study indicates that increasing the pressure ratio enhances the output power of the SOFC and the production of the gas turbine. However, this also leads to higher compressor consumption, thereby reducing net power. Furthermore, increasing the current density results in greater production of electricity, hydrogen, and freshwater, while also raising the exhaust gas temperature, which aids in the desalination process. The optimization results show an exergy efficiency of 61.38 % and a total cost rate of 132.9 $/h, with artificial neural networks reducing optimization time from 124 h to 14 min.

Thermal energy storage (TES) units featuring phase change materials (PCMs) have a crucial impact on efficient energy management. Since energy demands keep changing, and the world increasingly relies on sustainable energy sources, innovation is continuously required. New technologies, materials, and methods for energy storage must be developed to meet these shifting needs effectively. In this study, a triplex-tube heat exchanger (TTHE) was employed as a TES medium, utilizing PCM within the middle tube. Because of its high thermal energy storage capacity, RT50 was selected as the PCM. Four innovative arc-shaped fins were strategically integrated within the PCM space to accelerate heat release. The system was subjected to rotational speeds of 0.1, 0.3, 0.5, 1, and 1.5 rpm. The investigation was carried out in two stages: initially, the impact of varying rotational speeds on the discharging process of the PCM in a finless TTHE was explored; subsequently, the influence of the same speeds on the solidification behavior of the finned TTHE was analyzed. Natural convection and PCM’s solidification process were examined through the enthalpy-porosity method. The findings represented that in the absence of fins, the device’s rotation had a more noticeable impact on the solidification behavior; however, incorporating fins was much more influential. Up to 2850 s, all the finned systems had solidified entirely in the presence or absence of the rotational mechanism, while in the finless system with a rotational speed of 1.5, 30.52 % of the material was still unsolidified until this time. Finally, the influence of three key structural parameters of the fins: α (outer fins arc angle), β (inner fins arc angle), and S (space between fins) on the discharging time of the PCM was analyzed using an artificial neural network (ANN) model. The study offered critical insights for optimizing fin configuration by systematically varying these parameters within defined ranges. An ANN predictive model was also proposed to assist TES system manufacturers and developers in future design and optimization efforts. The results demonstrated that the optimal setting of the TTHE (with the parameters of α = 60°, β = 60°, S = 10 mm) achieved the liquid fraction of 0.1, 80.33 % faster than the finless system.

This research explores the intricate concept of the Slow Invariant Manifold (SIM) and its pivotal role in developing model reduction techniques (MRTs) for challenges within dissipative systems in chemical kinetics, specifically in mechanical engineering. Focusing on the multi-step mechanism with two intermediates, primary approximations of the SIM are constructed and compared using two prominent MRTs: The Spectral Quasi Equilibrium Manifold (SQEM) and Intrinsic Low Dimensional Manifold (ILDM). At the given rate coefficient, a special computational experiment was performed in which the efficiency of chemical species has been compared. Noteworthy innovation involves evaluating SIM separately for reduced species, departing from the conventional approach of considering every species within the mechanism. The study employs local sensitivity analysis with MATLAB's Sim-Biology toolbox, presenting quantitative findings in a tabular format for a comprehensive MRT comparison. Beyond contributing to a deeper understanding of model reduction techniques in complex chemical kinetics, this research marks the first computational exploration of dissipative systems. The novel perspective of evaluating SIM for reduced species offers nuanced insights, emphasizing the critical role of SIM in effectively addressing challenges in mechanical engineering applications. In summary, this study introduces computational advancements and novel approaches, advancing model reduction techniques and highlighting the significance of SIM in addressing challenges within mechanical engineering contexts.

Temperature plays a crucial role in various industries, equipment, and human life, and heat exchangers emerge as fundamental devices in managing this essential parameter. Heat exchangers come in various types, each tailored for specific applications. One commonly utilized variant is the finned-tube heat exchanger, which excels in transferring heat between liquid and gas. This study involved a numerical investigation of a finned-tube heat exchanger equipped with four bow-shaped vortex generators. The primary objective of this study was to assess the influence of three distinct geometric parameters associated with vortex generators on the performance of the heat exchanger. The key performance indicators investigated in this research were the Nusselt number and friction factor, which respectively represent the thermal and hydraulic performance of the device. Also, the relative efficiency index parameter was investigated to evaluate thermal and hydraulic performances simultaneously. Artificial Neural Network and Genetic Algorithm methods were employed to develop response models to simplify and increase the analysis accuracy and determine the optimal geometry. The artificial neural network's prediction models demonstrated high accuracy and effectively forecasted the desired outcomes. It was determined that the relative efficiency index of the finned-tube heat exchanger with vortex generators, even in its weakest-performing design, reached 1.0467. This means that by adding the bow-shaped vortex generators to the heat exchanger, there is a 4.67% increase in the overall performance in the worst case. Also, after predicting the optimal design for the heat exchanger, it was observed that this device has a 13.12% improvement in overall performance compared to the case without vortex generators. To adjust the Nusselt number of the finned-tube heat exchanger lacking vortex generators with that of the finned-tube heat exchanger featuring the optimal design, it is necessary to increase the inlet flow rate of the vortex generators-lacking heat exchanger by 53.20%. This increase in flow rate leads to a significant escalation of 108.73% in the heat exchanger's pumping power compared to the pumping power of the optimal design heat exchanger.

This research aims to explore the economic and energy efficiency ramifications of combining the supercritical carbon dioxide oxy-fuel cycle with the Goswami cycle in the context of integrated heating and cooling systems, as well as power generation. Oxy-fuel systems, known for their high operating temperatures. In oxy-fuel systems, gas turbine exhaust gases typically preheat incoming flows to the combustion chamber. Nevertheless, even after preheating the combustion chamber inlets, the exhaust gases from the gas turbine retain a relatively high temperature, demonstrating considerable potential for energy production. Through a comprehensive parametric study, the impact of independent parameters on system performance is systematically explored. Subsequently, a three-stage economic analysis, total capital investment cost estimation, total operating cost estimation, and income evaluation, is conducted. The investment return time is assessed based on the selling price of system products. In the final step, a genetic algorithm, combined with artificial intelligence, optimizes the system for rational and optimal economic and energy conditions. The research findings reveal that in the absence of the Goswami cycle, the system yields 13.5 MW of power and 8.5 MW of heating, with an investment return time of approximately 16.32 years. However, with the inclusion of the Goswami cycle, power production increases to 15 MW, accompanied by 5 MW of cooling and 6.7 MW of heating. Despite augmented initial and operational costs, the investment return time is significantly reduced to 9.9 years. The results of a three-objective optimization strategy, focusing on maximizing power, heating, and cooling, highlight the system's versatility. Opting for maximum power production enhances power by 12.53 % and cooling by 27.48 %, compared to the reference mode. However, heating production decreases by 37.01 %. Notably, total capital investment costs show improvement by about 4.54 %, and the pay-back time experiences a substantial reduction of approximately 27.68 %. Moreover, the levelized cost of energy decreases from 111.4 $/MWh in the reference state to 106.9 $/MWh, underscoring the economic efficiency of the integrated system.

Abstract Carbon nanotubes (CNTs) are nanoscale tubes made of carbon atoms with unique mechanical, electrical, and thermal properties. They have a variety of promising applications in electronics, energy storage, and composite materials and are found as single-wall carbon nanotubes (SWCNTs) and double-wall carbon nanotubes (DWCNTs). Considering such alluring attributes of nanotubes, the motive of the presented flow model is to compare the thermal performance of magnetohydrodynamic (MHD) mono (SWCNTs)/Ethylene glycol) and hybrid (DWCNTs- SWCNTs/Ethylene glycol) nanofluids over a bidirectional stretching surface. The thermal efficiency of the proposed model is gauged while considering the effects of Cattaneo-Christov heat flux with prescribed heat flux (PHF) and prescribed surface temperature (PST). The flow is assisted by the anisotropic slip at the boundary of the surface. The system of partial differential equations (PDEs) is converted into a nonlinear ordinary differential system by the use of similarity transformations and handled using the bvp4c numerical technique. To depict the relationship between the profiles and the parameters, graphs, and tables are illustrated. The significant outcome revealed that the fluid temperature rises in the scenario of both PST and PHF cases. In addition, the heat transfer efficiency of the hybrid nanoliquid is far ahead of the nanofluid flow. The truthfulness of the envisioned model in the limiting scenario is also given.

The studies of non-Newtonian fluids are extremely important because of their unlimited and extensive usage and applications in different mechanisms and processes. The current work carried out the visualization of thermal energy in the Oldroyd-B fluid model by inserting different types of nanoparticles, which are used to promote thermal performance. The flow is assumed to be passed through a cone with electro-magneto-hydrodynamic effect and thermal radiation and exponential external heat source. The modeled problem appears in the form of coupled nonlinear PDEs, which are transformed into corresponding ODEs by engaging the appropriate transformation. The solution of these transformed equations is calculated by using the finite element procedure. The utilization of tetra-hybrid nanoparticles improves the thermal performance and it is recorded that by inserting these particles fluid temperature increases significantly. It is worth mentioning that a finite element scheme is an authentic procedure to tackle highly nonlinear problems. The influence of multiple parameters such as M, E*, γ1,γ2 are examined and discussed in detail across the velocity profile whilst parameters behavior namely M,Ec,E* and Qs is observed graphically across the temperature profile. It was noticed through computed results that the velocity profile depicts enhanced behavior for E*, γ1,γ2 whereas, it illustrates reduced demeanor. Moreover, the Temperature profile illustrates accelerated behavior for M,Ec,E* and Qs. Moreover, skin friction and Nusselt number impact under distinct parameters are also presented graphically and show excellent agreement.

In modern technology, anticipating the optimal thermal performance in microchannel heat sinks (MCHSs) is a vital response to overheating challenges in high-performance electronic devices. The Response Surface Methodology (RSM) application presents an innovative way of modeling such equipment's behavior. In the current study, an MCHS with pin fins was chosen to examine the Nusselt number (Nu) and pressure drop (ΔP). Using two RSM models and multi-objective optimization, the study tried to discern the optimal values of the responses in the MCHS. The RSM models examined three key parameters: length, installation angle, and the spacing between pin fins. The interactive impacts of independent parameters and their consequent effect on the responses were also explored. The RSM models attained exceptional predictive accuracy, with the determination coefficient values of 0.9946 and 0.9986 for Nu and ΔP, respectively. The working fluid applied in the present work was water with an inlet temperature of 288 K and a Reynolds number of 300. Besides, the constant heat flux of 100 W/cm2 was exposed to the bottom wall of the MCHS. In pursuit of an optimal MCHS design that maximizes Nu while minimizing ΔP, the study employed the relative efficiency index (η) parameter. Considering heat transfer and ΔP in the pin-fin MCHS, the optimal design showed an impressive improvement of about 64.5 % compared to the fin-free heat sink. Moreover, the study provided Pareto optimal points for Nu, ΔP, and η, adding depth to understanding the complex trade-offs within the investigated MCHS.

Effective cooling solutions become increasingly crucial as micro-electrical devices advance in complexity and miniaturization. Microchannel heat sinks (MCHSs) are one of the solutions, and researchers are continuously exploring innovative designs and optimization techniques to ensure that these cooling solutions meet the evolving demands of modern electronics. In the current research, a novel disk-shaped MCHS with strategically positioned baffles was selected to be investigated. Total thermal resistance (Rt) and pressure drop (ΔP) were scrutinized to assess the role of the baffle’s structural parameters (length of baffles in the first row (L), with the values of 500 μm–1500 μm, space between the baffles (S), with the values of 1700 μm–2500 μm, and angle of baffles (θ), with the values of 45°–90°). Firstly, various combinations of structural parameters of the baffles were selected as study designs, and each was numerically simulated. Artificial neural network (ANN) models and a genetic algorithm were employed to predict the data and identify the optimal designs. Based on the obtained results, higher R2 values (0.9940 for Rt Model and 0.9996 for ΔP model) indicated the accurate performance of both ANN models. Two optimal designs of optimal thermal performance (OTP) and optimal hydraulic performance (OHP) with separate objectives of minimizing Rt and minimizing ΔP were recommended. The utilization of baffles with the dimensions of L = 1500 μm, S = 2500 μm, and θ = 90° in the disk-shaped MCHS led to the attainment of the OTP. While to acquire the OHP, it was necessary to use the baffles with dimensions of L = 500 μm, S = 1700 μm, and θ = 81.8325°. In the OTP design, Rt was reduced by about 49.47 %, while in the OHP design, it decreased by approximately 30.36 % compared to the reference design.

This specific research was carried out with the aim of investigating incompressible Casson fluid and Williamson liquid. Steady-state laminar boundary layer flow involving Brownian diffusion and thermophoretic diffusion is taken over a horizontal penetrating plate saturated with two distinct salts having distinct characteristics. Casson fluid motion was observed with the utilization of Darcy-Forchheimer technique. Whilst, Nanofluid motion and other salts motion induced by wall movement. Solar thermal collectors, Solar thermal systems, Solar cookers, Solar thermal energy, drying, generating electricity, chemical reactions, soil vapor extraction, medication and heat exchange medium. Physical quantities like density, temperature profile and concentration profile were addressed by aiding Bossiness approximation. Whereas the optimization of energy is carried out under the effects of cross-diffusion, regular diffusion and non-Fourier law for two different salt components. Formulation of acquired flow equations in terms of partial differential equations are further transformed into ordinary differential equations using similarity transformations. The numerical solution will be obtained by utilization of FE-method. It was found that the temperature field elevates on increasing solar thermal radiation number, modified Dufour numbers of salt 1 and salt 2. Thickness and width regarding momentum layers for Casson liquid rather than Williamson liquid while temperature field for Williamson liquid is higher than Casson liquid.

Abstract Inclusion of nanoparticles boosts thermal performance and is essential for thermal transport. The current investigation has been made to conduct research on heat mass transport in synovial material with the mixing of hybrid and tri-hybrid comprising variable viscosity past over a heated surface having constant density and a steady environment. The conservation laws have been considered in the presence of Lorentz force, heat generation/absorption, modified heat and mass fluxes together with chemical reaction. The mathematical model is developed in Cartesian coordinate in the form of coupled partial differential equation (PDEs). The derived PDEs are simplified by a boundary layer approach (BLA) and reduced PDEs have been converted into ordinary differential equation (ODEs) using scaling group Similarity transformation. The converted ODEs are highly nonlinear and have been solved numerically by finite elements scheme (FES). The used scheme is effective for nonlinear problem and can be frequently utilized to tackle nonlinear problems arising in mathematical physics.

The main goal of this paper is to describe the new version of extended Bessel–Maitland function and discuss its special cases. Then, using the aforementioned function as their kernels, we develop the generalized fractional integral and differential operators. The convergence and boundedness of the newly operators and compare them with the existing operators such as the Saigo and Riemann–Liouville fractional operators are explored. The integral transforms of newly defined and generalized fractional operators in terms of the generalized Fox–Wright function are presented. Additionally, we discuss a few exceptional cases of the main result.

Supercritical processes using carbon dioxide reached suitable sustainability in previous research. Hence, the current paper proposes and examines a recompression supercritical carbon dioxide Brayton cycle integrated with a solar power tower. To improve the stability of the solar subsystem for continuous daily operation, two energy storage stages are employed innovatively. In addition, the whole scheme embraces a solid oxide electrolyzer. Therefore, a novel combined electricity and hydrogen cogeneration model based on solar energy is evaluated here. To this end, the energy-, exergy-, sustainability-, and economic-based parametric sensitivity study is implemented comprehensively. Net present value is another performance metric investigated in this paper. Besides, a novel multi-objective approach utilizing an artificial neural network combined with a multi-objective grey wolf optimization is performed. Considering four different decision parameters, optimum objectives include exergy efficiency, hydrogen production rate, and products’ unit cost. From the parametric sensitivity study, it is inferred that the outlet pressure of the low-pressure turbine significantly affects performance metrics. Also, from the optimization, the optimum values of mentioned objectives equal 2587 kg/day, 20.89%, and 17.25 $/GJ, respectively. Moreover, the net present value indicates that the payback period can be reduced up to 8.1 years corresponding to optimum operational conditions.

To cope with the increasing heat generation resulting from increased power in micro-scale devices, an optimum design of a tree-like microchannel heat sink is suggested utilizing Artificial Neural Networks (ANN) and Response Surface Methodology (RSM). Based on the numerical finite volume model, both ANN and RSM models were trained, and their performances were assessed using four distinct performance criteria. The results showed that both of the models are reliable for the purpose of this study, showing a significantly high adjusted R2 for both performance control variables pressure drop (ΔP) and Nusselt number (Nu). The adjusted R2 values observed for the RSM were 0.987 and 0.997, respectively, for the Nu and ΔP. These values were calculated as high as 0.997 for Nu prediction and 0.997 for ΔP prediction using the ANN. Finally, utilizing the calibrated RSM and ANN models, three optimum design configurations for three different purposes given the priority requirements of the heat sink implementations were suggested. The minimum ΔP configuration is suitable for the longevity of the heat sink, and the maximum Nu configuration is suitable for applications where heat transfer performance is more critical. For a balanced optimum design, the efficiency index was maximized.

Natural convection heat transfer in a porous prismatic enclosure emplacing two movable hot baffles may well be executed in this research. The domain may be loaded with ternary hybrid nanofluid and the governing energy and Navier-Stokes equations might well be solved using finite element approach. The simulation is devised for a wide range of Darcy number, Rayleigh number, non-dimensional length between hot baffles. Examination of the influence of flow and thermal fields through isotherms, vertical and horizontal velocities, streamlines and Nusselt and Bejan numbers is carried out lucidly. Water + Carbon Nanotube (cylindrical)+ Al2O3(spherical)+ Graphene (platelet) is regarded as heat transfer medium within the enclosure. Results indicate that amplification of Darcy number upgrades vertical and horizontal velocities up to 88.51% and 83.77%, respectively, streamlines up to 88.12% as well as Nusselt numbers and isotherms effectively. Augmented Rayleigh number gives rise to intensification of isotherms, vertical and horizontal velocities, and streamlines while it exhibits reverse effect for average Bejan number. Moreover, rise of non-dimensional length between hot baffles from 0.4 to 0.6 leads to the encumbrance of maximum streamline, horizontal and vertical velocities by 11.32%, 8.6% & 5.12% respectively and lifts up average Bejan number by 24.17%.

Using vortex generators (VGs) in fin-tube heat exchangers (FTHEs) is one of the main options to increase their performance. Although numerical models can replace the expensive experimental studies, suggesting an optimum design configuration using numerical models involves trial and error procedures and can be very computationally demanding. To alleviate this situation in the present research, the utilization of Response Surface Methodology (RSM) and Artificial Neural Network (ANN) in the optimum design of FTHEs with VGs was proposed. To train the models, three explanatory variables were chosen: the length (L), arc angle (α), and attack angle (β) of the VGs. The target variables were the Nusselt number and the friction factor. The results showed that both ANN and RSM performed reliably, although the ANN outperformed the RSM in predicting the Nusselt number and the friction factor. Considering the Nusselt number value prediction, the ANN and RSM had an R-squared value of 0.990 and 0.954, respectively. Regarding the friction factor, the same performance criteria showed a value of 0.998 for the ANN and 0.972 for the RSM. In the end, based on whether the heat exchange performance or pressure drop reduction is the main objective of design or a balanced approach to both are the target, three optimum design configurations were suggested.

Multi Wall Carbon Nano-Tubes (MWCNT's) are new types of nanoparticles which enhance thermal behavior of the base fluids. These are applicable in automotive engineering. Besides, Micro-polar Nano-fluids are another type of Nano-fluid in which the micro-rotation of the nanoparticles is considered when solving the governing equations. In this study, the natural convection and entropy generation of a Micro-polar MWCNT-Fe3O4/Water Hybrid Nano-fluid is investigated. The governing equations which consider the Non-Newtonian behavior of this type of Nano-fluid are solved by FEM. The effcacy of micro-rotation parameter (0–2), Hartmann number (0-20), Rayleigh number (104-106), and inclination angle of the cavity (0°–90°) on the Nusselt number and S is investigated. The findings disclose that both micro-rotation parameter and Hartmann number reduce NC and Nuavg of hybrid nanofluid. Besides, Be number will increase by reducing Ra number. Moreover, the average of Be number will enhance up to 19% and the Nuavg will be weakened by 24% by changing the φ from 0° to 90°. Plus, Nuavg will descend by 27% when the micro-rotation parameter is increased from 0 to 2. This study is mostly applicable in solar collectors, heat exchangers, and biomedical engineering such as blood flow in which the micro-polar flow is present.

Improving the thermo-hydraulic performance of heat exchangers (HEs) can considerably manage the energy requirements in various engineering processes. The fin-and-tube heat exchangers (FTHEs) are extensively utilized in recent decades, and they provide substantial benefits over the typical HEs such as high heat transfer surface. The shape and geometry of the tubes remarkably affect the thermal efficiency and pressure drop of these devices. There are many experimental and numerical investigations relating to improving the thermo-hydraulic performance of FTHEs with circular and elliptical tubes in the literature. In the current research, five various hexagonal tubes with different side ratios were utilized to enhance the performance of an FTHE. The impact of various hexagon side ratios on heat transfer capacity and fluid flow characteristic of the FTHE at different Reynolds numbers has studied numerically. According to the obtained results, the plain-fin-and hexagonal tube HE with the side ratio of 0.25 had the best performance. Although the lowest values of the Colburn factor and the weakest heat transfer belong to the FTHE with the side ratio of 0.25, it is preferred to circular tube HEs due to the lower pressure drop they cause.

The significance of fractional calculus cannot be underestimated, as it plays a crucial role in the theory of inequalities. In this paper, we study a new class of mean-type inequalities by incorporating Riemann-type fractional integrals. By doing so, we discover a novel set of such inequalities and analyze them using different mathematical identities. This particular class of inequalities is introduced by employing a generalized convexity concept. To validate our work, we create visual graphs and a table of values using specific functions to represent the inequalities. This approach allows us to demonstrate the validity of our findings and further solidify our conclusions. Moreover, we find that some previously published results emerge as special consequences of our main findings. This research serves as a catalyst for future investigations, encouraging researchers to explore more comprehensive outcomes by using generalized fractional operators and expanding the concept of convexity.

Fuzzy-interval valued functions (FIVFs) are the generalization of interval valued and real valued functions, which have a great contribution to resolve the problems arising in the theory of interval analysis. In this article, we elaborate the convexities and pre-invexities in aspects of FIVFs and investigate the existence of fuzzy fractional integral operators (FFIOs) having a generalized Bessel–Maitland function as their kernel. Using the class of convexities and pre-invexities FIVFs, we prove some Hermite–Hadamard (H-H) and trapezoid-type inequalities by the implementation of FFIOs. Additionally, we obtain other well known inequalities having significant behavior in the field of fuzzy interval analysis.

In the present communication, we have model peristaltic transport of γAl2O3/H2O and γAl2O3/C2H6O2 in an asymmetric channel by using small Reynolds number (Re) and long wavelength (δ ≪ 1) assumptions. Two dimensional equations of peristaltic nanofluid flow are modeled and then solved by using analytical regular perturbation technique. We have formulated the peristaltic motion equations of nanofluids by applied magnetic field effect on asymmetric channel and derive energy equation by using heat generation/absorption effect. By using numerical integration the solution of pressure behavior and solution of resulting governing equations are calculated by Mathematica software. By using Matlab software the graphical behavior of pressure rise, trapping phenomena, temperature profile and pressure gradient are illustrated. We found that the narrow region of channel requires large pressure gradient, also the pressure gradient decreases with increase of channel width d. The pressure gradient of nanofluids increases due to increase in nanoparticle volume fraction, amplitudes and Hartmann number. Moreover, in trap phenomena the bolus size decreases by increase of Hartmann number and bolus size increases by increase of volume flow rate.

Theoretically, this work describes the exact solutions of fractional Casson fluid through a channel under the effect of MHD and porous medium. The unsteady fluid motion of the bottom plate, which is confined by parallel but perpendicular sidewalls, supports the flow. By introducing the dimensionless parameters and variables, the momentum equation, as well as the initial and boundary conditions, has been transformed to a dimensionless form. A mix of Laplace and Fourier transformations is used to get the exact solution for the momentum equation. The constitutive equations for Caputo-Fabrizio’s time-fractional derivative are also incorporated for recovering the exact solutions of the flow problem under consideration. After recovering the exact solutions for flow characteristics, three different cases at the surface of the bottom plate are discussed, by addressing the limiting cases under the influence of the side walls. Moreover, these solutions are captured graphically, and the effects of the Reynolds number Re, fractional parameter α, effective permeability Keff, and dimensionless parameter for Casson fluid β on the fluid’s motion are observed.

The central goal of the current research work is to develop a mathematical model by which we evaluate the influence of mixed convection on the flow rate, temperature profile and concentration rate in the occurrence of radiation and concentration impact respectively, through asymmetric divergent channel. The obtained resultant mathematical equations are transformed into system of first order differential equation by applying suitable transformation. Next we apply shooting method to calculated the numerical solutions with the help of MATLAB. Furthermore we graphically analysis the impact of geometry parameter a, Grashof number Gr and modified Grashof number Br on velocity profile. Also, we discuss the impact of heat generation parameter M and Prandtl number Pr on thermal profile. Moreover, we interpreter the consequence of activation energy E and temperature difference parameter Ω on concentration profile. Velocity profile u(y) reduces by enlarging geometry parameter a, velocity profile u(y) gives opposite performance for high value of Grashof Gr and modified Grashof numbers Br, temperature profile grows for large values of Prandtl numbers Pr and heat generation parameter M and concentration profile shows prominent impact more large values of activation energy and decreases for high values of difference parameter.

The present article reveals the study of heat transfer via buoyancy-driven flow and entropy generation of magnetic Fe3O4–H2O nanoliquid inside a porous enclosure using two square cylinders. The shape factor of diverse particle shapes may well be also considered. Numerical technique called FEM is implemented for the transformed governing equations. Results of Nusselt number, entropy generation, Bejan number along with the streamlines and the isotherms are obtained for disparate contributing parameters. Validation with earlier studies exhibits an excellent agreement in particular case and findings portray that entropy generation rises with growing Hartmann number and mounting Rayleigh number could overshoot the velocity gradient within the enclosure.

The transfer mechanism in fluid over a convectively and heated boundary with the consideration of Buoyancy and porous medium forces, Joule heating, viscous dissipation, temperature and concentration gradient and dispersion of nanoparticles is modeled. The related models are solved using finite element method (FEM). The convergent and mesh-free solutions are obtained. The dynamics of parameters on the flow fields are noted and displayed in the form of graphical and numerical data. Heat transfer rate can be increased by establishing temperature gradient whereas heat transfer can be increased by the compositional gradient. Flow over vertical boundaries experiences significant Buoyant force. Therefore, Buoyant force must be considered for vertical flows. The finite element solutions converge more faster than the solution obtained by the shooting method. Flow experience retardation due to Lorentz and porous medium forces. Thus flow can be controlled by applying an external magnetic field. The heat and mass transfer can be enhanced by the dispersion of nanoparticles in the fluid. Mass flux can be increased by establishing temperature gradient whereas heat transfer can be accelerated by the compositional gradient.

Natural convection (NC) of nanoliquids specifically in enclosures is of use in diverse fields such as solar collectors, HVAC, and medical treatment. Recent research shows that suspension of nano-materials within the base fluid enhanced the heat transfer performance. NC heat transfer of Al2O3–H2O nanoliquid within a crown cavity with a circular cylinder inside it is studied in this paper. The flow is considered buoyancy-driven which is under thermal radiation (Rd) and constant magnetic field within the porous media. Dimensionless modes of Navier-Stokes equations are considered as governing equations, and FEM is used to discrete the equations. Two scenarios are considered in the present paper. In the first scenario, the effect of parameters namely; Rayleigh number (103-105), Darcy number (0.001–0.1), Hartmann number (0–20), Radiation parameter (0.1–0.3), angle of magnetic field (0∘−90∘), and nanoparticle concentration (0.01–0.04) on heat transfer performance and entropy generation (Sgen) is studied. In the second scenario, the effect of geometry on Nusselt number (Nu) and Sgen is investigated. Da, Rd, and nanoparticle concentration have a positive impact on Nuavg and enhance heat transfer. Besides, the geometry with the best heat transfer performance is the one with the base wavy wall and the cylinder at the left side.

In this paper, numerical study of nanoparticles immersed in a viscoelastic fluid is discussed. The condition of zero normal flux is assumed which means that no mass flux exists near the variable thick surface. To control the arbitrary motion of nanoparticles, magnetic field is applied at the surface of sheet. After boundary layer, the governing partial differential equations are converted into ordinary differential equations by means of similarity transformations. The following system is calculated numerically by employing shooting method in conjunction with RK-five method. The velocity profile shows an increasing behavior for large values of α and λ. The base flow temperature profile reduces for increasing values of Prnumber and Nb parameter. Also, the results for skin frictions coefficient, mass and heat transport rate are explored with the help of tables. A comparative study has been made with prior published work.

The present article has been groomed to explore the boundary-driven magnetized flow of cross nanoliquid over thin needle subject to auto catalysis chemical reactions. In addition to it, the effect of entropy optimization model is incorporated and transportation of heat under non-uniform heat source/sink, Cattaneo-Christov heat flux (rectified Fourier) viewpoint (CCHF), and non-linear thermal radiation is also taken into account. Furthermore, the Brownian and thermophoresis aspects of nanoliquid are invoked. The dimensionless governing equations are solved by apposite shooting scheme. The outcomes of the present study via demonstrated graphs and numerical benchmarks seem to indicate that controlled Sakiadis and Blausius flow pattern of cross nanofluids is attained due to incremented magnetic field strength. Temperature ratio parameter contributes the upgradation of thermal boundary layer thickness and homogenous reaction rate leads to the diminution of nanoparticles concentration. The relative values of needle velocity and the velocity of cross nanofluid are most important factor for the regulation of viscous drag force and rate of heat transportation. Augmented Weissenberg parameter and Reynolds number are the prime factor for the uplift of the flow field and the related layer thickness. Furthermore, the existence of CCHF could result in augmentation in Nusselt.

In the recent era of research, the field of integral inequalities has earned more recognition due to its wide applications in diverse domains. The researchers have widely studied the integral inequalities by utilizing different approaches. In this present article, we aim to develop a variety of certain new inequalities using the generalized fractional integral in the sense of multivariate Mittag-Leffler (M-L) functions, including Grüss-type and some other related inequalities. Also, we use the relationship between the Riemann-Liouville integral, the Prabhakar integral, and the generalized fractional integral to deduce specific findings. Moreover, we support our findings by presenting examples and corollaries.

This article addresses the entropy generation in mixed convection magnetohydrodynamics Eyring–Powell nanofluid flow toward a permeable surface of a cylinder. The flow is modeled considering heat generation and chemical reaction aspects. The influence of buoyancy forces, magnetic field, and thermal radiation is also considered. Moreover, activation energy, viscous dissipation, and permeability effects on bio-nanofluid flow are assimilated in modeling of concentration and energy relations. Total entropy generation is modeled in view of the second thermodynamics law. The governing system of PDEs is deduced by incorporating boundary layer assumptions. Relevant transformations are used to reduce the dimensional flow model into a non-dimensional one. The built-in shooting technique and the NDSolve code in Mathematica software are used to handle the dimensionless flow expressions. Variation in velocity, temperature, concentration, motile micro-organisms, Bejan number, and entropy generation with respect to the involved parameters is scrutinized graphically. Surface drag force, heat transfer rate, mass transfer rate, and density number are further calculated and investigated. Important results are summarized at the end.