In the scenario of multidisciplinary applications in thermal engineering, there remains a gap in applications of artificial neural network (ANN) to fluid flow problems for magnetohydrodynamic (MHD) permeable wedge in presence of Soret-diffusivity effects. Similarity transformations were used for obtaining non-linear ordinary differential equations and solved using MATLAB bvp4c routine. Augmenting skin friction value by 37.45% is observed for an incrementing wedge angle parameter while Nusselt number increased for suction parameter (Fw) less than 0.6. Nusselt number portrays a decreasing nature for increasing magnetic parameter and increasing for incrementing suction parameter. Velocity profile decreased with increase of magnetic parameter (M) while concentration profiles decreased for Fwand Prandtl number. The maximum absolute error for neural network back-propagation results were of the order 10-5.. With 70% points used for training, the mean squared error (MSE) for Nusselt number was 3.3175x10-10in validation phase and 2.4922x10-10in training phase with 69 epochs.
This article has tried to study the jet flow of the ternary nanofluid suspended with motile microorganisms across a porous medium under the influence of radiation energy. These conditions have been carefully considered to enhance the heating conductivity of the nanoparticles. The ternary nanofluids (rGo-CoFe2O4-TiO2/H2O) enhanced thermal conductivity due to (TiO2) and (rGo) improves its performance in heat exchange model and thermal energy storage solutions. The inclusion of magnetic nanoparticles (CoFe2O4) gives rise to magnetohydrodynamic (MHD) effects, allowing for control of flow and heat transfer through external magnetic fields. In this study, thermophoresis and Brownian motion have been observed using the modified Buongiorno's model. The numerical solutions of the governing equations for various parameters have been calculated by the "bvp4c" method in the MATLAB software. The obtained graph represents the results of axial velocity, temperature, concentration, and motile density profiles for the ternary nanofluid. The study observed that by increasing radiation, magnetic, and mixed convection parameters, the velocity of the fluid gets enhanced. The concentration of the fluid is majorly enhanced by the increment of the thermophoresis parameter. Meanwhile, the motile density increases with the fall of the bio-convection Schmidt number. In addition, tables of skin friction coefficient, Nusselt number, Sharewood number, and motile density number displayed significant results. This model can be utilized in rocket engines, medical sciences, water jets, atmospheric science, agriculture, Cooling, and heating systems.
The study examined the behavior of Casson nanofluid flow, heat, and mass transfer around a wavy cylinder with convective Nield conditions. We analyzed the effects of several factors, including thermophoresis activation energy, and Brownian motion. We transformed the governing partial nonlinear system into system of ordinary differential equations by applying similarity variables. To derive numerical solutions, we employed the MATLAB bvp4c solver and presented our results using graphical representations. Our graphs illustrated the impact of different parameters, including the thermophoresis (Nt), magnetic parameter (M), Brownian motion parameter (Nb), Casson fluid parameter (beta), Lewis number (Le), Saddle/Nodal indicative parameter (c), and Chemical reaction parameter (sigma). We also computed Sherwood number and local Nusselt number, and skin friction coefficient along the x and y axes to obtain further understanding of the behavior of these emerging parameters. Increasing the Casson fluid parameter increases fluid velocity concentration while the temperature magnitudes tend to diminish. Improvement in Brownian motion, Biot number, and thermophoresis parameters results in a more effective thermal gradient and concentration.
Hyperthermia has been attracting great attention, research resources and clinical translation efforts as a cancer treatment. Metallic nanoparticles can enhance heat deposition in tumors when subjected to external energy sources like lasers. However, challenges remain in accurately estimating state variables, such as the temperature and heat sources, during treatments. This study presents a combined Physics-Informed Neural Network (PINN) and particle filter approach for state estimation in a model, representing a sample of a nanofluid heated by a near-infrared diode laser. The PINN is trained to solve the heat transfer model and serve as the state evolution model in the particle filter. Synthetic and actual temperature measurements from heating experiments involving a nanofluid of palladium-cerium oxide nanoparticles are used in the solution of the state estimation problem. Verification tests show that the particle filter can robustly estimate states with 850 particles in few seconds of computational time, due to the efficient PINN predictions. Overall, the combined PINN - particle filter approach demonstrates potential for solving state estimation problems in complex engineering systems, such as in cancer thermotherapy.
Effect of aspect ratio on thermal characteristics, including viscous dissipation, in pressure-driven flow of a third-grade fluid through a rectangular channel is considered. The walls of the channel are assumed to be maintained at uniform temperatures (the special case of the same upper and lower wall temperatures is also discussed). Earlier reported studies on heat transfer characteristics of third-grade fluids considered flow through large parallel plates. In actual case, however, flow occurs in channels and the parallel plate approximation results are only applicable near the central core of the channel, where, influence of the lateral walls are less. In view of this, in the present study, effect of the lateral walls are included in the governing equations and the results obtained are realistic from practical considerations. The effect of viscous dissipation is included in the energy conservation equation, and the influence of the aspect ratio is considered in the momentum and energy conservation equations. Momentum and energy conservation equations are formulated and reduced to their dimensionless forms by introducing suitable dimensionless variables and parameters. Entropy generation equation, including aspect ratio effect is deduced. The dimensionless governing equations are solved by applying the least square method (LSM), and the effects of parameters like aspect ratio, Brinkmann number, and non-Newtonian parameter on temperature, wall heat transfer, and velocity are examined. LSM is a semi-analytical technique which possesses a mixed characteristics of analytical and numerical methods and generates very accurate results. The results are validated with the results of least square homotopy perturbation method. It is important to note that heat transfer is reversed (from the upper wall to the surrounding cooling medium) at a distance of nearly 50% from the lateral walls) with rise in Brinkman number. In the region from lateral walls up to this limit (50% from the walls) heat is transferred from the upper wall to the flowing fluid. Results of the study can serve to be useful for design and analysis of heat exchangers in which lubricating oils, polymers flow takes place.
Numerical simulations for coupled heat and mass transport in a viscoelastic fluid motion created by an elongating cylinder are provided. The Soret effect as well as non-uniform internal heat source/sink effects are being studied. By assuming an electrically conducting medium, the behavior of axial magnetic force and associated Joule heating effect are incorporated together with the Darcy-Forchheimer term. By adopting a similarity solution technique, a self-similar solution is retrieved that contains important parameters. Drag force applied by the second-grade fluid on the cylindrical boundary is assessed and scrutinized under various controlling parameters. Through graphical illustrations, the impact of diffusion terms on the surface cooling rate is investigated. The current work considers flow visualization utilizing streamlines and isotherms for Newtonian and non-Newtonian flow situations through both porous and non-porous media. For the Newtonian fluid case, computed results for the skin friction coefficient and heat transfer rate are consistent with that of the existing literature.
Effective thermal management is essential in the design phase to ensure the optimal performance of solar PV panels. This work uses combination of experimental and numerical methodologies to examine improvements in heat transfer in plain plate fin and variable height plate fin heat sink. The investigation considers different orientations and heat inputs. The study focuses on four specific orientation angles: 0 degrees, 30 degrees, 60 degrees, and 90 degrees including a range of heat inputs, from 25 W to 100 W. The objective is to assess the performance of two different heat sink configurations. The numerical simulations conducted using ANSYS Fluent yield results that closely correspond to the experimental observations, indicating a significant level of agreement. A significant increase in the Nusselt number and heat transfer coefficient was observed when the heat input of 100 W was applied, reaching their maximum values. The Nusselt number (Nu) for the plain plate fin heat sink reached a maximum value of 81.22, while the variable height plate fin heat sink achieved 91.69 when positioned at a 90 degrees orientation. The plain plate fin heat sink achieved a maximum heat transfer coefficient of 11.77 W/m2K, but the variable height plate fin heat sink surpassed it with a heat transfer coefficient of 14.27 W/m2K. The findings offer useful insights for the creation of effective heat sink designs, especially for applications related to solar PV panels.
A novel multi-objective optimization scheme is implemented to enhance the heat transfer characteristics and to reduce pressure drop of heat exchanger in this article. The heat transfer efficiency and pressure drop of the finned heat exchanger are considered as the optimal objective function through the fine-tuning of the heat exchanger's fin spacing and fin angle. Numerical simulations of the prototype heat exchanger well agree with the experimental findings. The fin spacing and fin angles of the heat exchanger are manipulated as the optimization variables to attain the maximum Nusselt number and the minimum pressure drop. The nonlinear fitting of the data is performed using an Artificial Neural Network (ANN) to obtain the establishment of two predictive models. The models are optimized using a multi-objective Non-dominated Sorting Genetic Algorithm-II (NSGA-II), ultimately yielding a Pareto frontier curve. Two excellent optimization schemes can be obtained for heat exchanger. The Nusselt number of the optimized model rises as much as 4% when the pressure drop is almost consistent with the heat transfer of the original heat exchanger. The pressure drop of the optimized model reduces as much as 9% when the Nusselt number is well consistent with the drag force of the original heat exchanger. The energy efficiency is effectively improved by the optimization models of these two types of heat exchangers and the energy-saving goals are achieved through multi-objective optimization using NSGA-II.
Today, it is more necessary than ever to move toward optimizing energy consumption and providing sustainable energy. In addition to this, the aspects of optimizing energy consumption in the construction, design, and operation of equipment are of particular importance. Effect of the spiral with short lengths and regular distances with hybrid nanofluids on the heat transfer of turbulent flow and compared performance to the typical state is limit studied. In this regard, a numerical study was conducted to investigate the effects of simultaneous use of spiral and three types of hybrid nanofluid Tio2-Zno, Zno-Al2O3, and Al2O3-TiO2 with phi = 0.1% on flow and heat transfer characteristics for different spiral diameter ratios. In the next step, short spirals are used with Al2o3-Tio2 nanofluid with phi = 0.1, 0.3, and 0.5. The numerical solution is done using Ansys-Fluent software in three dimensions in a range of Reynolds numbers of 5000-28,000. The analysis shows that the spiral with d/D = 0.05 with Al2O3-TiO2 nanofluid creates maximum thermal performance. The highest Nusselt number is obtained with N = 4 and phi = 0.5% at Re = 27000. In the whole study, the changes of Nu/Nus, f/fs, and eta are 1.18-2.03, 2.47-5.093, and 0.82-1.348, respectively. Short spirals with regular intervals reduced the friction coefficient, and the thermal performance coefficient improved. This issue is useful in the cost management and energy consumption in industries.
The mixture of silver nanoparticles (Ag) and zinc oxide nanoparticles (ZnO) in terms of the hybrid nanofluid in a circular, and semicircular porous cavity offers enhanced heat transfer performance, making them suitable for various applications such as electronic cooling, thermal management in engines, and heat exchangers. A water-based hybrid nanofluid composition of Ag and ZnO is used for heat transfer (HT) performance applications. The porous cavity is considered for the flow field under the inspiration of an applied magnetic field. The problem of interest is tackled through the control volume finite element method "CVFEM." The artificial neural network (ANN) is also applied to handle the obtained results in terms of validation, testing, and training. Auto-encoder (AE) performance has been used for the impacts of different emerging factors of Ha and Ra. The interaction of the magnetic field and magnetic nanoparticles in the hybrid nanofluid causes magnetohydrodynamic (MHD) effects like Lorentz force and magnetization. Nanoparticle concentration, magnetic field intensity (Ha), and cavity porosity (Ra) are the parameters that affect the analysis. Varying these parameters allows for the estimation of the effects of the HNF to evaluate the heat transfer optimization. It is observed that ZnO enhances the HT rate by 14% and Ag + ZnO enhances the HT rate by up to 16% using the 5% nanoparticle volume fraction. Increased Rayleigh numbers enhance natural convection (HT) and encourage more energetic flow behavior, resulting in better heat transfer. Enhanced convective heat transfer is a result of Hartmann numbers, strong Lorentz forces, and metallic and oxide nanoparticles.
Recent progress in nanotechnology has resulted in the creation of advanced coolants known as nanofluids, which are used across various industrial and engineering fields. This research examines the impact of buoyant forces on the intricate flow patterns of Casson nanofluids when subjected to convective heating, magnetic fields, thermal radiation, viscous dissipation, and chemical reactions. It provides theoretical insights into the mathematical modeling of non-Newtonian nanofluid flows, focusing specifically on heat and mass transfer in cooling systems. The study analyzes the steady, laminar flow of two-dimensional Casson nanofluids over a permeable, shrinking/stretching slippery sheet in a porous medium, focusing on magnetohydrodynamic mixed convection. The partial differential equations (PDEs) and boundary conditions are converted into first-order ordinary differential equations (ODEs) and numerically solved using the Runge-Kutta-Fehlberg method (RKF45) with MAPLE software. The results depicting the impact of different parameters on dimensionless flow profiles are displayed through graphs and tables. The impact of different parameters on dimensionless flow profiles is illustrated both graphically and in tabular form. For both assisting and opposing flows (-1 <= N <= 1), enhanced convective heating and thermal Richardson numbers improve most flow profiles except solutal ones, while increased radiative thermal transfer benefits all but the concentration field. Moreover, for opposing flows (N <= 0), greater thermophoretic force reduces mass and heat transfer rates, whereas increased Brownian motion has the opposite effect. These findings align well with existing research.
A large number of scholars have considered the effects of temperature and concentration on fluid diffusion in a square cavity when using the lattice Boltzmann method (LBM) to study fluid motion, few have studied the influence of attenuation effect. In this study, the double-diffusive natural convection with temperature-dependent viscosity and attenuation effect inside a porous cavity was numerically investigated by LBM at representative elementary volume (REV) scale. The influence of different flow governing parameters, including attenuation coefficient, the temperature-dependent viscosity and porosity on heat and mass transfer rates was investigated. The streamlines, isotherms, isoconcentrations, average Nusselt number and average Sherwood number curves for different parameters were discussed in detail. The results indicated that decreasing the viscosity and increasing the porosity could improve the heat and mass transfer, and a certain degree of concentration decay rate contributed to fluid diffusion. When the parameters were changed, the Nusselt number under the corresponding parameters had to be recalculated, which took a lot of computational time. Therefore, we introduced a machine learning method to predict the Nusselt number. The goodness of fit reached 0.968 and the error rates were all within 7%. It achieved a high prediction accuracy and improved the computational efficiency.
A finite element analysis through a node-mesh technique was numerically investigated on a microhole component utilizing the coordinate geometry system in the X-, Y-, and Z-directions, respectively. The injection molding simulations and statistical analysis were conducted computationally for some selected cases in the study. The obtained results are effectively depicted through the utilization of graphs, allowing for a comprehensive understanding of the impact that the different parameters have on the warp deflection. The key findings in the study are that a higher melt temperature and considerable control of other process parameters to an intermediate level reduces the defect on the microhole component.
This research analyzes the application of nanofluid consisting of Co-H2O to enhance the heat transmission of mixed convection inside a lid-driven porous T-shaped enclosure. For greater thermal contact and heat transfer, copper foam with 40 PPI pore density and 90% porosity is used in this research. Furthermore, the Darcy-Brinkman model generates nanofluid-saturated porous medium equations. The governing equations with proper boundary are solved by using Galerkin weighted residuals of Finite Element Method (FEM). The outcomes are acquired for the average Nusselt number (Nuave), the solid volume fraction, delta (0.01-0.05), the Reynolds number, Re (50 - 200), the Darcy number, Da (10-5-10-2), the Richardson number, Ri (0.1-10). the unsteady parameter, tau (0.1-1.0), which are the prevailing parameters in this investigation. The graphical results of thermal fields and flow fields at the heated surface of the circumference include the average Nusselt number, streamlines and isotherms. It has been found that Da and Re have a momentous influence on the isotherms and streamlines observed in the enclosure. The results demonstrate that the heat transfer rate is greatly affected by Da, Re and delta. The Nusselt number is examined as a function of the base fluid (water, kerosene, and engine oil) and the volume percent of nanoparticles (Co, Ag, Al2O3, and TiO2). It has been found that increasing the Reynolds number from 50 to 200 at non-dimensional time tau=0.9, the average heat transfer rate increased by 75%. It is highlighted that the heat transfer rate for the Co-H2O nanofluid is higher (around 206% at tau=0.1) than the other eight types of considered nanofluids. Moreover, it has been found that water-based nanofluids have a significant higher heat transmission rate than kerosene-based nanofluids.
The present work addresses a numerical approach to the fully developed magnetohydrodynamic mixed convection of a viscous, incompressible, electrically conducted fluid, with the radiation parameter in the vertical micro-porous channel filled with porous medium and thermal nonequilibrium conditions being considered. Governing differential equations are solved numerically by using spectral collocation techniques. The aim of this study is to understand the effect of the inter-phase heat transfer coefficient (H), porosity-scaled thermal conductivity ratio gamma, radiation parameter Rd , and Darcy number Da on the velocity, magnetic field, and heat transfer rate Nu profile. The present study revealed that the magnitude of flow was reduced for the higher value of Rd for all three cases (zeta=1,zeta-0,zeta=-1). The Darcy number (Da) reduces the velocity as well as the magnetic field. The velocity profile for zeta=1 (when both walls are heated) decreases as the inter-phase heat transfer coefficient H increases, while for the other two cases, the reverse effect has been observed. The present study also revealed that there exists a threshold value H0 of H for zeta=1 and zeta=0 where the heat transfer rate Nu becomes the decreasing function of H in the interval [0,H0] when radiation parameter Rd increases from 1 to 5. Overall, the inter-phase heat transfer coefficient H makes the flow profile smooth (stabilizes the flow) and recovers the system to equilibrium.
A FORTRAN in-house code is elaborated to investigate the two-dimensional and laminar natural convection flow for a Newtonian fluid in an open square enclosure saturated with a nanofluid and subjected to an external magnetic field. Except for the bottom wall, which is heated with linear varying temperature, all cavity walls are held at a constant cold temperature. Applying magnetic fields in certain directions can serve to regulate and control fluid flow patterns and heat transmission for cooling electronic components. By embracing the stream function vorticity formulation, the physical phenomenon is modeled mathematically by a set of transport equations. The dimensionless governing equations are discretized utilizing the finite volume method and solved numerically using a relaxation iterative method. The examination of specific parameters encompassing the Hartmann number, nanoparticle volume fraction, magnetic field inclination angle, and Rayleigh number over a wide range was accomplished to understand their effects on heat transfer and flow patterns. The outcomes denote that, in convection heat transfer, regardless of the magnetic field's inclination angle, increasing its intensity diminishes the volume of fluid drawn into the cavity and also causes poorer heat transfer and diminished fluid motion. Conversely, the incorporation of nanoparticles enhances heat transfer and influences fluid motion in distinct ways: It promotes fluid movement in the absence of a magnetic field, whereas in the presence of a magnetic field, it hinders fluid motion.
Exploring the unique features of wavy surface amplitude in the existence of shear-thinning materials is a highly contributed work and is determined to be very attractive in different practical fields. However, this particular work focuses on incorporating nanofluid, thermal radiation, and gravity forces into the wavy motion of shear-thinning materials. The leading problem is then formulated in the form of dimensionless mathematical equations. All the prominent related results are plotted. The flow speed is noticed to increase with the variation in the viscosity ratio parameter and Richardson number. The higher temperature is noticed as a result of larger radiation, Brownian motion, and the wavy surface amplitude. The volume fraction is noted to be higher for various amplitudes, and a reverse trend is noticed for thermophoretic forces. The flow behaviors of mass and heat are remarkably affected by gravity forces. The method is verified by providing a well matched comparison.
This study analyzes the efficiency of Repeated Richardson Extrapolation (RRE) as an alternative for reducing and estimating discretization error (Eh) in the numerical resolution of the one- and two-dimensional two-phase flow problem in porous media. The numerical solution was obtained using the finite volume method (FVM) in space and the implicit Euler method for time discretization. For linearization, we employed the modified Picard method, and to solve the linear equations system, we used the Gauss-Seidel solver coupled with the multigrid method to accelerate the convergence of the iterative process. The variables of interest analyzed were the wetting and non-wetting pressures located at the central point of the domain. The results indicate that the employed methodology was suitable, with an increase in the accuracy level of numerical solutions from 10-3 to 10-14, and additionally, accurate estimates for Eh were obtained.
In this article, the hybrid Monte-Carlo method is extended to calculate view factor systems. The extension includes the application of view factor algebra and standard deviation weighting. The application of view factor algebra is straight forward but more efficient than when used with ray tracing. Standard deviation weighting is a novel algorithm that makes it possible to determine an optimal distribution of function evaluations of the view factor integral kernel when applied to view factor systems. The hybrid Monte-Carlo method has been extended to 2-Dimensional domains but is not as efficient as the Monte-Carlo method with ray tracing, the standard approach. In the evaluation of the hybrid quasi-Monte-Carlo method two low-discrepancy sequencies are considered, Halton sequencies and Sobol sequencies. One interesting conclusion of the investigation is that when standard deviation weighting is particularly effective Halton sequencies consistently outperform Sobol sequencies in reducing the RMS error. In previous applications to view factor evaluation the choice of low-discrepancy sequence has not been a significant issue.
