Heat Transfer最新文献

The current study explores the transient magnetohydrodynamic (MHD) flow with the interaction of quadratic convection, slip of second-order momentum, viscous dissipation, and Newtonian heating. In this setup, the governing equations become highly nonlinear. The numerical solutions are attained by utilizing an implicit type of the Crank–Nicolson technique. The primary aim of the exploration is to figure out the consequence of MHD nonlinear convection and momentum slip of second-order on the overall behavior of the system. The robust agreement is evinced by numerical computations verified against existing research. Skin friction and Nusselt number decreases for second-order slips, $���\delta �\; �=�\; �0�\; �,�\; �-�\; �2�\; �,�\; �-�\; �4�\; �,�\; �-�\; �8��$, and −16. And for $���\gamma �\; �=�\; �1.0��$ and $���\delta �\; �=�\; �-�\; �2.0��$ the temporal coefficients of friction and heat transmission attain a steady state at time t = 29.88. It is significant that nonlinear convection predominates over viscous dissipation and that nonlinear convection is influenced by magnetic fields. The results are described using plots and tables.

The current article deals with the steady behavior of a fully developed viscous electrically conducting and compressible Jeffrey fluid via infinitely parallel porous vertical microchannel in the sight of a transverse magnetic field. The fluid flow problem is modeled using Napier–Stokes and energy conservation equations. To analyze the problem, the leading equations are reformulated into dimensionless forms. These dimensionless transformed equations are described by nonlinear-coupled ordinary differential equations and are eliminated utilizing the shooting method based on the fourth-order Runge–Kutta technique through the boundary conditions; this represent slip velocity and temperature-jump situations on the fluid–fence interface. The model equations are numerically solved with MATLAB's built-in routine “bvp4c.” The behavior of Jeffrey fluid is described through graphs. The significance of model parameters is scrutinized and discussed in detail through graphs. Various significant impacts are examined in these simulations, such as radiation, magnetic field and viscous dissipation. Furthermore, the essential results of this investigation are the effects illustrated graphically and discussed quantitatively concerning various influencing parameters corresponding to the magnetic parameter, interaction parameter, buoyancy parameter, Darcy parameter, wall ambient temperature ratio, and the fluid-wall relationship. We noticed that both walls are heated, that is, $���\xi �\; �=�\; �1��$ the velocity decreases with a rising Jeffrey parameter.

Biodiesel has been chosen as a decent alternative to diesel in the context of establishing environmentally pleasant conditions and saving petroleum-based resources for future generations. It is well-established that biodiesel-powered diesel engines may achieve outcomes equivalent to those of diesel engines. The current investigation was conducted to study the effect of injection pressure (190, 210, and 230 bar) and exhaust gas recirculation (EGR) (5%, 10%, and 15%) on a single-cylinder variable compression ratio (VCR) diesel engine running using a B20 (20% MB + 80% PD) blend of microalgae biodiesel (MABD). This experiment was conducted in two stages. During the first stage of experimentation, the efficiency and emission characteristics of a diesel engine with a B20 blend of MABD at various fuel injection pressures and fresh air were investigated. During the second phase, fresh air was mixed with 5%, 10%, and 15% exhaust gases, and the experiment was conducted. It was discovered that increasing injection pressure to 230 bar provided considerable improvements. Brake thermal efficiency increased by 2.35%, brake-specific fuel consumption decreased by 3.57% and pollutants such as carbon monoxide (CO), hydrocarbon, and smoke were reduced by more than 50% compared to conventional diesel. These reductions were similarly significant (over 22%) as compared to the B20 blend at lower injection pressure (210 bar). However, there was a slight trade-off: nitrogen oxide (NO_x) emissions increased partially (3.14%), while exhaust gas temperature (EGT) increased by 1.72% at a higher pressure. The study then investigated the influence of EGR (5%, 10%, and 15%) at various injection pressures. The optimal value seems to be 10% EGR at 230 bar injection pressure. This combination substantially reduced NO_x emissions (by over 41% compared to the normal B20 blend) and EGT (by more than 8%), while having no notable effect on other performance or emission variables. Overall, the results show that employing a B20 MABD blend with high injection pressure (230 bar) and moderate EGR (10%) improves engine performance while reducing hazardous emissions.

This paper adopted the Lattice Boltzmann Method (LBM) to compute a two-dimensional incompressible convective flow field with two isothermal elliptical sinks attached vertically to the horizontal bottom north side of the enclosure, which is subjected to a horizontal magnetic field. The attained results show that LBM can effectively capture vortex shedding structures and other features inside such complex flow. Also, the Hartmann numbers with a panoply of heated sinks location were studied, and the results showed that fluid flow and heat transfer rate depend on the Hartmann number and are greatly influenced by the heated sinks positions. The effect of the Prandtl number on the average Nusselt number is also highlighted.

The investigation of aluminum gallium nitride/gallium nitride high electron mobility transistor (AlGaN/GaN HEMT) devices with a dual-metal gate (DMG) structure encompasses both electrical and thermal characteristics. As efforts to enhance heat dissipation progress, there is a concurrent exploration of novel semiconductor materials boasting high thermal conductivity, like boron arsenide and phosphide. Combining these materials into a model and measuring their interface achieves efficient energy transport. Minimizing the self-heating impact in AlGaN/GaN HEMTs is essential for enhancing device efficiency. This research exposes the heterogenous combination of boron arsenide and phosphide cooling substrates with metals, GaN semiconductors and HEMT. In this research, the autoencoder deep neural network techniques in GaN HEMT for self-heat reduction is driven by the ability to effectively analyze and model the thermal behavior of the device. Autoencoders learn complex relationships within temperature data and identify patterns associated with self-heating. By leveraging these learned representations, the deep neural network optimizes control strategies to mitigate self-heating effects in GaN HEMT devices, ultimately contributing to improved thermal management and enhanced overall performance. In this research, the use of genetic algorithms in GaN HEMT aims to optimize device parameters systematically, to minimize self-heating effects and enhance overall thermal performance. The structure also enhances electron mobility within the channel. Results show DMG structures, exhibiting higher saturation output currents and transconductance despite self-heating. The DMG exhibits a maximum g_m value of 0.164 S/mm, which is 10% higher significantly enhancing GaN-based HEMTs for improved reliability and efficiency in various applications.

In this study, an efficient optimization framework was developed to determine the parameters of through-silicon vias and the layout of heat sources in three-dimensional integrated circuits (3D ICs), employing an artificial neural network (ANN) reduced-order model in conjunction with a Bayesian optimization (BO) algorithm. The proposed method effectively predicts the temperature distribution in 3D ICs and refines their thermal parameters, offering solutions to thermal management challenges. Latin hypercube sampling was utilized for data sampling, enhancing the previously established rapid thermal analysis method through parameterization of heat source locations. The temperature distribution data for varying hotspot locations in 3D ICs were fitted using an appropriately defined objective function, leading to the development of a reduced-order ANN model that accelerates temperature prediction. The computational results demonstrate that the neural network model exhibits a deviation in predicted values of less than 2%, and the coefficient of determination R² approximately 0.93, underscoring high predictive accuracy. Additionally, the optimization outcomes and the efficiency of the selected BO algorithm were thoroughly evaluated. Notably, the BO algorithm achieved the global optimum in just 4.07 s across 250 iterations, demonstrating an effective power distribution strategy for the 3D ICs model.

In response to the escalating emphasis on sustainability across diverse sectors, this study addresses the imperative to combat environmental degradation through conscientious development. The primary focus is on assessing the feasibility of replacing fossil fuels with renewable alternatives in internal combustion engines (ICEs) equipped with direct fuel injection. The research employs energy and exergy analyses, coupled with economic analysis, to comprehensively evaluate the performance of fuels and blends. Applying the Lotus Engine software, computational analyses are conducted, taking into account the specific geometry of the engine. Simulations explore different λ-factors to identify optimal performance configurations for each fuel or blend. Noteworthy outcomes reveal that blends featuring green hydrogen yield remarkable improvements, showing high torque (max. +11.5%), power (max. +14.35%), thermal efficiency (max. +3%), and exergy efficiency (max. +21.56%). These blends also demonstrate reduced operating costs (max. −10%), although with higher exergy losses, indicating areas for potential enhancement. Conversely, fuels containing ethanol show intermediate values between the blends and pure fuels. Consequently, this study effectively establishes the significance of these fuels in ICEs, supported by comprehensive energy, exergy, and economic analyses. The findings underscore the promising potential of renewable fuels as viable alternatives to fossil fuels, marking a substantial stride towards sustainable energy solutions and environmental preservation.