International Journal of Thermofluids最新文献

The study of Bingham–Papanastasiou fluids is conducted in lid-driven cavity with consideration of viscous dissipation. The upper and left wall of the cavity is cold while other walls are insulated. Numerical simulations are conducted to study the isotherms, temperature profile, local and average Nusselt number. The main focus of work is to analyse the behaviour of heat transfer within trapezoidal cavity. The governing system of nonlinear dimensionless partial differential equations is analysed by using PDE solver of finite element method in COMSOL. The analysis is carried out for different parameters like Reynolds number, Bingham parameter, stress growth parameter, Eckert number and Prandtl number. It is observed that impact of Bingham parameter on temperature variation is negligible while the impact of stress parameter leads to the reduction in temperature within cavity. The novelty of this work is that no work is done for the case of trapezoidal cavity where Bingham–Papanastasiou fluid behaviour is observed under the consideration of viscous dissipation and mixed convection.

The present research comprehensively examines the influences of different input variables, such as cabinet load level, ambient temperature, relative humidity, door opening time and day, on the output parameters of frosting amount, recovery time, and total energy consumption of the upright domestic freezer following the door opening operation. Moreover, a holistic statistical methodology, which is known as the GLM-ANOVA, was implemented for determining the parametric experimental results. Meanwhile, the effects of binary interactions between the input factors on the output parameters were extensively evaluated using statistical methods. As a consequence, the amount of frost increases with an upward gradient as the load level within the cabinet escalates from 25 °C to 32 °C. Both the duration of door openings and the relative humidity level have a double impact on the frosting. Moreover, the duration required for the system to recover from half load to full load more than doubles with an increase in ambient temperature from 25 °C to 32 °C. On the other hand, at an outdoor temperature of 25 °C, the recovery time demonstrates a close to linear relationship with the load level of the cabinet. Furthermore, the duration of door openings and the load capacity within the cabinet are considered two important factors that simultaneously influence the daily energy consumption. The infiltration of ambient air into the freezer compartment and the presence of moisture in the air substantially increase the energy consumption, especially when the relative humidity fluctuates between 30 % and 65 % and the door opening duration stretches from 10 to 20 s, respectively.

A novel discharge dispersion model is developed to simulate the complex three-dimensional flow behaviour of thermal-induced buoyant water jets under current-wave coexisting conditions. The model solved the governing fluid flow and energy equations for two immiscible and incompressible phases (water and air) which were weakly coupled by applying the Boberbeck-Boussinesq approximation. Different turbulence models, such as k−ε multiphase, k-ω SST, k-ω SST-multiphase, k-ω SST-stable, and realizable k−ε were applied. Extensive verification of the model's performance is conducted by comparing the developed model results against a diverse range of analytical and experimental data. First, a series of simulations are carried out to evaluate the performance of the model in reproducing the results of the wave hydrodynamic and interactions with the submerged trapezoid bar. This is followed by numerically replicating the experimental results of a vertical non-buoyant submerged jet under current-only and current-wave environments. Finally, the potency of the coupled hydro-thermal algorithm is assessed by validating against different thermal-induced buoyant submerged jet experimental tests. For this purpose, numerical prediction of the developed model is tested against physical experiments for a series of tests for thermal-induced buoyant submerged horizontal jets in stationary water and inclined thermal-induced buoyant water jet under the influence of current-wave environments. Results showed that the k-ω SST-multiphase provides the best agreement with the laboratory measured data in terms of flow, temperature distribution field, plume trajectory and dilution. The findings confirmed that the developed model can be used as a reliable tool in precisely modelling characteristic of thermal-induced buoyant water jet in shallow coastal waters.

In the analysis, design, and optimization of a wide range of engineering applications involving stretching surfaces and fluid flow, the skin friction coefficient (SFC) at a stretching surface with heat transfer is an important parameter that reflects the fluid dynamics, heat transfer characteristics, and surface interactions. Owing such importance, the purpose of present article is offer artificial neural networking remedy for evaluation of SFC for Williamson flow field with thermal slip and heat source effects. The Williamson fluid flow is realized by considering surface stretching with an externally supplied magnetic field. The energy equation is used to address the heat transmission. The constructed differential system for flow field is solved by conjecturing artificial neural networking with Lie symmetry and shooting methods. Artificial Neural Networking (ANN) model is developed to predict the surface quantity namely SFC at thermally magnetized surface. The major findings includes the variation in SFC for pertinent flow parameters and we found that in the presence of heat transfer aspects, the SFC admits declining nature towards Weissenberg number while opposite is the case for magnetic field parameter.

Nowadays, electric vehicles (EVs) significantly affect transportation as they provide a more environmentally friendly alternative to traditional fossil-fueled automobiles. Electric vehicles, which depend on energy stored in batteries, significantly contribute to environmental preservation and comply with worldwide efforts to tackle climate change. However, the growing demand for electric vehicles causes traditional power grids under pressure emphasizing the necessity of establishing a suitable infrastructure for charging electric vehicles. Charging stations are becoming increasingly critical since they allow for the recharging of electric vehicles and play a significant role in stabilizing the power system. In order to optimize charging station infrastructure with multiple servers, the current research incorporates a Markovian queueing modeling approach. The primary objective of the study is to address queue management concerns and boost overall productivity. Considering the real-world challenges, a queue-based stochastic model for multi-server EV systems and individual feedback strategies is developed. Subsequently, a transition state diagram is provided by balancing the input-output rates between the adjacent states. Next, the system of Chapman-Kolmogorov differential-difference equations is formulated to help understand mathematical modeling better. The matrix method is employed to demonstrate the state probability distribution in equilibrium. The infographics are utilized and incorporated for better visualization of the research findings. For a better understanding from an individual's point of view, numerous managerial insights are provided. Lastly, several concluding remarks and future perspectives are provided that can help decision-makers and practitioners to construct and analyze economic strategies based on EV management systems.