In the present study, the effects of reduced gravity and solar radiation on the magnetohydrodynamics (MHD) fluid flow and heat transfer past a solid and stationary sphere embedded in a porous medium are investigated. A model describing the considered configuration is put in dimensionless form using appropriate dimensionless variables and then transformed to primitive form for a smooth algorithm on a computing tool. A primitive form of the model is solved by employing the finite difference method. Solutions for variables of interest, such as velocity distribution and temperature field, along with their gradients, are depicted in graphs and tables. The main goal of the paper is to study the physical impact of reduced gravity on heat transfer and fluid flow around a sphere surface inserted in a porous medium in the presence of an applied magnetic field and solar radiation. The effects of the governing parameters, which are the reduced gravity parameter, magnetic field parameter, radiation parameter, porous medium parameter, and the Prandtl number, are discussed and physically interpreted. The displayed solutions indicate that velocity rises with the reduced gravity and solar radiation parameters but decreases with augmenting the Prandtl number, magnetic field parameter, and porous medium parameter. It is deduced from the presented results that the temperature becomes lower by increasing the values of the reduced gravity parameter and the Prandtl number, but, on the other hand, it becomes higher by increasing the values of the magnetic field, the porous medium, and the radiation parameters at all the considered positions of the surface of the sphere. A comparison between the present and already published results is performed to check the validity of the proposed numerical model.

The computational fluid dynamics (CFDs) models based on the steady Reynolds-averaged Navier–Stokes equations (RANSs) using the $k-\omega$ two-equation turbulence model are considered in order to estimate the wind flow distribution around buildings. The present investigation developed a micro-scale city model with building details for the Hail area (Saudi Arabia) using ANSYS FLUENT software. Based on data from the region’s meteorological stations, the effect of wind speed (from 2 to 8 m/s) and wind direction (north, east, west, and south) was simulated. This study allows us to identify areas without wind comfort such as the corner of the building and the zones between adjacent buildings, which make this zone not recommended for placement of restaurants, pedestrian passages, or gardens. Particular attention was also paid to the highest building (Hail Tower, 67 m) in order to estimate, along the tower height, the wind speed effect on the turbulence intensity, the turbulent kinetic energy (TKE), the friction coefficient, and the dynamic pressure.

Due to the rapid urbanization of many cities around the world, industrial manufacturing plants have been expanded quickly, leading to the discharge of large amounts of pollutants into the environment. Consequently, a significant deterioration in local air quality is recorded, representing a high health risk for the city's residents. In this context, the main objective of this work is to understand the dispersion of gas pollution in high-density urban environments, specifically the Hail region of Saudi Arabia. The simulations carried out with Ansys Fluent 19.0 were based on actual climatic conditions, with particular attention paid to accurately reproducing the exact topography of the study area. The main results concern the characterization of flow behavior and the dispersion of gas pollutants emitted by power plants. Several factors, including building geometry and wind speed, are examined. The study reveals that for a reference wind speed of more than 7 m/s, gaseous pollution exhibits a significant tendency to accumulate within buildings, resulting in significant concentrations.

Using the RANS approach with the standard k-ω turbulence model, this study offers a novel investigation into the dynamic and thermal properties of turbulent impinging jet arrays. Our study examines the combined effect of the number of jets (N) and the jet–jet spacing (S) on flow mechanisms and heat transfer performance, which is unique compared to previous research that frequently focuses on the individual effects of parameters. Through the investigation of the turbulent kinetic energy, friction coefficient, velocity contours, streamlines, pressure contours, and local and mean Nusselt numbers, we provide important information about how these parameters impact flow dynamics. Local heat transfer in the central and lateral zones is greatly improved by increasing the number of jets (N) and the jet–jet spacing (S), according to our findings. When the jet–jet spacing (S) is increased from 1 to 4, the maximum value of the Nusselt number along the central zone improves by 21.2%. Furthermore, the best improvement in the maximum Nusselt number (24.5%) along the lateral zone is obtained by increasing the number of jets (N) from 5 to 11 for the lower value of jet–jet spacing S = 1. It has also been noted that lower jet-plate distance (H), lower jet–jet spacing (S), and a higher number of jets (N) result in better average heat transfer. To predict the average Nusselt number based on three parameters (N, S, and H), we establish a critical correlation, which provides a useful tool for optimizing impinging jet configurations in a variety of engineering applications. The diversification of the parameters studied and the thorough analysis in this study add important new results to the field by demonstrating the significant effects of the number of jets, jet–jet spacing, and jet-plate distance on the thermal and dynamic behavior of impinging jet arrays.

A combined turbulent wall jet and offset jet (also known as the dual jet) with and without the presence of a parallel co-flow stream is studied. The standard k-omega turbulence model is used to predict the turbulent flow. The study focuses on the effects of the co-flow velocity (CFV) on the heat-transfer characteristics of the dual jet flow with the bottom wall maintained at a constant wall temperature. The CFV is varied up to 40% of the jet inlet velocity, and the height of the offset jet is varied from 5 to 11 times the jet width with the inlet Reynolds number taken as 15,000. The heat-transfer results reveal that the local Nusselt number (Nu(x)) along the bottom wall exhibits a peak at the immediate downstream of the nozzle exit, followed by a continuous decay in the rest of the converging region before showing a small rise for a short streamwise distance in the merging region. Further downstream, in the combined region, Nu(x) gradually decreases with the downstream distance. Except the merging region, no influence of co-flow is observed in the other two flow zones (converging and combined regions). In the merging region, for a given offset ratio (OR), Nu(x) remains nearly constant for a certain axial distance, and it decreases as the CFV increases. As a result of the increase in the CFV, the average Nusselt number decreases, indicating a reduction in overall convective heat transfer for higher values of the CFV. A regression analysis among the average Nusselt number ((Nu)over bar), CFV, and OR results in a correlation function in the form of ((Nu)over bar) = e(7.5383) x OR-0.00073 x (CFV + 10)(-0.0204) within the range OR = 5-11 and CFV = 0-40%.

In this study, a computational fluid dynamics analysis is performed to explore how the number of nozzles (N) and the impingement height (H) influence heat transfer (HT) within an impinging jets array (IJA). To simulate the dynamic flow and the HT characteristics, the two-equation k–ω turbulence model was used. The results show that as the number of nozzles increases and impingement height decreases, local and mean Nusselt numbers increase. The greatest enhancement in the average Nusselt number (Nuavg) when reducing the impact height from H = 8 to 2 (57.4%) is noted for a maximum number of nozzles (N = 9). On the other hand, choosing a minimum impact height (H = 2) ensures a maximum improvement in (Nuavg) by 127% following the variation in the number of nozzles from 1 to 9. The streamlines contours of the turbulent flow indicate the presence of two types of vortices, simple vortices and counter-rotating vortices of different dimensions depending on (N) and (H) values. Non-dimensional velocity and static pressure contours highlight the complex interactions between the impingement plate and the flow, and the considerable effects of the number of nozzles and impingement height on the HT and turbulence levels. The linear regression method is adopted to estimate the average Nusselt number along the plate, and correlation given by this method shows moderate agreement with the numerical results. To improve the accuracy of the estimated values, artificial intelligence techniques are applied to find an optimal machine learning model for predicting the average Nusselt number based on a set of input features, including (N) and engineered polynomial transformations of (H). Modeling was accomplished using deep learning through a multi-layer neural network to capture complex relationships. The Adam optimizer and the mean squared error loss function were used to train the model, comparing predictions with actual simulation results. This research offers important insights into the design and optimization of the IJA for cooling applications.