A numerical investigation of a phase change material (PCM) energy storage tank working with carbon nanotube (CNT)–water nanofluid is performed. The study was conducted under actual climatic conditions of the Ha’il region (Saudi Arabia). Two configurations related to the absence or presence of conductive baffles are studied. The tank is filled by encapsulated paraffin wax as the PCM, and CNT–water nanofluid flows through the capsules. The main goal is to increase the temperature of the PCM to 70 °C in order to store the thermal energy, which can then be used during the night and cloudy weather. Numerical computations are made using the finite element method (FEM) based on actual measured weather conditions. Climate conditions were collected from a weather station located on the roof of the engineering college’s building at the University of Ha’il. The collected data served as input to the numerical model, and the simulations were performed for three months (December, March, and July). The solid CNT volume fraction range was (0 ≤ ϕ ≤ 0.05) and the nanofluid volume flow rate ranged was (0.5 L/min ≤ V ≤ 3 L/min). For both considered cases (with and without baffles), it was found that the use of CNT–nanofluid led to a reduction in the charging time and enhanced its performance. An increase in the volumetric flow rate was found to accelerate the melting process. The best performances of the storage tank occurred during July due to the highest solar irradiation. Furthermore, it was found that the use of baffles had no beneficial effects on the melting process.

Paying close attention to the environmental risks associated with traditional forms of energy, particularly the serious issue of global warming caused by the release and concentration of greenhouse gases, has become a paramount concern. This growing awareness has prompted governments worldwide to prioritize the benefits of alternative energy sources and develop comprehensive plans accordingly. Moreover, it is crucial to thoroughly consider and explore existing potentials in our quest for sustainable energy solutions. One such potential lies in the energy loss experienced due to gas pressure reduction in urban gas reduction stations. Furthermore, as we increase the amount of natural gas preheating, we observe a corresponding rise in the temperature within the expansion turbine, ultimately resulting in a significant boost in power generation. In this paper, we delve into the exciting possibility of harnessing electricity generation by utilizing a turbo expander within a municipal gas pressure reduction station. To maximize the work generation capacity, we propose preheating the gas input to the turbo expander using geothermal energy. Our study reveals that the turbo expander exhibits optimal performance, boasting an impressive isentropic efficiency of 92.3%, when the inlet temperature is maintained at 84 °C and the amount of preheating reaches 192.3 kJ/kg.

This work aims to analyze the effects of the main process parameters of fused filament fabrication (FFF) on the mechanical properties and part weight of 3D-printed thermoplastic polyurethane (TPU). Raster angle (RA), infill percentage (IP), and extruder temperature (FFF) in the ranges of 0–90°, 15–55%, and 220–260 °C, respectively, were considered as the FFF input parameters, and output variables part weight (PW), elongation at break (E), maximum failure load (MFL), ratio of the maximum failure load to part weight (Ratio), and build time (BT) were considered as responses. The Response Surface Methodology (RSM) and Design of Experiments (DOE) were applied in the analysis. Subsequently, the RSM approach was performed through multi-response optimizations with the help of Design-Expert software. The experimental results indicated a higher maximum failure load is achieved with an increased raster angle and decreased extruder temperature. ANOVA results show that ET has the most significant effect on elongation at break, with elongation at break decreasing as ET increases. The raster angle does not significantly affect the part weight of the TPU samples. The ratio of the maximum failure load to part weight of samples decreases with an increase in IP and ET. The results also indicated that the part weight and build time of FFF-printed TPU samples increase with an increase in IP. An ET of 220 °C, RA of 0°, and IP of 15% are the optimal combination of input variables for achieving the minimal part weight; minimal build time; and maximum elongation at break, maximum failure load, and ratio of the maximum failure load to part weight.

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.

In this article, we provide a metaheuristic-based solution for stability analysis of nonlinear systems. We identify the optimal level set in the state space of these systems by combining two optimization phases. This set is in a definite negative region of the time derivative for a polynomial Lyapunov function (LF). Then, we consider a global optimization problem stated in two phases. The first phase is an external optimization to search for a definite positive LF, whose derivative is definite negative under linear matrix inequalities. The candidate LF coefficients are adjusted using a Jaya metaheuristic optimization algorithm. The second phase is an internal optimization to ensure an accurate estimate of the attraction region for each candidate LF that is optimized externally. The key idea of the algorithm is based mainly on a Jaya optimization, which provides an efficient way to characterize accurately the volume and shape of the maximal attraction domains. We conduct numerical experiments to validate the proposed approach. Two potential real-world applications are proposed.