Arabian Journal for Science and Engineering最新文献

The remarkable properties of Casson fluid significantly contribute to its importance in technical and industrial sciences, establishing it as an essential variety among non-Newtonian fluids. Lorentz’s force is a crucial study area with profound implications for science and technology. It enhances our understanding of physical laws, drives technological innovation, and improves industrial processes. This study employs the finite element method to analyze the behavior of Casson fluid flow, applying thermal and velocity constraints in a square domain with a sinusoidal shape, and incorporating the effects of an inclined Lorentz force. The chamber has a sinusoidal pattern on its bottom and upper borders. The vertical and bottom borders are kept at consistently lower and higher temperatures, respectively, while no heat is transferred from the top border. Lorentz’s force is applied at an angle opposite the clockwise direction. The energy transport and velocity distribution mechanism are examined precisely for a wide range of pertinent factors of magnetic field (Ha), buoyancy force (Ra), Prandtl number (Pr), Casson factor (β), magnetic orientation (γ), and wave number (m). The results indicate that increasing the Lorentz force reduces the heat transfer through conduction, which is the dominant mechanism for fluid movement. When the buoyancy force increases, the maximum heat transport rate of about 135.86% occurs at m = 2. At low β, the strength of the stream function is weak due to the conduction mechanism of thermal transfer. For the increase in β, a higher value of approximately 118.50% of the heat transfer amount is observed at wave number 2. The results derive a new linear regression equation with various variables. Comparisons are conducted with existing literature, and the data agree.

This paper presents a comprehensive work involving testing, modeling and application of composite material produced from a vertical induction furnace using unsorted plastic waste as raw material. The work is novel because studies based on such a unique combination, i.e., between the raw material and the melting equipment, are not available in the literature. Here, the solid phase of the composite material from the furnace was targeted for tensile testing, where uncertain strain–stress relationships were found, mainly indicated by different tensile strengths between 3.5 and 6.69 MPa. In response to such uncertainty, a material model is proposed here by using a statistical approach to capture random microstructures of such a composite material. Meanwhile, to support the tensile tests, material characterizations were carried out using SEM, EDX and Raman spectroscopy, which not only revealed the presence of particle impurities but also provided information on chemical elemental compositions. Finally, a promising application of such a composite material in a house building project is presented.

With the use of hydrogen as an energy source, mitigation of its leakage in the environment is critical. Therefore, it is required that passive autocatalytic hydrogen–oxygen (H–O) combination systems are developed to combine this hydrogen with oxygen from the air. In the present study, reduced graphene oxide (rGO) sheets loaded with platinum nanoparticles have been developed and studied for their performance as catalysts for hydrogen dissociation as well as its combination with oxygen. The formation of platinum particulates on rGO substrate was confirmed by XRD and SEM. Crystallite sizes of platinum nanoparticles were in the range of 7.8–8.3 nm. However, the platinum particles had spherical morphology with an average particle size of 90 nm when seen through SEM. The particle size distribution was almost similar irrespective of platinum loading. At lower platinum contents, the number density of platinum particles was lower but as the number density of the particles increased with an increase in loading they tended to agglomerate. These platinum-loaded rGO samples were employed as catalysts for hydrogen dissociation kinetic studies using hydrogen deuteride formation as an indicating tool. It was found that the dissociation rate per unit mass improved with an increase in platinum loading up to 11 wt% (Pt11@rGO) and then decreased. When used for (H–O) combination reaction, Pt11@rGO demonstrated the highest combination rates per unit mass of catalyst. Consequently, Pt11@rGO catalyst gave the best performance with regard to the economic use of precious metals for catalytic application. This catalyst may be a potential candidate for traditional as well as futuristic hydrogen mitigation applications.

The growing demand for disinfection of polluted waters from different sources has led to the development of new and more effective water purification technologies. Photocatalysis holds great promise as an efficient and sustainable oxidation technology for application in wastewater treatment. Coordination polymers CPs have attracted ever growing research interest as a new kind of potential photocatalyst because of their unique properties of designable structure. New coordination polymers have been prepared by condensation of terephthalic acid with iron(II) and iron(III) ions. The corresponding structures have been described by infrared spectroscopy (FTIR), ultraviolet–visible spectroscopy, X-ray powder diffraction and thermogravimetric analysis. The obtained data revealed that the Iron(II) polymer exhibits an infinite one-dimensional chain structure; while, Iron(III) polymer exhibits a three-dimensional metal–organic framework structure. The FTIR results suggested that the terephthalic acid was coordinated to the metal center as a dibasic tetradentate manner. The behavior of thermal decomposition for the prepared polymers was discussed. The kinetic and thermodynamic parameters have been evaluated using the Coats–Redfern method. The photocatalytic performances of the polymers for decomposition of methyl orange under UV irradiation were studied. The effect of parameters such as addition of H₂O₂ and pH of the dye solutions on the removal efficiency of methyl orange was also investigated. The prepared polymers promising photocatalytic activities for the degradation of methylene orange in water at room temperature.

The number of gas flow channels in a serpentine-type channel configuration for Polymer Electrolyte Membrane Fuel Cells (PEMFC) is a critical design parameter. It influences mass transport, pressure drop, and water management, all of which contribute to the overall performance and efficiency of the fuel cell. In this study, different channel number configurations for small active area fuel cell and their role in contributing to a more sustainable energy environment are discussed. The influence of the number of multiple channels on the operational performance was examined in a fuel cell with 25 cm² of active area. Six different flow channel configurations belonging to the traditional serpentine-designed flow channel were utilized, with multiple inlet–outlet structures. Numerical calculations for pressure, velocity, distribution of reactants (oxygen and hydrogen), membrane water content, and changes in water saturation concentration were conducted using the ANSYS Fluent program. The highest power density of 0.657 W/cm² was achieved in the single-channel design, resulting in a 14% performance increase compared to the eight-channel design, which exhibited the lowest performance. However, the highest pumping loss due to pressure drop was observed in the serpentine one-channel design at 0.016573 W/cm². While the pressure drop enhances performance in the same channel design, when constructing a fuel cell stack with a large number of cells, significant difficulties may arise in procuring a compressor capable of providing the desired pressure and flow rate. Therefore, alternative designs with reduced pressure drop need to be considered.

The rotor of the permanent magnet synchronous motor develops localized high temperatures at high-torque or high-speed operating conditions so that the demagnetization failure phenomenon may occur. To address this problem, a rotor temperature prediction model based on long-and-short-term memory (LSTM) neural networks is proposed. In addition, the effects of several hyperparameters on the network construction are investigated. To better improve the accuracy of prediction results, Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) are used to optimize the construction of the network parameters. The results of the study show that the LSTM model has a large error throughout the process, which ranges from − 2.66–6.64 °C. GA-LSTM has an error of − 1.71 ~ 3.91 ℃ throughout the process. The error of PSO-LSTM is − 1.78 ~ 0.96 ℃. Additionally, the proposed PSO-LSTM prediction model exhibits good accuracy and stability with RMSE of 0.7114 and MAPE of 1.22%.

A deformable robot is a new type of robot that utilises a metamorphic mechanism into the field of robotics and switches between vehicular and humanoid states during reconstruction processes. In the process of driving reconstruction of the deformable robot, the nonlinear vibration will be generated due to the excitation of uneven roads, and tipping instability of the deformable robot may occur. To address this phenomenon, the effect of road excitation on the stability of the deformable robot during driving reconstruction process was investigated based on a nonlinear dynamics analysis method. In this study, a kinematic model for the coupled reconstruction process of a deformable robot was developed using the homogeneous coordinate transformation method. A nonlinear dynamics equation for the variable-centre-of-mass system of the robot was derived for the driving reconstruction process. Nonlinear dynamics analysis methods were applied to analyze the nonlinear response and the change of the system stability of the deformable robot driving reconstruction under the influence of different road excitation amplitudes, excitation frequencies, and reconstruction speeds. The accuracy of the established nonlinear dynamics model was further verified through experiments. It is shown that when the road excitation amplitude exceeds (0.07;{text{m}}) or the excitation frequency exceeds (7.2;{text{Hz}}), irregular chaotic motion as well as tipping instability may occur. With the increase of reconstruction time, the stability of the deformable robot is enhanced during driving reconstruction. When the reconstruction time exceeds 18 s, the driving reconstruction of the deformable robot can be stably executed on typical roads.

In this theoretical investigation, two-photon decay of an amplitude-modulated Gaussian laser ({(omega }_{0},{k}_{0})) into a pair of electromagnetic waves with frequencies ({omega }_{1},{ omega }_{1}) and wave numbers ({k}_{1},{k}_{2}), respectively, is shown to be susceptible in the presence of a Langmuir wave (left({omega }_{r},{k}_{r}right)) in a density rippled unmagnetized plasma. The decay waves propagate in sideward direction so that the momentum remains conserved. The density ripple plasma compensates for the wave number mismatch between pump and decay waves; otherwise, such decay of electromagnetic wave is not allowed. The necessary phase matching conditions for the interaction are considered as ({overrightarrow{k}}_{r}={overrightarrow{k}}_{0}-{overrightarrow{k}}_{1}-{overrightarrow{k}}_{2}, {omega }_{r}={omega }_{0}-{omega }_{1}-{omega }_{2}). The discrepancy can be attributed to the static density ripple. The momentum transfers between the pump and decay wave by the density ripple. The decay of pair of electromagnetic waves occurs at plasma densities below one-fourth of the critical density. The decay waves propagate at finite angles to the amplitude-modulated Gaussian laser beam. The obtained growth rate is strongly dependent on the rippled density and laser beam modulation index. At the centre of laser beam propagation transverse distance, the growth rate of decay wave is attained the peak value. The various graphical diagrams show that growth rate decreases with the increase in the normalized frequency of pump laser beam and angle ({theta }_{r}).

Long-term accurate forecasting of the various sources for the electric energy production is challenging due to unmodelled dynamics and unexpected uncertainties. This paper develops non-parametric source models with higher-order polynomial bases to forecast the 16 sources utilized for the electric energy production. These models are optimized with the modified iterative neural networks and batch least squares, and their prediction performances are compared. In addition, for the first time in the literature, this paper quantifies the unseen uncertainties like the drought years and watery years affecting especially the hydropower and natural gas-based electric energy productions. These uncertainties are incorporated into the parametric imported-local source models whose unknown parameters are optimized with a modified constrained particle swarm optimization algorithm. These models are trained by using the real data for Türkiye, and the results are analysed extensively. Finally, 10 years ahead estimates of the 16 imported-local sources for the energy production have been obtained with the developed models.

With the growing urge to decarbonize the energy sector, actions toward reducing emissions of the oil and gas sector can contribute to bringing large cuts to carbon emissions. One of the routes to achieve this goal is sustainable hybrid energy systems involving renewable energy sources integrated with conventional energy systems. Employing solar energy to drive crude oil refineries is one of the investigated pathways for using renewable energy sources to support lowering the carbon emissions and environmental impact of operating the processing of fossil-based fuels. This paper proposes a solar-assisted method for a petrochemical refinery, considering hydrogen production deployed in Yanbu, Saudi Arabia, as a case study to greenize oil refineries. The integrated technologies are: concentrated solar tower, radiative heat tube, steam power cycle, hybrid solar and oil-fired steam generator, and alkaline electrolysis. The system is analyzed thermodynamically to provide a good understanding of its control and performance. An optimized design of the heliostat field, solar tower, and solar receiver is implemented as part of the hybrid system. Based on the solar data for the selected case study, the fuel oil consumption rate and the temperature profile are determined. It is found that the proposed integrated system can save about 131 tonnes per day of fuel oil, thus mitigating the emission of 414 tonnes per day of carbon dioxide while maintaining energy and exergy efficiencies of 82.4% and 55.5%, respectively. These results present an opportunity for the urging transition to more sustainable energy systems.