Engineering and Technology Journal

The urgent need for potable water has increased despite water covering much of the Earth's surface. Solar desalination, which uses solar energy to produce fresh water, is an eco-friendly and cost-effective solution, especially for remote areas lacking clean drinking water. This technology is valuable even in deserts with no other freshwater sources. However, conventional solar stills have low efficiency. To address this, various experiments have been conducted to enhance the daily output of solar stills by improving evaporation and condensation rates, which standard distillers cannot achieve. This innovation in using solar power thus finds its place even on desert sands where no other freshwater source is available, demonstrating the immense value of harnessing solar energy for such an important purpose as providing fresh water. This article reviews the latest techniques to boost water productivity and improve various solar distiller designs' overall performance and thermal efficiency. It concluded with recommendations for future research, highlighting strategies that showed significant promise. The combination of flat plate collectors with spherical solar stills was the most efficient, achieving a productivity level of 7620 ml/m2/day. 57% increase due to the unique conditions created by rotating balls within the still. These findings underscore the potential of innovative design enhancements to improve desalination systems using solar energy significantly, and they encourage further research and development in this field.

Wind tunnels are instrumental in the aerodynamic analysis of aircraft model structures, enabling the replication of real circumstances for better design and performance evaluation. This paper presents a novel enhancement to stress distribution predictions in wind tunnel simulations by combining Finite Element Analysis (FEA) and Artificial Neural Networks (ANN). First, the research focuses on analyzing ANSYS Fluent data, which provides insights into the complex fluid dynamics inside the wind tunnel. The proposed approach combines the best available FEA and ANN techniques regarding prediction accuracy and computational efficiency. Such findings are those that evidence that predictions of real stress levels using ANN are quite near, with RMSE 12%, and, hence, quite accurate. The results indicated agreement between the functions generated by ANN and real stress levels and, therefore, were considered to manifest a very low error percentage. The methodology shows that it is significant for being computationally efficient since the ANN works much quicker compared to the conventional FEA approach. In addition, the methodology is significant in computations since the ANN works quicker than conventional FEA. These results thus indicate that the integrated FEA-ANN approach is beneficial and holds much promise in accurately and efficiently predicting stress distributions. Herewith, the provided method advances engineering simulations by making exact predictions of stress distributions necessary to improve design and structural analysis

This review comprehensively encompasses a range of recent studies on journal bearings, emphasizing wear fault diagnostics, condition monitoring, and fault diagnosis methodologies. A significant finding reveals a shift back to the utilization of journal bearings in various rotating machinery such as compressors, motors, turbines, and pumps. Various methodologies employed in these recent studies include vibration analysis, machine learning, deep learning, and both numerical and experimental simulations. Key findings indicate that ensemble models, such as the CNN and deep neural network (CNNEPDNN) model, significantly improve convergence speed, test accuracy, and F-Score in bearing fault diagnosis by 15-20% compared to individual models. Additionally, convolutional autoencoders have demonstrated impressive performance, achieving an average Pearson coefficient of 91% in wear estimation, underscoring the critical importance of predictive maintenance. Despite these remarkable advancements, challenges persist due to the lack of uniform evaluation criteria and the focus on specific error types under particular operating conditions. Collaborative efforts among researchers are essential for developing robust and broadly applicable diagnostic models. Addressing these ongoing issues will further enhance condition monitoring and defect detection, leading to more reliable and academically rigorous diagnostic methods applicable in diverse real-world scenarios.

The thermoelectric cell generators (TECG) convert thermal power into electrical power generated. The main factor in its operation is the temperature difference (TD) between its surfaces. An experimental investigation was conducted into the waste heat recovery performance of the TECG system. Forty thermoelectric cells (TECs) are supported on the (4×5) upper and (4×5) lower and upper arrangement of the engine exhaust manifold. The electrical power generated (EPG), exhaust gas flow rate, exhaust temperature, brake-specific fuel consumption (BSFC), and exhaust emissions were experimentally investigated under different engine speeds and 50 N.m and 100 N.m engine load conditions. Comparative results of the BSFC and emissions have ben measured with and without the TECG system. Experimental results observed that the maximum power values generated are approximately 218.5 W by TECG. An engine BSFC has been reduced by (2.56%:2.89%) under the TECG. The results show the improvement of CO is approximately 1.92% and 5.12% with the TECG system at loads 50 N.m and 100 N.m, respectively, and CO2 emission improvement by 4.32% and 4.52% with the TECG system throughout the speed range and two engine loads. The proposed TECG system, supported by a gasoline engine, is effective under high engine speed and load

This study investigates the natural frequencies and vibrational behavior of aluminum foam sandwich panels by using numerical and analytical methods. The panels consist of an aluminum foam core sandwiched between two aluminum sheets, offering a lightweight yet structurally robust solution, making them ideal for applications in the aerospace and automotive industries. A mathematical model based on classical plate theory (CPT) was developed to compute the natural frequencies of supported rectangular sandwich plates. The Gibson-Ashby equation was employed to estimate the Young's modulus of the aluminum foam core. The analytical model was validated using finite element analysis (FEA) conducted in ANSYS 2021 R1, allowing for a thorough comparison between numerical and analytical results. The results showed strong agreement between the numerical and theoretical analysis, especially at high foam densities. The discrepancies between the numerical simulation and analytical predictions decreased with increasing foam density. For instance, at a density of 850 kg/m³, the difference between the numerical natural frequency (674 Hz) and the analytical prediction (681.75 Hz) was only 1.14%. In contrast, at a lower density of 350 kg/m³, the discrepancy increased to 8.52%, with numerical and analytical frequencies of 739.66 Hz and 808.51 Hz, respectively. This trend can be attributed to the complexities in the material behavior at lower densities, which the analytical model simplifies by neglecting nonlinear deformations and complex stress distributions. As foam density increases, the material exhibits more consistent mechanical properties, resulting in closer alignment between numerical and analytical results. Moreover, higher foam densities contribute to an increase in mass, which negatively affects the natural frequency, causing it to decrease. Conversely, an increase in Young's modulus enhances the stiffness of the material, resulting in higher natural frequencies. Therefore, the optimal foam density range of 350 to 450 kg/m³ is crucial for achieving a good balance between stiffness and weight. Maintaining a lightweight structure while improving stiffness is essential for achieving optimal performance. Consequently, these panels are particularly suitable for applications in the aerospace and automotive sectors that require lightweight, high-performance structures.

The implementation of nanoparticles in solar thermal conversion devices has long been a challenge. Consequently, employing ferrofluids combined with magnetic fields has been an intriguing investigation. The magnetization of magnetic nanoparticles, which noticeably enhances the thermal conductivity of ferrofluid, is one of the principal effects of the magnetic field. Given that the compelling behavior of ferrofluid in the presence of external magnetic fields supplied by either permanent magnets or current-carrying wire as the primary source of magnetic field and the significant effect of this combination on the thermal performance of solar devices, the current review focused on introducing research involving various collections of nanoparticles, ranging from solid metal to metal oxide and hybrid nanoparticles, and demonstrating their effect on the thermal conductivity. Based on the literature, results indicated that in particular cases, by applying a magnetic field in the range of 0.02-1T via permanent magnet and in the presence of hybrid nanoparticles Mn-Zn Fe2O4/H2O for 0.5 vol%, the highest improvement in the obtained efficiency of the thermal is about 47%. Contemporary, the best performance of 74% was obtained for 4 vol% of Fe3O4/Therminol 66 ferrofluid under a 500 G magnetic field supplied by current carrying wire

This study demonstrates the effect of quenching medium, solution treatment, and aging parameters on the mechanical properties of Aluminum alloy 6061. To select an optimal quenching medium from three distinct quenching media (corn, sunflower, and sesame oil), solution heat treatment was conducted on three impact test samples at 545 ℃ for 2 hours, and then quenching was conducted in the mentioned quenching medium. Consequently, sesame oil demonstrated the lowest shock absorption rate; thus, sesame oil was selected as the quenching medium. As a result, every sample for tensile, hardness, and impact tests underwent solution heat treatment at 545 ℃ for 2 hours, followed by quenching in sesame oil to room temperature, and subsequent artificial aging at temperatures of 180 ℃, 195 ℃, and 210 ℃ for 1, 2, 4 hours. Considerable enhancements in the mechanical properties were observed in tensile strength value and hardness while the ductility decreased. It was observed that the best result was achieved by aging at 195 ℃ for 4 hours, increasing the ultimate tensile strength to 351.413 N/mm2 from the initial value of 260 N/mm2 for the as-received specimen; this is attributed to the formation of finely dispersed grains. In contrast, the ultimate tensile strength for the annealing condition decreased to 152.020 N/mm2 due to forming a coarser grain size. Furthermore, the highest hardness value achieved is 39.10 HRB, achieved by aging at 195 ℃ for 4 hours, compared to the hardness value achieved by annealing conditions at 18.23 HRB.

This study applied a population-based metaheuristic algorithm to optimize liquid nitrogen pumping in hydrocarbon and allied fluids piping systems to ensure optimal process efficiency, minimize energy consumption, and minimize operational costs without extensive computational tasks while considering uncertainties and variability. Liquid nitrogen pumping in hydrocarbon and allied fluids piping systems involves a heat and mass transfer process where by liquid nitrogen flows from the storage tank to the pump unit and is converted to the gaseous state in an integrated heat exchanger. The continuous high pressure from the triplex reciprocating pump pushes the gaseous nitrogen to the hydrocarbon and allied fluids piping systems. Hence, the pumping is for purging, inerting, or pneumatic testing of the piping systems. Thus, the pumping process was parameterized and parametrically characterized to define the performance and control parameters quantitatively. Surrogate optimization models were formulated using an efficient response surface method. Since the models are multiparametric, multi-objective genetic algorithms were utilized to provide global optimal solution sets for the models. The results show that 3.79% decrease in pressurized volume of the piping systems, 1.48% decrease in test pressure of the piping systems, 3.42% increase in maximum discharge pressure of the liquid nitrogen pump and 1.08% increase in maximum flow rate of the liquid nitrogen pump across test packs increased total volume of liquid nitrogen pumped by 40.00%, decreased test duration by 0.70%, decreased pumping duration by 0.70% and increased total volume of liquid nitrogen used by 8.00% and vice versa.