Bilel Najlawi

This study addresses the critical challenge of ensuring railway vehicle system (RVS) safety and comfort when traveling on short-radius curved tracks. Traditional deterministic optimization methods often fail to account for the inherent uncertainties in design variables (DVs), which can significantly affect a vehicle’s performance. The objective of this work is to develop a robust optimization framework that simultaneously considers RVS safety and comfort while accounting for these uncertainties. To achieve this, an algorithm combining a multi-objective improved colonial competitive approach (MICCA) and the polynomial chaos expansion method (PCEM) is developed. This robust optimization process integrates RVS safety and comfort, their standard deviations, and the uncertainties of the DVs into a single framework. The obtained results demonstrate that the robust design significantly reduces the sensitivity of the railway vehicle’s performance to design variable uncertainties, outperforming deterministic approaches and existing literature findings.

Water is one of humanity’s most fundamental needs. The demand for freshwater rises in tandem with population expansion. Only 0.01 percent of freshwater is available as surface water in lakes, wetlands, and rivers. As a result, the only choice is to extract water from the oceans. Desalination is an effective option for this. This study focused on the multi-objective design optimization, fabrication, and thermal evaluation of an integrated desalination system combining a solar still with a flat plate collector (SS-FPC). The study investigated the trade-off between two competing objectives: maximizing the efficiency of the SS-FPC system while minimizing its total cost. A numerical code is written in MATLAB to simulate the influence of changing design parameters (DPs) on the SS-FPC performances. The optimal SS-FPC design, offering low costs and a high thermal efficiency, was identified using the multi-objective colonial competitive algorithm (MOCCA). This design was subsequently fabricated and experimentally evaluated under the climatic conditions of Unaizah in Al Qassim, Saudi Arabia. The optimal numerical results were compared with both the literature values and experimental measurements. The comparison revealed strong agreement with the experimental data, with a maximum relative error of 4%. Moreover, the obtained results indicate that the optimized SS-FPC design is capable of achieving a 31% increase in efficiency and a 49% reduction in total cost relative to those reported in the literature.

The high expenses of electrical energy needed for heating water can be significantly decreased by using flat plate collectors (FPCs). On the other hand, solar thermal systems offer a fair payback period and the capacity to significantly cut fuel usage. As a result, the design of FPCs having optimal performance has received a lot of attention in recent research projects. However, because there are uncertainties in material qualities, geometry, and environmental variables, it is critical to include uncertainty analysis into design optimization to achieve trustworthy results. In this work, a multi-criteria robust design for multiobjective optimization (MRDMO) of the FPC is developed where uncertain design parameters (UDPs) are considered. A combined multi-objective colonial competitive algorithm (MOCCA) and polynomial chaos expansion method (PCEM) is developed and used for the MRDMO of the FPC. The MRDMO simultaneously considers four objectives functions including the FPC efficiency, the FPC cost, and their sensitivities to UDP. The results indicated that the FPC design achieved by determinist optimization had 30% efficiency sensitivity and 27% total cost sensitivity. However, the MRDMO offers a robust FPCs design having comparable optimum performances to the deterministic approach but with significantly reduced sensitivity to UDPs. Furthermore, the performances sensitivities of the robust FPCs were decreased by 62% when compared to the literatures results.