نشر الاستاذ المساعد الدكتور ازهر محسن عبد بحثا علميا في مستوعبات سكوبس بعنوان Enhancing battery pack safety" against cone impact using machine learning techniques and Gaussian noise" Abstract The increasing prevalence of electric vehicles underscores the need for enhanced battery pack safety, particularly against impacts that can lead to thermal runaway and fires. This study investigates the mechanical and thermal characteristics of battery packs subjected to cone impact on the lower part. Utilizing finite element model (FEM) simulations, we examined the effects of top radius, shell condition, velocity, and angle on the battery pack's response. Five machine learning (ML) techniques were employed to predict the battery pack's behavior under impact, with training data generated from a well-planned Latin hypercube experiment based on FEM dynamic simulations. The accuracy and robustness of the ML models were evaluated under various scenarios, including the introduction of Gaussian noise. Among the models tested, BESA-ELMM (Bald eagle search algorithm-Extreme learning machine model) demonstrated exceptional speed, making it suitable for real-time assessments, while WOA-SVMM (whale optimization algorithm-Support vector machine model) exhibited superior resilience and accuracy, particularly under noisy conditions. Both models, along with the other ML techniques, showed significant effectiveness in predicting the mechanical responses of battery packs to impact. Our findings indicate that ML approaches are highly efficient in evaluating the mechanical effects on battery packs, providing crucial insights for designing safer and more durable battery packs. This study contributes to the advancement of battery safety by demonstrating the potential of integrating ML techniques with FEM simulations to enhance the resilience and impact resistance of battery packs in electric vehicles.

قام الاستاذ المساعد الدكتور ازهر محسن عبد بنشر بحث بعنوان "Techno-economic assessment and transient modeling of a solar-based multi-generation system for sustainable/clean coastal urban development" Abstract To ensure the health of vulnerable coastal ecosystems, a transition to sustainable energy solutions is essential. Environmentally friendly systems powered by renewable sources offer not only a reduction in pollution but also the adaptability needed for a flexible and resilient energy future. This study proposes and comprehensively evaluates an integrated solar-based system designed to meet the daily needs of coastal cities. The proposed system incorporates key components such as dual-loop power cycles, parabolic trough solar collectors, liquefied natural gas (LNG) regasification, reverse osmosis, and proton exchange membrane electrolysis. To optimize energy utilization, the inclusion of a thermoelectric generator (TEG) is considered, harnessing the thermal gradient among the LNG stream and the power cycle fluid. We conduct transient modeling, incorporating comprehensive scenarios that account for both thermal and economic aspects. The performance evaluation of the system focuses specifically on coastal regions, with San Francisco serving as a case study. The dynamic simulation results demonstrate the capability of the integrated system in fulfilling the urban needs for one year, delivering 1,134,207 cubic meters of potable water and generating 11,306 MWh of electricity. Financial analysis reveals that the solar unit accounts for over 46 % of the total cost, with an hourly cost rate of $69.61. The levelized cost of electricity is predicted at 4.61 cents/kWh, while the levelized cost of water is calculated at 30.54 cents/m3. These findings provide valuable insights into the cost-effectiveness and competitive advantage of the system in terms of energy and water production.

نشر الاستاذ المساعد الدكتور ازهر محسن عبد بحث علمي بعنوان "Data-driven study/optimization of a solar power and cooling generation system in a transient operation mode and proposing a novel multi-turbine modification concept to reduce the sun's intermittent effect" Abstract This study explores the integration of solar energy via parabolic trough collectors (PTC) with the Goswami cycle, focusing on evaluating life cycle costs (LCC) and energy performance in both steady-state and transient conditions. A suitable system configuration of a single-turbine Goswami cycle is designed for integration with PTC. Then, in the system's steady-state, a parametric study is performed based on the change in the design components. Subsequently, a multi-objective optimization method using energy and lifetime cost objectives are defined to identify the best system performance. This optimization technique utilizes artificial neural network (ANN) and the grey wolf optimizer (GWO) algorithm. Accordingly, the transient performance of the system is investigated during its optimal operating point using the TRNSYS software. Suitable controllers are considered for regulating power production and cooling system. Annual transient performance graphs of the systems are obtained. Following these investigations, a novel multi-turbine system is proposed. This configuration aims to enhance system reliability and diminish the reliance on storage devices by optimizing performance. The proper configuration of the five-turbine system with the required controllers is also provided. Hence, the method of dividing the turbine into similar turbines with lower capacity is a new concept proposed in this study. In the optimal state, it is observed that for 100 kW of net power output, 161 kW of effective cooling load can be achieved. At this configuration, the system's LCC amounts to 2.91 M. Moreover, during transient operation, an average power of 90 kW is attainable with a cooling load of 120 kW. The findings indicate that modifying the system from single turbine to five-turbines incurs an additional cost of approximately 9.2 % and reduces power output by 6 %. However, this modification significantly mitigates the transient impact of solar fluctuations.