Assist. Prof. dr. Azher Mohsin Abd has published a scientific research titled "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.

Assistant Professor Dr. Azhar Mohsen Abd has published a research paper titled "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.

Assistant Professor Dr. Azher Mohsin Abd had published a research paper titeled ''An approach of analyzing gas and biomass combustion: Positioned of flame stability and pollutant reduction" Abstract Biomass, as a renewable energy source, has gained attention as a sustainable alternative to conventional fuels due to its global availability and usability in rural areas. However, biomass combustion presents challenges such as flame instability and pollutant emissions. This study compares the use of methane gas, wood shavings, and a combination of these two fuels, examining the effects of preheating and flame stability in the combustion of natural gas, biomass, and their co-firing using numerical methods. An equivalence ratio of 1 has been used to avoid a rich-fuel mixture, and an air mass flow rate of 0.0001 kg/s are considered to ensures a sufficient supply of oxygen for the combustion process. Biomass fuel particles are injected from the surface of the fuel and its particle diameter ranging between 5 and 10 μm. The results showed that using the combined fuel not only increased the flame temperature at the beginning of the combustion chamber but also achieved more complete and stable combustion, with over 97 % of the fuel consumed at the start of combustion. As a result of this complete combustion, the emissions of toxic gases CO and NOx in the combustion products reached zero. Additionally, preheating the air increased the flame temperature by 14 %, while preheating the fuel reduced NOx emissions by 24 %.