With the increasing number of hydropower plants under construction and proposed in Nepal, the country is anticipated to experience a surplus of hydropower that exceeds its peak load demand. This surplus electricity becomes particularly high during the wet seasons, when hydropower production reaches its maximum capacity. This research focuses on the mathematical modeling of an alkaline electrolyzer, specifically analyzing the stack performance and the electricity flow within the balance of plants required to support the stack operation. The developed model is then used to estimate the production cost of hydrogen by utilizing forecasted surplus electricity up until the year 2030. The output of the study is expected to help in the sustainable utilization of surplus hydropower in the country, thus enhancing the low carbon economic development path. The study shows that there is a significant opportunity for hydrogen production from surplus hydroelectricity, ranging from 91 ktonne/year to maximum, of 414 ktonne/year. The average levelized cost of hydrogen is estimated at 5.65 USD/kg. The cost can be further reduced if policy interventions like tax rebates and tariff rate subsidies are in place.

The main goal in this research is to train various machine learning models to predict charging cycles in EV Electric Vehicles) battery systems. The considered models are gradient boosting, random forests, decision trees, and linear regression. Each of these was assessed based on its R-squared score, which is an important statistical measure in indicating the variance proportion yielded by the model. In contrast, the Random Forest model significantly improved, with an R-squared value of 0.83, thereby doing an excellent job in capturing nuances of the data. Only surpassed by the Gradient Boosting model at an astonishing R-squared score of 0.87, it is this excellent score that underlines its capability to predict the outcome quite accurately by modeling complex interrelations. In other words, gradient boosting outran the rest and provided the most robust results concerning drivers of students' performance. It also underlines how important choosing a good model is in educational analytics in order to increase the accuracy of the predictions. The use of these models in the proposed EV Battery Charging Cycle Predictor App results in accurate predictions to aid schedule maintenance and energy-related decisions. This research brings light to the future of advanced machine learning methods in enhancing the battery efficiencies of EVs and the development of electric mobility technologies. It is possible that the future work will imply the additional inclusion of real data and the integration of the application to general energy systems.

Alkaline water electrolysis (AWE) operated by surplus electricity is suitable for producing green hydrogen in Nepal. Simulation models are built using DWSIM software for AWE, multistage compression, and the Organic Rankine Cycle (ORC). The AWE system's Capital Expenditure (CAPEX) is determined to be $47 million and Operational Expenditure (OPEX) of $7.65 million/year. The storage system, including the multistage compression system and Type IV cylinders, has a CAPEX of $52 million and an OPEX of $17 million/year. The ORC has a CAPEX of $500,000 and an OPEX of $200,000/year. The thermal power generated from AWE and multistage compression can be converted to electricity by the ORC and supplied to the AWE system. This process decreases the Levelized Cost of Hydrogen (LCOH) from $3.5141/kg over 5 years to $3.4725/kg over 25 years. The techno-economic analysis performed confirms the feasibility of implementing these plants in Nepal.

Additive manufacturing of NiTi shape memory alloys has attracted attention in recent years, due to design flexibility and feasibility to achieve four-dimensional (4D) function response. To obtain customized 4D functional responses in NiTi structures, tailorable phase transformation temperatures and stress windows as well as one-way or two-way shape memory properties are required. To achieve this goal, various heat treatments, including direct aging, annealing and annealing followed by aging, were optimized for the Ti-rich NiTi (Ni49.6Ti (at. %)) fabricated by laser powder bed fusion (L-PBF). Microstructural evolution, phase transformation, precipitation and shape memory behaviour were systematically investigated by multiscale correlative microstructural, differential scanning calorimetry analysis and thermomechanical analysis. Based on optimized heat treatments, ∼25 K phase transformation temperature windows and ∼90 MPa stress windows were achieved for the one-way shape memory effect. Solutionized annealing was found to be the most effective way to improve one-way shape memory degradation resistance, due to the reduction of defects and solid solution strengthening. One of the main findings of this study is that the heterogonous microstructures between hard intergranular Ti₂NiO_x and soft NiTi matrix, induced by solutionized annealing with subsequent aging, result in strain partitioning and enclosing the internal stress state, which was found to promote a pronounced two-way shape memory effect response. The results of this work provide in-depth knowledge on tailoring and designing functional shape memory characteristics via heat treatments, which contributes to expanding L-PBF NiTi application fields, such as biomedical implants, aerospace components, and other advanced engineering applications.