A numerical analysis was carried out into the coupled heat and mass transfer processes in an elliptical reactive hydride composite (RHC) reactor (ERHCR) bed packed with high-temperature RHC material (2Li_RHC + 1TiO2). The performance of the H2 storage reactor was examined by varying operational parameters such as heat transfer fluid (HTF) inlet temperature and H2 supply pressure. The effects of variations in H2 supply pressure and HTF inlet temperature on the average bed temperature and H2 fraction are presented. In the present work, the developed mathematical model was validated using a tube-in-tube cylindrical reactor model, and the obtained numerical results were found to be in good agreement with experimental results reported in the literature. The deviation between the model predictions and experiment data was 1.74%. At a given initial bed temperature, an increase in H2 supply pressure leads to an increase in H2 concentration, and consequently, the H2 fraction, thereby enhancing the H2 storage capacity of the ERHCR. The elliptical cooling tube (CT) reactor requires a 16% reduction in hydrogen absorption time compared to circular CT reactor. The results of this work show that the RHC 2Li_RHC stored about 43.275 g of H2 per kg of reactive hydride (2Li_RHC) under supply conditions of 50 bar H2 pressure and a HTF temperature of 673 K.
In this article, a rigorous model has been developed and applied for the estimation of the solar energy potential and optimum tilt angle of solar flat surfaces for due south direction in the eastern Himalayan agro-climatic zones of India. The simulation model has been built in the MATLAB software environment and with the National Solar Radiation Data Base (NSRDB) based Typical Meteorological Year (TMY) data. Accordingly, monthly, seasonal, and yearly options for the assessment of the optimal tilt angles were duly considered. For the considered locations in the conducted simulation based investigations, the optimum inclination angle varied from 0° (horizontal) to 56° tilt. For all cities, the minimum optimal tilt angle was identified for May, June, and July months and the maximum was found for the December month. Also, the monthly average clearness index (KT) ranged from 0.3743 to 0.6680 and conveyed the diversified climatic condition of the studied region. Climatological seasons such as monsoon, pre-monsoon, post-monsoon and winter were considered to capture the seasonal variations. The findings inferred that the annual optimal tilt angles were close to the location latitude. Compared to the horizontal surface, the average gain in annual average total solar radiation for monthly, seasonal and yearly tilt cases was 15.93
This study investigates the structural efficacy and durability of geocoir-based natural geocells as an environmentally responsible reinforcement solution for flexible pavements. Cyclic loading tests on laboratory model pavement test sections showed that geocoir cells significantly improve structural response, reducing rutting by up to 70
Vehicle-to-grid (V2G) infrastructures play a key role in the integration of electric vehicles into smart grids; however, their reliance on bidirectional communication exposes them to a wide range of cyber threats. In this work, we present an intrusion detection framework based on a bio-inspired learning strategy derived from the grouping cockroach classifier (GCC). The proposed system analyzes V2G traffic patterns to identify abnormal behaviors associated with malicious activities. A dedicated dataset was constructed through realistic V2G simulations using the MiniV2G platform and refined using feature selection techniques to enhance classification efficiency. The effectiveness of the proposed approach was assessed under multiple attack scenarios, including Denial-of-service (DoS) and man-in-the-middle (MitM) attacks targeting electric vehicle charging stations. Experimental results show that the GCC-based detection model achieves a classification accuracy of 99.21
The energy supply is the amount of energy that is available for use. It is important to have a reliable prediction of it to make a renewable source of energy reliable, so that industries and governments can plan the allocation of their resources to keep things running very well. The proposed paper presents a novel particle swarm optimization (PSO) algorithm, PSO with power pull (PSOPP), that can train the time-series models to predict the amount of energy that can be generated by renewable energy sources (RESs). The time-series models of different configurations are trained using the PSOPP algorithm to improve their performance, then grouped to produce the final results. Through numerical trials, training the models with mean absolute error as a benchmark, we found that PSOPP performs better than traditional PSO in most of the situations. Here, we used three different configurations of an autoregressive time-series model trained for data corresponding to every RES and ensembled by averaging their results. Compared with the existing algorithms, the proposed PSOPP algorithm gives an improved solution.
