{"id":{"repo_id":"nus","oai_identifier":"oai:scholarbank.nus.edu.sg:10635/242697"},"canonical_url":"https://search.dev.ndltd.org/etd/nus/oai:scholarbank.nus.edu.sg:10635/242697","repository":{"repo_id":"nus","name":"National University of Singapore","base_url":"https://scholarbank.nus.edu.sg/oai/request"},"display":{"title":"MANAGING CRYOGENIC ENERGY FLUID STORAGE AND TRANSFER OPERATIONS","abstract":"As cleaner and renewable energy sources gain traction, the transportation and storage of energy fluids are gaining increasing importantance. To tackle the complexities in these systems, we developed an advanced mathematical model that employs a novel, adaptive, stretchable, and spatially moving grid to accurately predict the moving Vapor-liquid Interface. It surpasses existing CFD models in literature and commercial simulators, delivering rapid and comprehensive results. Our model is integrated into a dashboard tool that utilizes real operational data to predict visual profiles of variables like liquid level, temperature, pressure, and composition in cryogenic tanks. Additionally, we combined the tank model with the Unisim® process simulator to create a dynamic simulation twin for LNG bunkering systems, enabling analysis of Truck-to-Ship and Ship-to-Ship bunkering operations following the LNG bunkering protocol. By conducting this analysis, we identified shortcomings and provided recommendations to enhance safety and decrease operating costs, particularly in busy bunkering ports.","abstract_html":"As cleaner and renewable energy sources gain traction, the transportation and storage of energy fluids are gaining increasing importantance. To tackle the complexities in these systems, we developed an advanced mathematical model that employs a novel, adaptive, stretchable, and spatially moving grid to accurately predict the moving Vapor-liquid Interface. It surpasses existing CFD models in literature and commercial simulators, delivering rapid and comprehensive results. Our model is integrated into a dashboard tool that utilizes real operational data to predict visual profiles of variables like liquid level, temperature, pressure, and composition in cryogenic tanks. Additionally, we combined the tank model with the Unisim® process simulator to create a dynamic simulation twin for LNG bunkering systems, enabling analysis of Truck-to-Ship and Ship-to-Ship bunkering operations following the LNG bunkering protocol. 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