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dc.contributor.authorEl-Amin, Mohamed F.
dc.date.accessioned2024-05-21T07:24:41Z
dc.date.available2024-05-21T07:24:41Z
dc.date.issued2024-03-01
dc.identifier.doihttps://doi.org/10.1109/LT60077.2024.10469235en_US
dc.identifier.urihttp://hdl.handle.net/20.500.14131/1679
dc.description.abstractDigital twin technology is revolutionizing the modeling and simulation of complex systems, such as hydrogen leakage. This paper focuses on integrating digital twin components, starting with experimental measurements. The collected data is then used to calibrate the corresponding physics-based model, improving the accuracy of predictions. A numerical simulator is developed to enable virtual experiments and performance analysis. Comprehensive datasets are generated from real and virtual experiments, enhancing the capabilities of the twin by providing diverse training data. Machine learning prediction utilizes real and artificial datasets to gain insights into system behavior. The bidirectional relationship between the numerical simulator and machine learning prediction enhances accuracy and enriches the overall process. Viewed through the lens of science fiction, the case study on hydrogen leakage can be sculpted into a compelling narrative intertwining advanced technology, human resilience, and the ever-present challenge of harnessing and managing powerful, futuristic energy sources deep within planets.en_US
dc.publisherIEEEen_US
dc.subjectAnalytical models;Hydrogen;Machine learning;Predictive models;Extraterrestrial measurements;Data models;Digital twins;Digital twin;Modeling and simulation;Experimental measurements;Physics-based model;Numerical simulator;Machine learning;Hydrogen leakageen_US
dc.titleDigital Twin Integration for Hydrogen Leakage Modeling and Analysisen_US
dc.contributor.researcherNo Collaborationen_US
dc.contributor.labEnergy Laben_US
dc.subject.KSAENERGYen_US
dc.contributor.ugstudent0en_US
dc.contributor.alumnae0en_US
dc.source.indexScopusen_US
dc.contributor.departmentNSMTUen_US
dc.contributor.pgstudent0en_US
dc.contributor.firstauthorEl-Amin, Mohamed F.
dc.conference.locationEffat University, Jeddah, KSAen_US
dc.conference.name2024 21st Learning and Technology Conference (L&T)en_US
dc.conference.date2024-01-26


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