Digital Twin Integration for Hydrogen Leakage Modeling and Analysis

Abstract

Digital 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.