Digital twin modeling and leak diagnosis of temperature and stress fields in LNG storage tanks.

• Established a twin model for temperature and stress monitoring in LNG storage tanks. • Integrate artificial intelligence and digital twin technology to achieve LNG tank leak diagnosis. • Integrating Seq2seq, CNN, and LSTM to achieve spatiotemporal prediction of leakage areas. Temperature and stress serve as crucial indicators for monitoring the health of LNG storage tanks. To address the limitations of traditional point-based monitoring and transition to comprehensive digital monitoring, this paper introduces the application of digital twin (DT) technology. The DT model plays a pivotal role in accurately representing the state of the physical entity and serves as the foundation for monitoring services and meeting application requirements within DT systems. This article introduces a numerical simulation method guided by temperature sensor data. It utilizes a temperature and stress calculation program as a platform to create twin models for real-time assessment of the temperature and stress within the storage tank. By comparing with the Fluent simulation of physical field leakage, the DT model realizes real-time inversion of the temperature field and stress field from sensor data to the whole region. To enhance the diagnostic and predictive capabilities of the DT system, we flatten the computed temperature nephogram from the DT model and employ machine vision techniques to extract cloud map features. Machine learning methods such as support vector machine (SVM) and linear regression are utilized to achieve leakage diagnosis, leakage volume calculation, and leakage location calculation. Additionally, we introduce the sequence-to-sequence (Seq2seq) framework and combine it with convolutional neural networks (CNN) and long short-term memory (LSTM) to train the time series of nephogram, enabling spatiotemporal prediction of leakage areas. [ABSTRACT FROM AUTHOR]