Study on Rapid Simulation of the Pre-Cooling Process of a Large LNG Storage Tank with the Consideration of Digital Twin Requirements

Energies Pub Date : 2024-07-15 DOI:10.3390/en17143471
Yunfei Zhao, Caifu Qian, Guangzhi Shi, Mu Li, Zaoyang Qiu, Baohe Zhang, Zhiwei Wu
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Abstract

The pre-cooling of a large LNG storage tank involves complex phenomena such as heat transfer, low-temperature flow, gas displacement, and vaporization. The whole pre-cooling process could take up to 50 h. For large-scale, full-capacity storage tanks, it is particularly important to accurately control the pre-cooling temperature. Digital twin technology can characterize and predict the full life cycle parameters from the beginning of pre-cooling development to the end and even the appearance of damage in real time. The construction of a digital twin platform requires a large number of data samples in order to predict the operating state of the device. Therefore, a simulation method with high computational efficiency for the pre-cooling process of LNG tanks is of great importance. In this paper, the mixture model and discrete phase model (DPM) are applied to simulate the pre-cooling process of a large LNG full-capacity tank. Following Euler–Lagrange, the DPM greatly simplifies the solution process. Compared with the experimental results, the maximum error of the DPM simulation results is less than 11%. Such a highly efficient simulation method for the large LNG full-capacity storage tank can make it possible to build the digital twin platform that needs hundreds of data model samples.
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考虑数字孪生需求的大型液化天然气储罐预冷过程快速模拟研究
大型液化天然气储罐的预冷涉及传热、低温流动、气体置换和汽化等复杂现象。对于大型全容量储罐而言,精确控制预冷温度尤为重要。数字孪生技术可以实时表征和预测从预冷开始到预冷结束甚至出现损坏的整个生命周期参数。数字孪生平台的构建需要大量的数据样本,以便预测设备的运行状态。因此,一种计算效率高的 LNG 储罐预冷过程仿真方法就显得尤为重要。本文采用混合物模型和离散相模型(DPM)来模拟大型 LNG 全容量储罐的预冷过程。按照 Euler-Lagrange 方法,DPM 极大地简化了求解过程。与实验结果相比,DPM 仿真结果的最大误差小于 11%。如此高效的大型 LNG 全容量储罐仿真方法,使得建立需要数百个数据模型样本的数字孪生平台成为可能。
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