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1. Development and outlook of engineering technology for underground water-sealed storage caverns

Author

XIA Xilin, ZHANG Dongyan, XIA Binghuan, HAN Liliang, and ZHAO Yi

Subjects
underground water-sealed cavern, oil storage, pressurized cavern, atmospheric cavern, liquefied hydrocarbon, crude oil, engineering technology, Oils, fats, and waxes, TP670-699, Gas industry, TP751-762
Abstract

Underground water-sealed caverns are currently being constructed to facilitate the storage of crude oil, refined oil products,and liquefied petroleum gas. These caverns are witnessing a growing trend towards large-scale storage and a diverse range of storage mediums. As advancements in design and construction technologies for underground water-sealed caverns continue, the associated industrial system also improves. This paper presents a comprehensive overview of the origin and development of Chinese and International underground water-sealed cavern technology. It explores the core technologies through the whole process of underground water-sealing storage cavern construction, including designing, surveying, and constructing, with a particular focus on the current construction technologies for pressurized caverns and atmospheric caverns. The study identifies various challenges associated with the development of this field, including the need to improve current industry standards and the technologies in engineering design and exploration, engineering equipment and construction. Furthermore, the paper assesses the future prospects of refining existing technologies, introducing improved iterations, and leveraging new business applications based on the unique characteristics of these caverns. Finally, key technologies are proposed to facilitate the overall construction process of the underground water-sealed caverns.These research findings are valuable in improving current technologies and serve as a reference for the development of storage methods for petroleum products, cryogenic materials, and new types of gas energy for which contact with water is not desirable.

Published
2023
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2. A deep convolutional neural network model for rapid prediction of fluvial flood inundation

Author

Kabir, Syed, Patidar, Sandhya, Xia, Xilin, Liang, Qiuhua, Neal, Jeffrey, and Pender, Gareth

Subjects
Computer Science - Machine Learning, Electrical Engineering and Systems Science - Signal Processing, Statistics - Machine Learning
Abstract

Most of the two-dimensional (2D) hydraulic/hydrodynamic models are still computationally too demanding for real-time applications. In this paper, an innovative modelling approach based on a deep convolutional neural network (CNN) method is presented for rapid prediction of fluvial flood inundation. The CNN model is trained using outputs from a 2D hydraulic model (i.e. LISFLOOD-FP) to predict water depths. The pre-trained model is then applied to simulate the January 2005 and December 2015 floods in Carlisle, UK. The CNN predictions are compared favourably with the outputs produced by LISFLOOD-FP. The performance of the CNN model is further confirmed by benchmarking against a support vector regression (SVR) method. The results show that the CNN model outperforms SVR by a large margin. The CNN model is highly accurate in capturing flooded cells as indicated by several quantitative assessment matrices. The estimated error for reproducing maximum flood depth is 0 ~ 0.2 meters for the 2005 event and 0 ~ 0.5 meters for the 2015 event at over 99% of the cells covering the computational domain. The proposed CNN method offers great potential for real-time flood modelling/forecasting considering its simplicity, superior performance and computational efficiency., Comment: 45 pages, 14 figures, 7 tables

Published
2020
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7. A Multi-Scale Mapping Approach Based on a Deep Learning CNN Model for Reconstructing High-Resolution Urban DEMs

Author

Jiang, Ling, Hu, Yang, Xia, Xilin, Liang, Qiuhua, and Soltoggio, Andrea

Subjects
Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning, Electrical Engineering and Systems Science - Image and Video Processing, Statistics - Machine Learning
Abstract

The shortage of high-resolution urban digital elevation model (DEM) datasets has been a challenge for modelling urban flood and managing its risk. A solution is to develop effective approaches to reconstruct high-resolution DEMs from their low-resolution equivalents that are more widely available. However, the current high-resolution DEM reconstruction approaches mainly focus on natural topography. Few attempts have been made for urban topography which is typically an integration of complex man-made and natural features. This study proposes a novel multi-scale mapping approach based on convolutional neural network (CNN) to deal with the complex characteristics of urban topography and reconstruct high-resolution urban DEMs. The proposed multi-scale CNN model is firstly trained using urban DEMs that contain topographic features at different resolutions, and then used to reconstruct the urban DEM at a specified (high) resolution from a low-resolution equivalent. A two-level accuracy assessment approach is also designed to evaluate the performance of the proposed urban DEM reconstruction method, in terms of numerical accuracy and morphological accuracy. The proposed DEM reconstruction approach is applied to a 121 km2 urbanized area in London, UK. Compared with other commonly used methods, the current CNN based approach produces superior results, providing a cost-effective innovative method to acquire high-resolution DEMs in other data-scarce environments.

Published
2019

15. High-performance simulation technologies for water-related natural hazards

Author

Xia, Xilin

Subjects
363.34
Abstract

Water-related natural hazards, such as flash floods, landslides and debris flows, usually happen in chains. In order to better understand the underlying physical processes and more reliably quantify the associated risk, it is essential to develop a physically-based multi-hazard modelling system to simulate these hazards at a catchment scale. An effective multi-hazard modelling system may be developed by solving a set of depth-averaged dynamic equations incorporating adaptive basal resistance terms. High-performance computing achieved through implementation on modern graphic processing units (GPUs) can be used to accelerate the model to support efficient large-scale simulations. This thesis presents the key simulation technologies for developing such a novel high-performance water-related natural hazards modelling system. A new well-balanced smoothed particle hydrodynamic (SPH) model is first presented for solving the shallow water equations (SWEs) in the context of flood inundation modelling. The performance of the SPH model is compared with an alternative flood inundation model based on a finite volume (FV) method in order to select a better numerical method for the current study. The FV model performs favourably for practical applications and therefore is adopted to develop the proposed multi-hazard model. In order to more accurately describe the rainfallrunoff and overland flow process that often initiates a hazard chain, a first-order FV Godunovtype model is developed to solve the SWEs, implemented with novel source term discretisation schemes. The new model overcomes the limitations of the current prevailing numerical schemes such as inaccurate calculations of bed slope or friction source terms and provides much improved numerical accuracy, efficiency and stability for simulating overland flows and surface flooding. To support large-scale simulation of flow-like landslides or debris flows, a new formulation of depth-averaged governing equations is derived on the Cartesian coordinate system. The new governing equations take into account the effects of non-hydrostatic pressure and centrifugal force, which may become significant over terrains with steep and curved topography. These equations are compatible with various basal resistance terms, effectively leading to a unified mathematical framework for describing different type of water-related natural hazards including surface flooding, flow-like landslides and debris flows. The new depthaveraged governing equations are then solved using an FV Godunov-type framework based on the second-order accurate scheme. A flexible and GPU-based software framework is further designed to provide much improved computational efficiency for large-scale simulations and ease the future implementation of new functionalities. This provides an effective codebase for the proposed multi-hazard modelling system and its potential is confirmed by successfully applying to simulate flow-like landslides and dam break floods.

Published
2017

39. Recommendations for Improving Integration in National End-to-End Flood Forecasting Systems: An Overview of the FFIR (Flooding From Intense Rainfall) Programme

Author

Flack, David, primary, Skinner, Christopher, additional, Hawkness-Smith, Lee, additional, O’Donnell, Greg, additional, Thompson, Robert, additional, Waller, Joanne, additional, Chen, Albert, additional, Moloney, Jessica, additional, Largeron, Chloé, additional, Xia, Xilin, additional, Blenkinsop, Stephen, additional, Champion, Adrian, additional, Perks, Matthew, additional, Quinn, Niall, additional, and Speight, Linda, additional

Published
2019
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