Modelling of flow-induced loading and fatigue crack trajectory in pipelines

流动引起的结构效应，如应力、振动、疲劳和裂纹扩展，对加拿大能源工业中心的管道运营和维护构成了重大风险。在地下管道存在弯曲的情况下，预测这些特征对结构的影响变得更具挑战性。拟议的研发工作旨在开发一种基于机器学习的模型，以识别各种操作条件下管道中流动引起的结构特征。建立了一个跨学科和多样化的研究团队来研究这一现象的各个方面。最终目标是建立一个综合的预测模型，以表征和预测管道中流动引起的结构伪影的形成和轨迹，跨越广泛的参数空间，例如流速、流体粘度、管道内径、弯角和表面材料。该研发项目包括三个综合主题，将实验、计算和理论方法与各自的活动和时间表结合起来。在主题1中，流体力学专家评估了管道中流动诱导载荷的物理特性，并开发了振动、应力和疲劳的半经验模型，涵盖了一系列流动条件和流体特性。与此同时，主题II的结构专家研究了管道中结构裂纹、疲劳和应力的形成、扩展和轨迹。最后，在主题III中，结果被集成到一个新的基于机器学习的模型中，该模型使用从常规管道监测过程中获得的流动数据来预测管道中结构特征的形成和传播。该项目有助于提高对管道流动动力学、流动诱导载荷、疲劳裂纹扩展和基于机器学习的物理模型的基本理解，同时也为改善管道的操作和维护开发了一种新的工具。该项目通过提高管道的安全性和效率，减少对环境的影响，对加拿大的能源工业产生了巨大的影响。此外，这个项目使HQP成为加拿大未来的领导者。

Flow-induced structural effects, such as stress, vibration, fatigue, and crack-propagation, pose a major risk in operations and maintenance of pipelines at the heart of Canada's energy industry. Predicting structural impact of such features become more challenging in the presence of bends for pipes buried underground. The proposed R&D efforts aim at developing a machine-learning-based model that identifies flow-induced structural features in pipes at various operating conditions. An inter-disciplinary and diverse team of researchers are established to look at various aspects of this phenomenon. The final objective is to develop a comprehensive predictive model to characterize and forecast the formation and trajectory of flow-induced structural artifacts in pipes across a broad parameter space, e.g., flow speed, fluid viscosity, pipe inner diameter, bend angles, and surface material. This R&D project consists of three integrated themes that combine experimental, computational, and theoretical methodologies with their individual activities and timelines. In Theme I, fluid mechanics experts evaluate the physics of flow-induced loading in pipes, and develop semi-empirical models for vibration, stress, and fatigue, across a range of flow conditions and fluid properties. In parallel, structural experts in Theme II study the formation, propagation and trajectory of structural cracks, fatigue, and stresses in pipes. Finally in Theme III, the results are integrated within a novel machine-learning-based model to use flow data obtained from routine pipe monitoring processes to predict the formation and propagation of structural features in pipes. This project contributes to advancing knowledge in fundamental understanding of pipe flow dynamics, flow-induced loading, fatigue crack-propagation, and machine-learning-based physical models, while also developing a novel tool for bettering the operations and maintenance of pipelines. The impact of this project on Canada's energy industry is enormous by enhancing the safety and efficiency of pipelines and reducing their environmental footprint. Moreover, this project enables training of HQP to become future leaders in Canada.