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

流体引起的结构效应，如应力、振动、疲劳和裂纹扩展，在加拿大能源工业中心的管道运营和维护中构成了主要风险。在埋在地下的管道存在弯头的情况下，预测这些特征的结构影响变得更加困难。拟议的研发工作旨在开发一种基于机器学习的模型，以识别不同运行条件下管道中流动诱导的结构特征。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。最终目标是开发一个全面的预测模型来表征和预测管道中流动诱导的结构伪影的形成和轨迹，这些参数空间包括流速、流体粘度、管道内径、弯曲角和表面材料。这个研发项目由三个综合主题组成，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I中，流体力学专家评估了管道中流体诱导加载的物理学，并开发了针对各种流动条件和流体性质的振动、应力和疲劳的半经验模型。同时，主题II中的结构专家研究管道中结构裂纹、疲劳和应力的形成、扩展和轨迹。最后，在主题III中，将结果集成到一个新的基于机器学习的模型中，以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。该项目有助于增进对管道流动动力学、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了解，同时也开发了一种改进管道操作和维护的新工具。该项目对加拿大能源行业的影响是巨大的，因为它提高了管道的安全性和效率，并减少了它们的环境足迹。此外，该项目还使HQP培训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.