A spatiotemporal data-driven homogenization approach for hierarchical modeling of multiphase frozen soil in permafrost

时空数据驱动的多年冻土多相冻土分层建模方法

• 批准号: RGPIN-2019-06471
• 负责人: Na, SeonHong
• 金额: $ 1.89万
• 依托单位: McMaster University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2020
• 资助国家: 加拿大
• 起止时间: 2020-01-01 至 2021-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=715664
• 关键词: spatiotemporal; data; driven; homogenization; approach

Global issues related to extreme climate change and demanding energy resources have created new engineering problems. Those issues are associated with sustainable development and resilient infrastructure in our society, such as heavy rainfall-induced slope stability, sinkhole due to groundwater level change, hydraulic fracturing for unconventional energy recovery, geothermal energy facility, nuclear waste disposal, etc. Within the Canadian context, the melting of frozen soil is a critical issue in permafrost. The main reason is global warming that accelerates the thawing and subsequently the cyclic freezing, which in turn results in damaging buildings and infrastructures. In other words, frozen soils in permafrost are subjected to coupled loading conditions in terms of thermal, hydraulic, and mechanical aspects. As a mixture of air-water-ice-solid, furthermore, the multiphase frozen soil possesses multiscale characteristics that show size-dependent responses. Therefore, the thawing and freezing soil in permafrost requires our fundamental knowledge and advanced technologies to understand, analyze, and predict its complicated behavior. In the short term, the proposed research program is designed to develop a unified computational framework that transcends the multiscale behavior of multiphase frozen soils based on a hierarchical modeling approach. Focus Area I concentrates on deriving theoretical formulations to characterize the coupled thermo-hydro-mechanical processes under freeze-thaw actions for the pore-scale level. Focus Area II involves developing a data-driven constitutive model aiming at specimen-size level by leveraging machine learning to improve multiscale computational cost and flexibility. The pore-scale simulations from Focus Area I will be used to generate a database for training the data-driven model. Focus Area III involves constructing a unified framework for field-scale simulations by developing a multiscale bridging algorithm. The domain mapping technique to accommodate the spatiotemporal information of frozen soil will be further developed to be combined into the framework. This process will integrate the achievements from Focus Areas I and II and field/experimental data a hybridized modeling method. In the long-term, the unified computational framework will be further advanced to interactively update monitoring information or additional site investigation in permafrost. This hybridized computational framework will provide an innovative methodology in analyzing conventional engineering problems, validating designs, and making predictions for resilient infrastructures, intelligent energy systems, and challenging environmental issues in Canada. The ultimate goal and strategic plan of the applicant's research will further provide cutting-edge knowledge and advanced technical skill set not only to train High Qualified Personnel (HQP), but to support industry, government, and academia in Canada and across the world.

与极端气候变化和能源需求相关的全球问题带来了新的工程问题。这些问题与我们社会的可持续发展和弹性基础设施有关，例如强降雨引起的边坡稳定性、地下水位变化引起的天坑、非常规能源回收的水力压裂、地热能源设施、核废料处理等。在这种情况下，冻土融化是永久冻土的一个关键问题。主要原因是全球变暖加速了融化和随后的循环冻结，进而导致建筑物和基础设施遭到破坏。换句话说，永久冻土中的冻土受到热、水力和机械方面的耦合载荷条件的影响。此外，作为空气-水-冰-固体的混合物，多相冻土具有多尺度特征，显示出与尺寸相关的响应。因此，多年冻土中的解冻和冻结需要我们的基础知识和先进技术来理解、分析和预测其复杂的行为。在短期内，所提出的研究计划旨在开发一个统一的计算框架，超越基于分层建模方法的多相冻土的多尺度行为。重点领域 I 专注于推导理论公式，以表征孔隙尺度水平冻融作用下的耦合热-水-机械过程。重点领域二涉及通过利用机器学习来开发针对样本大小的数据驱动本构模型，以提高多尺度计算成本和灵活性。重点领域 I 的孔隙尺度模拟将用于生成用于训练数据驱动模型的数据库。重点领域 III 涉及通过开发多尺度桥接算法来构建现场尺度模拟的统一框架。将进一步开发适应冻土时空信息的域制图技术，并将其合并到该框架中。该过程将整合重点领域 I 和 II 的成果以及现场/实验数据和混合建模方法。从长远来看，统一的计算框架将进一步发展，以交互式更新监测信息或对永久冻土进行额外的现场调查。这种混合计算框架将为分析传统工程问题、验证设计以及对加拿大的弹性基础设施、智能能源系统和具有挑战性的环境问题进行预测提供创新的方法。申请人研究的最终目标和战略计划将进一步提供尖端知识和先进的技术技能，不仅可以培训高素质人才（HQP），而且可以支持加拿大和世界各地的工业界、政府和学术界。