A scalable uncertainty quantification and data assimilation framework for tracking stochastic fluid interfaces: application to civil and environmental engineering

许多工程问题都涉及复杂的流锋演化动力学，如污染物泄漏、洪水和采油作业。因此，预测锋面演变的能力具有重大的现实意义。例如，利用测量数据（航空图像和陆基传感器数据）不断更新的计算机预测，可以预测污染物前沿或洪水在几小时、几天或几周内的传播情况，从而帮助制定疏散计划，以挽救生命和财产。本研究将开发一种计算模型，通过将观测数据注入正在运行的计算机模拟中，不断更新预测，从而更准确地预警运动流锋的运动。为实现这一目标，本研究将涵盖五个关键领域：(1)利用不确定性量化和序列数据同化技术，利用测量数据不断更新流动模拟模型，以提高预测的置信度；(2)所需的高性能仿真模型将采用域分解方法；(3)采用水平集法跟踪流体界面的动态变形；(4)将开发可扩展的集成软件套件来实现上述算法，以利用新兴的高性能计算硬件；(5)该方法可应用于污染物跟踪、洪水预报和采油。拟议的研究对加拿大的水资源决策者（例如灌溉）、生态系统和环境决策者（例如与水和空气质量有关的决策者）、省和地方应急管理（例如由于有毒化学物质释放）和石油开采工作都有好处。

Many engineering problems involve complex dynamics of evolving flow front such as contaminant spills, floodwater and oil recovery operation. Therefore the ability to predict the evolving front is of great practical concern. For example, a continuously updated computer prediction with measurement data (airborne images and land-based sensor data) on how contaminant front or floodwater will propagate in few hours, days or weeks can help plan evacuations in order to save lives and properties. In this proposal, a computational model will be developed to continuously update prediction by injecting observational data into a running computer simulation for more accurate early warning on the motion of the moving flow front. To achieve this goal, the proposed research covers five key areas: (1) uncertainty quantification and sequential data assimilation techniques will be used to continuously update flow simulation models with measurement data to improve the confidence in their predictions; (2) the requisite high performance simulation models will exploit a domain decomposition method; (3) dynamic deformation of fluid interfaces will be tracked using the level set method; (4) a scalable integrated software suite will be developed to implement the above algorithms to take advantage of emerging high performance computing hardware; (5) this approach will be applied to contaminant tracking, flood forecasting and oil recovery. The proposed research is of benefit to water resource decision-makers (e.g. irrigation), ecosystem and environmental decision-makers (e.g. related to water and air quality), provincial and local emergency management (e.g. due to toxic chemical release) and oil recovery effort in Canada.