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

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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.