Abstract:

Three-dimensional modeling of the transport and fate of oil, gas and hydrates is essential to capture the correct behavior of oil spills and their impact on natural and human resources. Current forecast models generally embody depth-averaged or two-dimensional coastal models that do not capture the three-dimensional spatio-temporal dispersion of the multi-component spill and additives. Given the concern about the dispersants reducing the upward migration of the oil, modeling the transport and fate of oil and gas and its dispersant additives in the underwater regions is very important. Our efforts include, in the long term, comprehensive long-term physics-based oil spill model that is three-dimensional, contains the interacting flow components of oil, gas (methane), water and dispersant, and the spatio-temporal evolution and fate of the multi-component, multi-phase mixture including their interactions with the microbial environment. These computationally intensive and complex simulations are accompanied by elaborate three-dimensional visualizations to understand the data, both for scientific usage as an analysis tool and for education and public outreach in classrooms and the media. The initial one year inter-disciplinary project has several integrated elements- simulation, field measurements and visualization. A suite of simulation tools are being developed and applied ranging from a detailed physics based three dimensional unsteady simulation of the two-phase, multi-component oil spill in the turbulent oceanic environment (Acharya, mechanical engineering) to a simplified Lagrangian jet model formulation that can provide rapid predictions of the oil spill for decision making (Huang, oceanography and costal sciences). Field data using a suite of distributed sensors are being collected (Huang; Li), and will be used for validating the simulation tools. Finally, to make sense of the large data sets being generated via simulation or measurements, visualization tools are being developed that have the sophistication and capability to probe the data set, extract and visualize key features for understanding the behavior (Benger, Center for Computation & Technology; Brenner, computer science).