The characteristic of turbulent fluid flow as an agent of efficient dispersion of contaminants, chemical reactants and particulate matter in general is of critical importance in many environmental and engineering applications.The objectives of this proposal are to pursue fundamental advances in the study of contaminant dispersion in turbulent flows, with emphasis on several aspects crucial in the linkage between basic understanding of turbulence physics and the development of the predictive tools of improved physical realism.The research will involve a tightly-coupled combination of large-scale numerical simulations enabled by the use of advanced cyberinfrastructure, and the development of novel extensions of Lagrangian stochastic modeling using a massive simulation database. The intellectual merits of this work include the unique challenges of tracking Lagrangian fluid element trajectories backwards in time, which will provide new insights in relating the trajectory data on multi-particle clusters to the local spatial structure of the flow, where intense local deformation can develop especially in conditions of high Reynolds number. When coupled with trajectories of diffusing molecules in Brownian motion relative to the fluid, this approach also connects particle paths directly to the statistics of passive scalar fluctuations arising from typically localized sources of pollutants in the environment.The effects of finite particle inertia and gravitational forces will likewise be addressed. New stochastic models accounting for these effects will be developed in collaboration between researchers with demonstrated high synergism and expertise in high-performance computing, turbulence theory and modeling.
As broader impacts, this project will provide much-needed improvements in the modeling of a major fluid dynamics problem in environmental science. Improved models of turbulent dispersion expected from this work will be applicable not only to atmospheric air quality but also to accidental or terrorism-driven discharge of toxic material (where higher-order moments are important), insect behavior in agriculture (where backward tracing mimics essential organism behaviors) and windborne dispersion of seeds and other aerosol-like material (where inertia is important). Besides training a PhD student with exposure to the expertise and perspective of a senior and internationally-respected scientist, this project will provide special summer internships at Georgia Tech for undergraduate students with disabilities to be selected from a national pool of applicants. The latter arrangement will be administered via a subaward to the American Association for the Advancement of Science, which has been very active in recruiting and nurturing students from this under-represented population.