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Probabilistic Regional Ocean Predictions: Stochastic Fields and Optimal Planning

Speaker: Deepak Narayanan Subramani
[Announcement (PDF)]
Speaker Affiliation: Ph.D. Candidate, Department of Mechanical Engineering, MIT
Date: Thursday, November 9, 2017 at 2:30PM in 3-333


The ocean is a prime example of multiscale nonlinear fluid dynamical system. Ocean fields are usually complex, with intermittent features and nonstationary heterogeneous statistics. Due to the limited measurements, there are multiple sources of uncertainties, including the initial conditions, boundary conditions, forcing, parameters, and even the model parameterizations and equations themselves. To reduce uncertainties and allow long-duration measurements, the energy consumption of ocean observing platforms need to be optimized. Predicting the distributions of reachable regions, time-optimal paths, and risk-optimal paths in uncertain, strong and dynamic flows is also essential for their optimal and safe operations. Motivated by the above needs, the objectives of this thesis are to develop and apply the theory, schemes, and computational systems for: (i) Dynamically Orthogonal ocean primitive-equations with a nonlinear free-surface, in order to quantify uncertainties and predict probabilities for four-dimensional (time and 3-d in space) coastal ocean states, respecting their nonlinear governing equations and non-Gaussian statistics; (ii) Stochastic Dynamically Orthogonal level-set optimization for energy-optimal path planning of autonomous agents in coastal regions, rigorously incorporating realistic ocean predictions; (iii) Probabilistic predictions of reachability, time-optimal paths and risk-optimal paths in uncertain, strong and dynamic flows.

The theoretical and computational foundation is laid out and several idealized-to-realistic applications are demonstrated. Examples in the Middle Atlantic Bight region, Northwest Atlantic, and northern Indian ocean are showcased. The probabilistic prediction and path planning methodologies developed here are PDE-based and provide stochastic ocean fields, and energy-optimal, stochastic time-optimal and risk-optimal predictions without heuristics. Computationally, the new methods are several orders of magnitude faster than direct Monte Carlo methods.

Such technologies can be utilized for several commercial and societal applications, now and in the future. Specifically, the probabilistic ocean predictions can be input to a technical decision aide for a sustainable fisheries co-management program in India, which has the potential to provide environment friendly livelihoods to millions of marginal fishermen. The risk-optimal path planning equations can be employed in real-time for efficient ship routing to reduce greenhouse gas emissions and save operational costs.