With advances in engineering and technology, mining the deep sea for untapped rare metal resources from the bottom of the ocean has recently become economically viable. However, extracting these metal ores from the seabed creates plumes of fine particles that are deposited at various depths within the ocean, and these may be extremely harmful to the marine ecosystems and its components. Thus, for sustainable management, it is of utmost importance to carefully monitor and predict the impact of such harmful activities including plume dispersion on the marine environment. To forecast the plume dispersion in real-time, data-driven ocean modeling has to be coupled with accurate, efficient, and rigorous sediment plume transport computations. The goal of the present paper is to demonstrate the real-time applications of our coupled 3D-and-time data-driven ocean modeling and plume transport forecasting system. Here, the region of focus is the southern California bight, where the PLUMEX 2018 deep sea mining real-time sea experiment was recently conducted (23 Feb – 5 Mar, 2018). Specifically, we demonstrate the improved capabilities of the multiscale MSEAS primitive equation ocean modeling system to capture the complex oceanic phenomenon in the region of interest, the application of the novel method of composition to efficiently and accurately compute the transport of sediment plumes in 3D+1 domains, and the portability of our software and prediction system to different operational regions and its potential in estimating the environmental impacts of deep sea mining activities, ultimately aiding sustainable management and science-based regulations.

Congratulations to David Ferris on his graduation! Dave received an SM from Mechanical Engineering for his research on “Time-Optimal Multi-Waypoint Mission Planning in Dynamic Flow Fields” with our MSEAS group at MIT.

MSEAS research on the development of methodologies to predict the most informative sampling sites in the ocean for a given mission and optimal paths to reach them was highlighted in MIT News on Wednesday, 9 May, 2018. This research was funded, in part, by the Office of Naval Research, the MIT Lincoln Laboratory, the MIT Tata Center, and the National Science Foundation. The full article can be found here.

This thesis demonstrates the use of exact equations to predict time-optimal mission plans for a marine vehicle that visits a number of locations in a given dynamic ocean current field. The missions demonstrated begin and end in the same location and visit a finite number of locations or waypoints in the minimal time; this problem bears close resemblance to that of the classic “traveling salesman,” albeit with the added complexity of a continuously changing flow field. The paths, or “legs,” between all goal waypoints are generated by numerically solving exact time-optimal path planning level-set differential equations. The equations grow a reachability front from the starting location in all directions. Whenever the front reaches a waypoint, a new reachability front is immediately started from that location. This process continues until one set of reachability fronts has reached all goal waypoints and has returned to the original location. The time-optimal path for the entire mission is then obtained by trajectory backtracking, going through the optimal set of reachability fields in reverse order. Due to the spatial and temporal dynamics, a varying start time results in different paths and durations for each leg and requires all permutations of travel to be calculated. Even though the method is very efficient and the optimal path can be computed serially in real-time for common naval operations, for additional computational speed, a high-performance computing cluster was used to solve the level set calculations in parallel. This method is first applied to several hypothetical missions. The method and distributed computational solver are then validated for naval applications using an operational multi-resolution ocean modeling system of real-world current fields for the complex Philippines Archipelago region. Because the method calculates the global optimum, it serves two purposes. It can be used in its present form to plan multi-waypoint missions offline in conjunction with a predictive ocean current modeling system, or it can be used as a litmus test for approximate future solutions to the traveling salesman problem in dynamic flow fields.

Abstract: The data assimilation community has developed a variety of strategies for the blending of observations and models, taking into account their inherent uncertainties. It has offered persuasive arguments for its utility in some applications. For example, in weather forecasting and subsurface hydrology. I will focus on Bayesian methodologies, and describe a few new data assimilation strategies that take advantage of computational and physical conditions inherent in the intended application in order to provide useful alternative forecasts (estimates).

Biography: Juan M. Restrepo is Professor of Mathematics at Oregon State University. He holds courtesy appointments in the College of Engineering as well as the College of Earth, Oceans and Atmospheric Sciences. His research interests straddle computational data-driven and probabilistic methods and multi-scale dynamics. The applications emphasize ocean dynamics and transport. The recipient of a DOE Young Investigator award, and the SIAM Geoscience Career Award. He’s a fellow of SIAM.