Onboard forecasting is challenging but essential for unmanned autonomous ocean platforms. Due to the numerous operational constraints of these platforms, efficient adaptive Reduced-Order Models (ROMs) are needed. In this work, we employ the incremental Low-Rank Dynamic Mode Decomposition (iLRDMD), which is an adaptive, data-driven, DMD-based ROM that enables efficient forecast compression, transmission, and onboard forecasting. We demonstrate the algorithm on 3D multivariate Hybrid Coordinate Ocean Model (HYCOM) ocean fields in the Middle Atlantic Ridge (MAR) region. We further demonstrate that these iLRDMD ocean forecasts can be used for interdisciplinary applications such as underwater acoustics predictions. Here, acoustics fields computed from the ocean iLRDMD forecasts are compared to those computed from HYCOM fields. We also illustrate the application of a joint ocean-acoustics iLRDMD model for predetermined acoustics configurations. In the MAR region, we find that iLRDMD models are sufficiently accurate and efficient for onboard ocean and acoustic forecasting of temperature, salinity, velocity, and transmission loss fields.

Real-time sea experiments often involve large computational costs and software development associated with running numerical ocean simulations. Effective visualization tools that interpret the results of these simulations are therefore a necessity, and must overcome the challenges of plotting large, high-resolution, three-dimensional, time-dependent, and probabilistic ocean fields and associated quantities in real-time. Although disparate visualization tools aimed at ocean forecasting exist, a complete, integrated visualization suite that is efficient, interactive, and has 3D capabilities is still needed. In this work, we present the MSEAS high-performance visualization suite for real-time sea experiments. It processes multidisciplinary oceanographic fields in a computationally efficient manner and creates easy-to-use, portable, and interactive visualizations. The suite includes static visualization tools based on NCAR Graphics and MATLAB; the interactive web-based tool 2DSeaVizKit built using leaflet and D3.js for interactive 2D visualization on the world map; and 3DSeaVizKit, a browser-based, interactive 3D visualization tool built using Plotly and WebGL for exploratory 3D analysis of ocean forecasts. It can provide standard 2D cross-sections for scalar-valued data; quiver plots, pathlines, and streamtubes for vector-valued data; Lagrangian products (such as trajectories, Lagrangian Coherent Structures, etc.); isosurfaces for 3D data; and an interactive graphical user interface for selecting the quantities, times, and sub-domains of interest. We showcase applications of the visualization suite during three recent exercises that took place in the Gulf of Mexico, the Clarion-Clipperton Fracture Zone in the Pacific Ocean, and the Balearic sea.

Congratulations to Abhinav Gupta on his graduation! Abhinav successfully defended and received his PhD from Mechanical Engineering for his research on “Scientific Machine Learning for Dynamical Systems: Theory and Applications to Fluid Flow and Ocean Ecosystem Modeling” with our MSEAS group at MIT. We wish all the best to Abhinav for his next steps!

For intelligent ocean exploration and sustainable ocean utilization, the need for smart autonomous underwater vehicles (AUVs), surface craft, and small aircraft is rapidly increasing. The challenge of creating time-optimal navigation routes for these vehicles has many applications, including ocean data collection, transportation and distribution of goods, naval operations, search and rescue, detecting marine pollution, ocean cleanup, conservation, and solar-wind-wave energy harvesting, among others. In this thesis, we employ the Massachusetts Institute of Technology – Multidisciplinary Simulation, Estimation, and Assimilation Systems (MIT-MSEAS) time-optimal path planning theory and schemes based on exact Hamilton–Jacobi partial differential equation (PDE) and Level Set methods to predict and study the sensitivity of reachable sets and time-optimal trajectories in the Portugal–Azores–Madeira region of the Northern Atlantic, for several types of missions and autonomous ocean vehicles. Specifically, using the MIT-MSEAS multi-resolution ocean modeling and data assimilation system to provide four-dimensional ocean currents in the region, we compute time-reachable sets and time-optimal paths for several missions, and examine the sensitivity to variations in vehicle type, speed, start time, voyage direction, and operating depths. Our real-data-driven multi-resolution simulation study illustrates how navigational paths vary with these parameters, and how ocean dynamics and variability in the Portuguese ocean regions affect the time optimization, as compared to direct voyages in the absence of any ocean currents. We also highlight effects of the Azores and Madeira archipelagos, differences between surface and bottom path planning, interception routes between vehicles of different speeds, and the utilization of arrival time fields in planning. Results showcase how principled path planning, integrating data-driven multi-resolution ocean modeling with exact reachability theory and numerical schemes, can assess the capabilities of ocean vehicles in the Portugal–Azores–Madeira ocean region, by predicting the fastest travel time, expected range, and optimal headings, for varied types of ocean missions.

MSEAS PhD candidates Aaron Charous and Wael Hajj Ali have won first place in the 2022 de Florez Award Competition. Their presentation, entitled “High-Resolution Seafloor Images Using a Novel Wide-Area Ocean Floor Mapping System”, placed first in the Graduate Science category. The competition website is here. Congratulations to Aaron and Wael!