The ocean is one of the most unforgiving environments. Large swathes of the ocean remain unexplored due to its unpredictable weather patterns and limited communications.
Wim van Rees is the ABS Career Development Professor at MIT. He says that “the ocean is a fascinating ecosystem with several current challenges like Microplastics and coral bleaching.” “At the same time, there are many opportunities in the ocean — from aquaculture to energy harvesting to exploring the many sea creatures we don’t yet know.”
Van Rees is an ocean engineer and mechanical engineer who uses scientific computing to solve the ocean’s many problems and take advantage of its opportunities. These scientists are working to develop technologies that will allow us to understand better the oceans and the way organisms and humans can move in them.
Bio-inspired underwater devices
Fish dart through the water in a complex dance. Flexible fins move in water currents and leave a trail of eddies behind.
Fish have complex internal muscles that adjust their fins and bodies. Van Rees explains that this allows fish to propel themselves in many other ways, far beyond the capabilities of any artificial vehicle in terms of agility, maneuverability, or adaptability.
Van Rees says that we are now closer than ever to creating flexible and morphing fins for underwater robotics thanks to the advances in additive manufacturing, optimization techniques, and machine learning. This makes it more important to understand the impact of soft fins on propulsion.
Van Rees and his colleagues are using numerical simulation methods to explore the design possibilities for underwater devices with increased freedom due to deformable fins that look fish-like.
These simulations allow the team to understand better the fluid-structure interaction of soft and flexible fins in fish. They now understand how fin shape changes can affect swimming performance and harm. Van Rees adds that supercomputers can solve the problem at the interface of the flow and structure by using precise numerical techniques and parallel implementations.
Van Rees hopes to create an automated design tool that will allow for the development of a new generation of autonomous underwater devices by combining his simulation algorithms for flexible structures with optimization and machine-learning techniques. Engineers and designers can use this tool to create underwater vehicles and robotic fins that adapt their shapes to meet their immediate operational goals.
Van Rees explains, “We can use optimization and AI to do an inverse design within the whole parameter space, create smart, adaptable device from scratch, and use precise individual simulations to identify physical principles that determine why one form performs better.”
Robotic vehicles using swarming algorithms
Michael Benjamin, Principal Research Scientist, wants to improve how vehicles navigate through water. Benjamin, then a postdoctoral researcher at MIT, launched an open-source project to develop an autonomous helm technology. This software has been used by companies such as Sea Machines and BAE/Riptide, as well as Thales UK and Rolls Royce. It also uses a unique method of multi-objective optimization. Benjamin’s Ph.D. research led to the development of this optimization method. It allows a vehicle to choose its heading, speed, and direction to meet multiple objectives simultaneously.
Benjamin is now developing obstacle avoidance and swarming algorithms. These algorithms could allow dozens of uncrewed vehicles to communicate with each other and explore a particular part of the ocean.
Benjamin is currently focusing on how to disperse autonomous vehicles across the ocean.
Let’s say you want to launch 50 vehicles into a portion of the Sea of Japan. Benjamin explains, “Is it more efficient to drop all 50 vehicles in one place or have a mothership drop them off at specific points within a particular area?”
His team has developed algorithms to answer this question. Each vehicle communicates its location periodically to other cars using swarming technology. Benjamin’s software allows these vehicles to spread in the best possible distribution for the area they are operating in.
The ability to avoid collisions is key to the success and safety of swarming cars. Collision avoidance can be complicated by international maritime rules called COLREGS (or “Collision Regulations”), which determine which vehicles have the “right-of-way” to cross paths. This presents a unique challenge to Benjamin’s swarming algorithms.
The COLREGS were written to avoid one contact. However, Benjamin’s swarming algorithm must account for multiple unpiloted vehicles trying not to collide with each other.
Benjamin and his team devised a multi-object optimization algorithm to rank specific maneuvers from zero to 100. One would indicate a collision, while 100 would suggest that the vehicles have avoided collision completely.
Benjamin says, “our software is the only marine program where multi-objective optimization provides the core mathematical basis for decision-making.”
Researchers like Benjamin and van Rees employ machine learning and multi-objective optimization to solve the problem of vehicles traversing ocean environments. Pierre Lermusiaux at MIT, the Nam Pyo Uh Professor, uses machine learning to understand the ocean environment.
Improved ocean modeling and prediction
Oceans are the most well-known example of a complex dynamical system. The ocean is a dynamic environment with fluid dynamics, weather patterns, and climate change. It can be unpredictable at all times. Forecasting can be difficult due to the constantly changing ocean environment.
Although researchers have used dynamical system models to predict ocean environments, Lermusiaux points out that these models could be better.
When developing models, you can only account for some molecules in the ocean. Model resolution and accuracy are minimal, as well as ocean measurements. A model could have a data point for every 100m, every kilometer, or if you’re looking at climate models of global oceans, one data point per 10 km. This can impact the accuracy of your predictions,” says Lermusiaux.
Abhinav Gupta, a graduate student, and Lermusiaux, a machine-learning framework, have been developed to compensate for these models’ low resolution or accuracy. The algorithm can take a simple low-resolution model and fill in the gaps to create a complex model with high resolution.
Gupta & Lermusiaux have created a framework that learns from existing approximate models and introduces time delays to increase their predictive abilities.
Gupta says that things in nature don’t happen in a single moment. However, most prevalent models assume they do. “To make an approximate model more precise, the machine learning and data you input into the equation must represent past states’ effects on future predictions.”
The “neural closing model” of the team, which accounts for these delays, could lead to better predictions, such as a Loop Current current hitting an oil rig in the Gulf of Mexico or the amount of phytoplankton present in a particular part of the ocean.
Researchers can unlock more ocean mysteries and solve many ocean problems as computing technologies like Lermusiaux and Gupta improve.