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Shavinesh Sukesh

Alex K. Vaskov

MSEAS group awarded seed funding for high performance computing

The Massachusetts Green High Performance Computing Center (MGHPCC) has awarded $600,000 in seed grants to seven multi-university teams on issues ranging from the ecosystem off the New England coast to medical imaging to the speed of computing itself. The MGHPCC is designed to promote research collaboration among the participating universities (Boston University, Harvard University, the Massachusetts Institute of Technology, Northeastern University and the University of Massachusetts) through high-performance computing, a pillar of all scientific inquiry today. The seed grant program is intended to accelerate the MGHPCC’s mission of computational collaboration.

From the MGHPCC web site: “To complement the forthcoming deployment of a state-of-the-art underwater observation platform, part of the NSF-sponsored Ocean Observatories initiative, John Marshall (MIT), Pierre Lermusiaux (MIT), Amala Mahadevan (WHOI) and Amit Tandon (UMass Dartmouth) will create models to provide insights into the turbulent mixing that regulates nutrient cycle and ocean ecosystem dynamics off the New England coast.”

The full announcement can be found here.

Amy Guyomard

Path Planning in Time Dependent Flow Fields using Level Set Methods

Lolla, T.; Ueckermann, M.P.; Yigit, K.; Haley, P.J.; Lermusiaux, P.F.J., 2012, Path planning in time dependent flow fields using level set methods, 2012 IEEE International Conference on Robotics and Automation (ICRA), 166-173, 14-18 May 2012, doi: 10.1109/ICRA.2012.6225364.

We develop and illustrate an efficient but rigorous methodology that predicts the time-optimal paths of ocean vehicles in dynamic continuous flows. The goal is to best utilize or avoid currents, without limitation on these currents nor on the number of vehicles. The methodology employs a new modified level set equation to evolve a wavefront from the starting point of vehicles until they reach their desired goal locations, combining flow advection with nominal vehicle motions. The optimal paths of vehicles are then computed by solving particle tracking equations backwards in time. The computational cost is linear with the number of vehicles and geometric with spatial dimensions. The methodology is applicable to any continuous flows and many vehicles scenarios. Present illustrations consist of the crossing of a canonical uniform jet and its validation with an optimization problem, as well as more complex time varying 2D flow fields, including jets, eddies and forbidden regions.