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.

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.

Following the scientific, technical and field trial initiatives ongoing since the Maritime Rapid Environmental Assessment (MREA) conferences in 2003, 2004 and 2007, the MREA10 conference provided a timely opportunity to review the progress on various aspects of MREA, with a particular emphasis on marine environmental uncertainty management. A key objective of the conference was to review the present state-of-the art in Quantifying, Predicting and Exploiting (QPE) marine environmental uncertainties. The integration of emerging environmental monitoring and modeling techniques into data assimilation streams and their subsequent exploitation at an operational level involves a complex chain of non-linear uncertainty transfers, including human factors. Accordingly the themes for the MREA10 conference were selected to develop a better understanding of uncertainty, from its inception in the properties being measured and instrumentation employed, to its eventual impact in the applications that rely upon environmental information.

Contributions from the scientific community were encouraged on all aspects of environmental uncertainties: their quantification, prediction, understanding and exploitation. Contributions from operational communities, the consumers of environmental information who have to cope with uncertainty, were also encouraged. All temporal and spatial scales were relevant: tactical, operational, and strategic, including uncertainty studies for topics with long-term implications. Manuscripts reporting new technical and theoretical developments in MREA, but acknowledging effects of uncertainties to be accounted for in future research, were also included.

The response was excellent with 87 oral presentations (11 of which were invited keynote speakers) and 24 poster presentations during the conference. A subset of these presentations was submitted to this topical issue and 22 manuscripts have been published by Ocean Dynamics.