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Gabriel David Weymouth
Post Doctoral Research Associate
Vortical Flow Research Laboratory
Department of Mechanical Engineering
Massachusetts Institute of Technology
Mail: 77 Massachusetts Avenue, Rm 5-331, Cambridge, MA 02139
Email: weymouth@mit.edu
Phone: (617)-501-8211
Curriculum Vitae (PDF)
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Interactive Research Examples
Processing implementation of the Boundary Data Immersion Method (BDIM) with dye injection for visualization. Left click and drag the red ball through the flow. Right click to reset the simulation and toggle a uniform free stream. Double click anywhere to add or remove a ball. BDIM captures complex solid/fluid interactions such as the alternating vortex wake pattern without boundary fitted grids.
In order to acheive real-time calculation speed, the simulation is first-order and single precision. As such this tool is strictly for illustrative and educational purposes.
Processing implementation of the conservative Volume Of Fluid (cVOF) method for two fluids with a density ratio of 0.9 such as oil and water. Move the mouse to rotate the tank. The conservative method ensures the tank remains half full (or half empty) even whem modeling breaking waves, spray, and bubbles.
In order to acheive real-time calculation speed, the simulation is first-order and single-precision. As such this tool is strictly for illustrative and educational purposes.
Education
Sc.D., Ocean Engineering, Massachusetts Institute of Technology, 2008
M.S., Mechanical Engineering, University of Iowa Institute of Hydraulic Research, 2003
B.S., Naval Architecture and Marine Engineering, Webb Institute, 2001
Research Interests
Free-interface fluid dynamics: high energy two-phase flows and turbulence, water entry and cavity formation, air entrainment and bubble dynamics
Hydrodynamic sensing: environmental mapping from pressure information, flow optimization and control, biological fluid dynamics
Computational fluid dynamics: Cartesian-grid methods, conservative Volume of Fluid (cVOF) method, Boundary Data Immersion Method (BDIM), Multi-Grid solvers for Poisson equations
Computational Learning and Optimization: Physics Based Learning Models (PBLM), Unscented Kalman filters, Interior Point methods, Evolutionary algorithms, Support Vector Machines, Connected Component Analysis
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