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Grid-Spacing-Independent and Discretization-Order-Independent Simulation for Naval Single-Phase and Two-Phase Flow Applications

Award Information
Agency: Department of Defense
Branch: Navy
Contract: N68335-15-C-0247
Agency Tracking Number: N15A-002-0190
Amount: $149,752.00
Phase: Phase I
Program: STTR
Solicitation Topic Code: N15A-T002
Solicitation Number: 2015.0
Solicitation Year: 2015
Award Year: 2015
Award Start Date (Proposal Award Date): 2015-06-09
Award End Date (Contract End Date): 2016-10-30
Small Business Information
1101 McMurtrie Drive NW
Huntsville, AL 35806
United States
DUNS: 622989239
HUBZone Owned: No
Woman Owned: Yes
Socially and Economically Disadvantaged: No
Principal Investigator
 Bono Wasistho
 (256) 489-2346
Business Contact
 Jay Sullivan
Title: Technical Point of Contact
Phone: (256) 489-2346
Research Institution
 California Institute of Technology
 Dr. Josette Bellan
1200 East California Boulevard
Pasadena, CA 91125
United States

 (818) 354-6959
 Nonprofit College or University

Turbulent shear flows in naval applications are characterized by vastly different lengths and time scales associated with rotor tip vortices and the vortical structures shed from the ship, and additional phase from water drops and water vapor. To tackle the modeling challenges, we propose a novel methodology that combines a vorticity preserving method and a new approach to LES turbulence modeling that is grid-spacing-independent and discretization-order-independent, called Explicitly Filtered Large Eddy Simulation (EFLES). The EFLES concept has been shown to work for both single-phase and two-phase flows, which is important for the Navy because of the spray from the surrounding sea water. For single-phase, it was found that the subgrid scale (SGS) model did not make any difference in accuracy, provided that an SGS model is used. For two-phase flow, however, the type of SGS model may matter. The advantage of EFLES is that one could use very large filter-widths since the LES grid is not related to the filter width. Hence, one could capture predictability and accuracy independent of the discretization order and number of LES grid points, which is an enabling force due to the significant reduction in computational cost.

* Information listed above is at the time of submission. *

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