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Self-Organizing Maps for Fast LES Combustion Modeling

Award Information
Agency: National Aeronautics and Space Administration
Branch: N/A
Contract: NNX11CD81P
Agency Tracking Number: 105326
Amount: $99,997.00
Phase: Phase I
Program: SBIR
Solicitation Topic Code: A2.02
Solicitation Number: N/A
Solicitation Year: 2010
Award Year: 2011
Award Start Date (Proposal Award Date): 2011-02-18
Award End Date (Contract End Date): 2011-09-29
Small Business Information
19039 E. Plaza Dr., Suite 290
Parker, CO 80134-
United States
DUNS: 196231166
HUBZone Owned: No
Woman Owned: No
Socially and Economically Disadvantaged: No
Principal Investigator
 Bradley Hitch
 Principal Investigator
 (720) 232-3597
Business Contact
 Todd Leeson
Title: Business Official
Phone: (303) 881-7992
Research Institution

Tremendous advances have been made in the development of large and accurate detailed reaction chemistry models for hydrocarbon fuels. Comparable progress has also been achieved in CFD as an engineering design tool. Highly accurate hydrocarbon chemistry is now desired for simulating gas turbine combustors and automobile engines to better predict both performance and pollutant emissions. Newer and more accurate CFD techniques like Large Eddy Simulation (LES) are being used more as computational power increases along with the demand for better flow predictions. Unfortunately, using large, detailed chemical mechanisms to simulate real turbulent combustion devices is problematic due to the sheer computational burden of the added chemistry. As a result, chemistry mechanisms employing a large number of chemical species are currently only feasible to run in the simplest of flow geometries, and only the simplest and least accurate chemistry models are currently tractable to run in LES CFD codes. We propose using a unique neural network approach to create a fast and accurate species source term function that could alleviate both of these problems.

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

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