Self-Organizing Maps for Fast LES Combustion Modeling

Award Information
Agency:
National Aeronautics and Space Administration
Amount:
$99,997.00
Program:
SBIR
Contract:
NNX11CD81P
Solitcitation Year:
2010
Solicitation Number:
N/A
Branch:
N/A
Award Year:
2011
Phase:
Phase I
Agency Tracking Number:
105326
Solicitation Topic Code:
A2.02
Small Business Information
Reaction Systems, LLC
19039 E. Plaza Dr., Suite 290, Parker, CO, 80134-
Hubzone Owned:
N
Woman Owned:
N
Socially and Economically Disadvantaged:
N
Duns:
196231166
Principal Investigator
 Bradley Hitch
 Principal Investigator
 (720) 232-3597
 hitch@reactionsystemsllc.com
Business Contact
 Todd Leeson
Title: Business Official
Phone: (303) 881-7992
Email: tleeson@reactionsystemsllc.com
Research Institution
 Stub
Abstract
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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