Self-Organizing Maps for Fast LES Combustion Modeling

Award Information
Agency:
National Aeronautics and Space Administration
Branch
n/a
Amount:
$99,997.00
Award Year:
2011
Program:
SBIR
Phase:
Phase I
Contract:
NNX11CD81P
Award Id:
n/a
Agency Tracking Number:
105326
Solicitation Year:
2010
Solicitation Topic Code:
A2.02
Solicitation Number:
n/a
Small Business Information
19039 E. Plaza Dr., Suite 290, Parker, CO, 80134-
Hubzone Owned:
N
Minority Owned:
N
Woman Owned:
N
Duns:
196231166
Principal Investigator:
Bradley Hitch
Principal Investigator
(720) 232-3597
hitch@reactionsystemsllc.com
Business Contact:
Todd Leeson
Business Official
(303) 881-7992
tleeson@reactionsystemsllc.com
Research Institute:
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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