Advanced Liquid Surface Tracking Software for Predicting Atomization in Gas Turbine Combustors and Augmentors

Award Information
Agency:
Department of Defense
Branch
Navy
Amount:
$79,995.00
Award Year:
2007
Program:
SBIR
Phase:
Phase I
Contract:
N68335-07-C-0280
Award Id:
82595
Agency Tracking Number:
N071-046-0134
Solicitation Year:
n/a
Solicitation Topic Code:
n/a
Solicitation Number:
n/a
Small Business Information
6210 Keller's Church Road, Pipersville, PA, 18947
Hubzone Owned:
N
Minority Owned:
N
Woman Owned:
N
Duns:
929950012
Principal Investigator:
Kevin Brinckman
Research Scientist
(215) 766-1520
kbrinckman@craft-tech.com
Business Contact:
Sanford Dash
President & Chief Scientist
(215) 766-1520
dash@craft-tech.com
Research Institution:
n/a
Abstract
Recently developed gas/liquid (G/L) methodology, being used for cavitating flow problems, and extended for all-speed operability using preconditioning, will be used to analyze fuel jet atomization in high-speed military gas turbines. This methodology can capture G/L interfaces, and using multi-element unstructured numerics in conjunction with grid adaptation, will resolve G/L shear layer details needed for accurate atomization predictions. Previous primary breakup studies using traditional VOF methodology in a structured grid code, used advanced correlations to predict local droplet formation rates and sizes. Such results will be greatly improved using the new G/L methodology and refined grids along the interface. In this proposed effort, we will demonstrate the applicability of the new G/L approach for tracking the liquid fuel jet in problems typifying gas turbine fuel injection processes. We will also exhibit how grid adaptation can be used to enhance overall accuracy. Primary work will entail implementing the detailed primary breakup models that we have used earlier into the new G/L framework, and demonstrating how we can predict localized atomization rates and droplet sizes along a fuel jet interface. In Phase II, we will focus on validation using full-scale, JSF relevant, gas turbine data sets.

* information listed above is at the time of submission.

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