Fusion of a Real-time Analytical Model with Facility Control Systems

Award Information
Agency:
Department of Defense
Branch
Air Force
Amount:
$99,953.00
Award Year:
2010
Program:
STTR
Phase:
Phase I
Contract:
FA9550-10-C-0137
Award Id:
94976
Agency Tracking Number:
F09B-T16-0005
Solicitation Year:
n/a
Solicitation Topic Code:
AF 09TT16
Solicitation Number:
n/a
Small Business Information
3100 Fresh Way SW, Huntsville, AL, 35805
Hubzone Owned:
N
Minority Owned:
N
Woman Owned:
N
Duns:
124289294
Principal Investigator:
Alton Reich
Lead Engineer
(256) 713-1220
Alton.Reich@StreamlineAutomation.biz
Business Contact:
Alton Reich
Lead Engineer
(256) 713-1220
Alton.Reich@StreamlineAutomation.biz
Research Institution:
Southern Illinois University Edward
Stephen Hansen
Rendleman Hall
Box 1046
Edwardsville, IL, 62026
(618) 650-3010
Nonprofit college or university
Abstract
AEDC personnel have developed and demonstrated the effectiveness of coupling a control volume model with a wind tunnel control system. The performance of the model was hampered because parameters of the model were assumed to be constant, when they are likely variables. A method for using facility data to determine functional relationships defining these parameters would allow them to vary during hardware in the loop testing and likely improve the results produced by the model. A framework is needed that is flexible enough to be utilized on different wind tunnels that will perform data fusion to tune system model parameters. Streamline Automation and Southern Illinois University Edwardsville propose to utilize our Intelligent Data Extraction Algorithm to process and fuse data captured from multiple sensors during operations of a wind tunnel facility. The algorithm will then be used to tune the model parameters for use in hardware in the loop testing. During the Phase 1 project, the team will develop and test the algorithm for use on the SIUE wind tunnel. During Phase 2, the team will apply the methodology to an Air Force wind tunnel facility. BENEFIT: There are several high-performance wind tunnel facilities at AEDC that can benefit directly from the technology proposed herein. It will enable data that is difficult to measure directly to be extracted to from the measured data. This data can be used to determine relationships between cause and effect that will improve the fidelity of facility models and improve hardware in the loop testing. The technology can be utilized directly at other wind tunnel facilities, including those at NASA and at universities.

* information listed above is at the time of submission.

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