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Spatiotemporal Nonlinear Data Analysis Tools and Reduced Order Models for Prediction of High-Pressure Reacting Flow Dynamics and Control

Award Information
Agency: Department of Defense
Branch: Air Force
Contract: FA9300-13-M-1503
Agency Tracking Number: F12B-T15-0060
Amount: $149,881.00
Phase: Phase I
Program: STTR
Solicitation Topic Code: AF12-BT15
Solicitation Number: 2012.B
Solicitation Year: 2012
Award Year: 2013
Award Start Date (Proposal Award Date): 2013-06-03
Award End Date (Contract End Date): 2014-03-03
Small Business Information
5100 Springfield Street Suite 301
Dayton, OH -
United States
DUNS: 782766831
HUBZone Owned: No
Woman Owned: No
Socially and Economically Disadvantaged: No
Principal Investigator
 Sukesh Roy
 (937) 255-3115
Business Contact
 Sivaram Gogineni
Title: President
Phone: (937) 266-9570
Research Institution
 University of Houston
 Gemunu Gunaratne
617 Science&Research Bldg. 1
Houston, TX 77204-
United States

 (713) 743-3550
 Nonprofit College or University

ABSTRACT: This Phase-I research effort is designed to forward the engineering investigation of the dynamics and control of turbulent combustion in high-pressure combustion systems by developing a set of game-changing nonlinear analysis tools that can significantly improve the post-processing speed and intelligent data mining of large numerical or experimental data sets. Moreover, a system based on nonlinear dynamical phase-space analysis (such as Chaos Theory) will be explored for reduced order modeling, devising control strategies, and providing quantitative information for system identification and comparison. The specific objectives of this effort are: (1) Remove noise from large data sets and reduce the quantity of data needed to be further analyzed by separating the noise from nonlinear dynamics, (2) Remove redundant information from large sets of data, (3) Identify dynamically significant information within a very short amount of time to enable scientists and engineers to quickly understand the underlying physics and control the spatio-temporal dynamics, (4) Develop a set of dynamical invariant tools to extract underlying physics from high-pressure combustion systems and compare experimental and numerical data in a solid quantitative manner, and (5) Develop reduced order model (ROM) and devise control strategies by exploiting the POD-based or wavelet-based dynamical system as they evolve in a reconstructed phase space. BENEFIT: This research effort on the development nonlinear spatio-temporal data analysis tools would allow investigation of turbulent reacting flow phenomena in a real-time basis. This tool set should also allow devising intelligent control strategies through reduced order modeling for the development of intelligent engines and will have a major impact on understanding of high-speed time-evolving phenomena related to ignition, flame growth, and stability in high-pressure combustors. Hence, this research effort along with methods to analyze high-speed planar and tomographic images will make the concept of real time sensing and control of combustion phenomena a reality. The proposed software system could be easily integrated with any on-board sensing and control system, which would have a significant impact on engine health and stability for the war fighter. The software system should be applicable for any high-bandwidth experimental or numerical data, and as such will have a very broad commercial application covering most of the Universities, Government laboratories, engine companies, etc. Furthermore, renewed interest in diverse and alternative fuels makes the proposed data analysis tools particularly timely for addressing a number of important research and development needs in high-performance, clean power production from sustainable sources.

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

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