Concurrent Agent-enabled Feature Extraction (CAF?)

Award Information
Agency:
Department of Defense
Branch
Air Force
Amount:
$749,999.00
Award Year:
2010
Program:
STTR
Phase:
Phase II
Contract:
FA9550-10-C-0035
Agency Tracking Number:
F08A-017-0010
Solicitation Year:
n/a
Solicitation Topic Code:
AF 08T017
Solicitation Number:
n/a
Small Business Information
21st Century Systems, Incorporated
6825 Pine Street, Suite 141, Omaha, NE, 68106
Hubzone Owned:
N
Minority Owned:
N
Woman Owned:
N
Duns:
949183701
Principal Investigator:
Robert Woodley
Senior Scientist
(573) 329-8526
robert.woodley@21csi.com
Business Contact:
Stuart Aldridge
Sr. VP, Research & Development
(402) 505-7887
stuart@21csi.com
Research Institution:
Brigham Young University
Brent W Webb
A-376 ASB Campus Drive
Provo, UT, 84602
(801) 422-5995
Nonprofit college or university
Abstract
High fidelity simulations of complex systems still pose a challenge to the scientist trying to understand its physical characteristics. The challenge is in finding useful bits in terabytes of data that directly relate to the nature of time-varying, multivariate data. An intelligent data mining capability is needed that has both knowledge (descriptive physics) and foresight (cognitive model of users' needs). Concurrent Agent-enabled Feature Extraction (CAF?), from 21st Century Systems, Inc. and Brigham Young University, will address this challenge. CAF? features 21CSI's intelligent agent technologies that leverage BYU's expertise. CAF?'s innovative intelligent agent structure and evidential inference engine will allow concurrent data-mining, making it possible for multiple analysis methods to work together to improve the data-mining performance. This phase implements a bottom-up clustering algorithm to help tune feature extraction and predict features well before the simulation has converged. The agent design allows direct collaboration between data-mining algorithms and scientist. CAF? allows the scientist to observe and correct data-mining of simulations without wasting valuable research time. With our impressive track record of transitioning technology (100th percentile DoD commercialization index) and our strong team, we are the right team to provide intelligently guided concurrent data-mining for high fidelity fluid dynamic simulations. BENEFIT: Current costs to develop a new aircraft engine are more than $2 billion and 10 years. While the Air Force and other services don't develop these components internally, the development of a new aircraft engine or airframe can have significant military application. University researchers and commercial contractors perform the lion's share of this type of work. A better understanding of the complexities of the component and its interactions could have a significant impact in extending the envelope of current aircraft and in the development of new aircraft. CAF? offers the capability to perform intelligent concurrent data-mining for high fidelity fluid dynamic simulations. The CAF? tool is able to extract and display accurate, near real-time patterns from massively large data sets by searching for physics-related events in complex large scale simulations. With CAF?, the potential exists to go even further through the interaction of concurrent, on-the-fly, queries and responses among the CAF? agents as well as with the human operator. The primary target customer will be the CFD researcher at the research institution or commercial entity. Initially, the CAF? tool will be tailored to simulations of fans and compressors of gas turbine engines. In order to minimize overall technical risk while at the same time reducing physical testing costs, aircraft manufactures continuously search for computational design tools to remain competitive. They rely heavily on continual advancement of the technological frontier for faster solutions to more complex problems, more accurate results with improved performance, enhanced safety, environmental acceptability, and less time involved to develop new products. 21CSI recognizes these challenges facing aircraft manufactures. The computational models developed under CAF? will significantly accelerate the testing cycle allowing more efficiency in the design processes used to develop various types of flight vehicles. Using these tools, aircraft engineers and designers can address a wide range of design challenges including airfoil and geometry selection, wake vortex alleviation, flutter aero-elastics, load design, stability and control, and high-speed wing design. Businesses will be easily able to justify the CAF? software expense with the time saving that achieve.

* information listed above is at the time of submission.

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