| ||The technology within this topic is restricted under the International Traffic in Arms Regulation (ITAR), which controls the export and import of defense-related material and services. Offerors must disclose any proposed use of foreign nationals, their country of origin, and what tasks each would accomplish in the statement of work in accordance with section 3.5.b.(7) of the solicitation.|| ||STATEMENT OF INTENT: This topic holds the greatest potential for meeting the technical needs of our warfighters supported by PEOs and Centers.
|| Objective: ||Improve the speed, reliability, and accuracy of parameter identification algorithms used for flutter and flying qualities flight testing.
|| Description: ||A significant portion of the flight testing that must be accomplished in order to certify a new aircraft is dedicated to flutter testing and flying qualities testing. The objective of flutter testing is to assure that the aircraft is aeroelastically stable within the operational envelope of the aircraft. The purpose of flying qualities testing is to assure that the aircraft is controllable within the operational envelope of the aircraft.
Flutter testing and flying qualities testing differ significantly in terms of frequencies of interest, open versus closed loop stability, and frequency-damping versus phase-magnitude. However, both disciplines are fundamentally trying to identify the transfer functions that best describe the aeroelastic stability and flying qualities of the aircraft at various flight conditions.
Currently it takes several minutes worth of flight test data to identify one of these transfer functions for one flight condition. With the right combination of pre-processing, modern parameter identification algorithms, and flight test techniques the time should be reduced to 5 seconds or less, which would dramatically reduce the time and cost required to clear the aircraft envelope, and subsequently certify the aircraft.
In order to realize this potential gain in efficiency, fundamental development of the pre-processing techniques and parameter identification algorithms must be addressed. Specifically, the algorithms must be made robust enough to tolerate the low signal to noise ratio inherent in flight test data and adequate data resolution given the limited bandwidth available for telemetry of flight test data.
Although this advanced research will build on existing parameter identification technology, preparing them to be utilized in an open air flight test environment will require a significant development. Ultimately, the results of this research will not only benefit the military and commercial aviation, but also other fields that rely on parameter identification, like the automotive, music, voice recognition, and rotating machinery industries.
|| ||PHASE I: Identify time and frequency domain parameter identification algorithms that tolerate low signal to noise ratio and identify parameters with very limited data sets. Identify developmental pathways that lead to a robust parameter identification tool for flutter and flying qualities flight testing.
|| || ||PHASE II: Using the results from Phase I, develop a robust parameter identification tool and displays prototype to be utilized for flutter and flying qualities flight testing.
|| ||DUAL USE COMMERCIALIZATION: Commercial and military aviation will benefit from this research since both segments of the aircraft industry are equally affected by instabilities that effect the elastic structure and the rigid body motion of the aircraft. In addition there are many industries that rely on parameter identification that could also benefit from this research. For example, the automotive, music, voice recognition, and rotating machinery industries could benefit.
|| References: ||Edward N Bachelder, Peter M. Thompson, Chuck Harris, “System Identification Methods for Improving Flutter Flight Test Techniques.” AIAA Atmospheric Mechanics Conference, 16-19 August 2004, Providence, RI
2. Rick Lind, Marty Brenner, Flight Test Evaluation of Flutter Prediction Methods, American Institute of Aeronautics and Astronautics, 2002, AIAA-2002-1649
|Keywords: ||Parameter Identification, System Identification, Data Analysis|