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Semi-empirical Rotorcraft Acoustics Model using Parameter Identification Techniques

Award Information
Agency: Department of Defense
Branch: Army
Contract: W911W6-19-C-0035
Agency Tracking Number: A183-132-0362
Amount: $99,995.56
Phase: Phase I
Program: SBIR
Solicitation Topic Code: A18-132
Solicitation Number: 18.3
Solicitation Year: 2018
Award Year: 2019
Award Start Date (Proposal Award Date): 2019-02-05
Award End Date (Contract End Date): 2020-03-15
Small Business Information
34 Lexington Avenue
Ewing, NJ 08618
United States
DUNS: 096857313
HUBZone Owned: No
Woman Owned: No
Socially and Economically Disadvantaged: No
Principal Investigator
 Daniel A Wachspress
 Senior Associate
 (609) 538-0444
Business Contact
 Barbara A. Agans
Phone: (609) 538-0444
Research Institution

US warfighter safety could be significantly improved by providing an ability to quickly and accurately assess the acoustic impact of rotorcraft operations in the field and reduce the likelihood of detection. Current mission planning tools are empirical in nature, limited to operating conditions for which flight test data exists and challenged in predicting the acoustic impact of maneuvering flight. First-principles models capable of predicting acoustics beyond the range of test data cannot produce helicopter noise spheres with the accuracy of empirical models nor in the time frame required by mission planners. The solution proposed here is to develop a time-domain based hybrid method, in which a mid-fidelity, physics-based helicopter aeroacoustic model is calibrated to measured data using parameter identification techniques to provide accuracy comparable to current empirical models where measured data exists and superior to current empirical models for flight conditions where measured data does not exist. This includes accurate extrapolation from steady flight data to maneuvering flight predictions. Existing, validated, mid-fidelity, commercially-available helicopter aeroacoustic prediction software will be calibrated to measured data using parameter identification methods tailored specifically for use in this application based on insights drawn from the experience of the proposing team and Phase I research.

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

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