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Prediction of Rotor Loads from Fuselage Sensors for Improved Structural Modeling and Fatigue Life Calculation

Award Information
Agency: Department of Defense
Branch: Navy
Contract: N68335-17-C-0376
Agency Tracking Number: N17A-009-0007
Amount: $129,893.00
Phase: Phase I
Program: STTR
Solicitation Topic Code: N17A-T009
Solicitation Number: 2017.0
Solicitation Year: 2017
Award Year: 2017
Award Start Date (Proposal Award Date): 2017-05-19
Award End Date (Contract End Date): 2018-01-02
Small Business Information
13290 Evening Creek Drive South
San Diego, CA 92128
United States
DUNS: 133709001
HUBZone Owned: No
Woman Owned: No
Socially and Economically Disadvantaged: No
Principal Investigator
 Nicolas Reveles, Ph.D.
 Project Engineer
 (245) 258-8406
Business Contact
 Joshua Davis
Phone: (858) 480-2028
Research Institution
 Georgia Institute of Technology
 Jarrett Ellis
 (404) 894-6929
 Domestic Nonprofit Research Organization

ATA Engineering and researchers at the Georgia Institute of Technology will develop a framework for the accurate reconstruction of rotor loads from a suite of fixed-frame fuselage sensors that are utilized to augment physics-based simulations. The loads reconstruction framework will consist of two modules: the physics module, which provides first-principles predictions from simulations, and the sensor augmentation module, which combines virtual sensor data from the physics module with physical in-flight sensor measurements to improve the correlation. The sensor augmentation module, to be developed from an iterative confluence algorithm that will update the simulation model based on the difference between virtual sensors and measured data, will be capable of resolving nonlinear effects. The physics module will include a physics-based simulation (built from a tightly coupled comprehensive solver), a finite element analysis code, and an optional fast aerodynamic rotor wake code. Together, the loads reconstruction framework will improve correlation between simulation and measurement, enabling full dynamic response reconstruction for fatigue prediction. In Phase I, the feasibility of the approach will be investigated by testing the predictive capability on high-fidelity simulations and by comparing results with flight test data. Finally, the team will investigate optimal sensor selection to improve correlation.

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

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