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Intelligent fly-by-feel systems for autonomous aircraft

Award Information
Agency: Department of Defense
Branch: Air Force
Contract: FA8649-21-P-0163
Agency Tracking Number: FX20D-TCSO1-0161
Amount: $149,999.00
Phase: Phase I
Program: STTR
Solicitation Topic Code: AFX20D-TCSO1
Solicitation Number: X20.D
Timeline
Solicitation Year: 2020
Award Year: 2021
Award Start Date (Proposal Award Date): 2020-11-19
Award End Date (Contract End Date): 2021-05-20
Small Business Information
835 Stewart Drive -
Sunnyvale, CA 94085-1111
United States
DUNS: 043688410
HUBZone Owned: No
Woman Owned: Yes
Socially and Economically Disadvantaged: Yes
Principal Investigator
 Amrita Kumar
 (408) 745-1188
 akumar@acellent.com
Business Contact
 Amrita Kumar
Phone: (408) 745-1188
Email: akumar@acellent.com
Research Institution
 Stanford University
 Natlie Muzzio
 
485 Broadway, Third Floor
Redwood City, CA 94063-0000
United States

 (650) 724-0907
 Nonprofit College or University
Abstract

Future intelligent autonomous vehicles like the Orb/eVTOL/UAM (Electric Vertical Takeoff and Landing/Urban Air Mobility) vehicles need the ability to “feel”, “think”, and “react” in real time to enable state-sensing, awareness, and self-diagnostic capabilities in complex dynamic environments to ensure safe operations, reduced maintenance costs, and complete life-cycle management. The proposed STTR will focus on the development and transition of bio-inspired stretchable sensor technologies integrated with composite air vehicle structures to provide the ability for state sensing as well as operational and flight changes to enable Fly-by-Feel sensing capability mimicking the biological bird flight. The team of Acellent (lead) and  Stanford University supported by Boeing and Triumph Aerospace will collaboratively work on this program. In Phase I, the team will develop micro-fabricated stretchable sensor networks, including integrated piezoelectric, strain, and temperature sensors that are designed and monolithically embedded in a composite aircraft structure. In addition, artificial deep learning algorithms integrated with physics-driven models will be developed to interpret the sensing data collected in real time in terms of flight state and health condition of the vehicle. An experimental evaluation and assessment of the intelligent composite structure will be demonstrated through wind tunnel testing. Plans for testing the complete system in Phase II when  integrated with an air vehicle identified in Phase I will also be developed. 

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

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