SPECIAL NOTICE
99 -- TECHNOLOGY TRANSFER OPPORTUNITY –-Inflight Global Nonlinear Aerodynamics Modeling and Simulation: LAR-18410-1
- Notice Date
- 8/10/2016
- Notice Type
- Special Notice
- NAICS
- 927110
— Space Research and Technology
- Contracting Office
- NASA/Langley Research Center, Mail Stop 144, Industry Assistance Office, Hampton, Virginia, 23681-0001
- ZIP Code
- 23681-0001
- Solicitation Number
- TT01192
- Archive Date
- 8/23/2017
- Point of Contact
- Jesse C Midgett, Phone: 7578643936
- E-Mail Address
-
j.midgett@nasa.gov
(j.midgett@nasa.gov)
- Small Business Set-Aside
- N/A
- Description
- NASA Langley Research Center in Hampton, VA solicits inquiries from companies interested in obtaining license rights to commercialize, manufacture and market the following technology. License rights may be issued on an exclusive or nonexclusive basis and may include specific fields of use. NASA provides no funding in conjunction with these potential licenses. THE TECHNOLOGY: Scientists at NASA Langley Research Center have developed an inflight global nonlinear aerodynamics modeling and simulation system. The technology replaces the normal labor-intensive iterative process of repeated flight tests and combining locally-valid models with a single flight and automatically developed globally-valid model. The technology is highly accurate and efficient for developing global aerodynamic and thrust models for aircraft. This technology, the first phase in a suite of technologies designed to control autonomous (unmanned) vehicles, is a method of developing a model that characterizes the aerodynamics and/or thrust of an airplane or spacecraft. The model can be developed and flight validated in as little as a single flight by tracking data relevant to the aircraft response to controls and external forces acting on the vehicle. Current state of the art requires that repeated test flights be made to gather data from different flight conditions, followed by evaluation and analysis on the ground, and repeated over a number of flights, to eventually yield a combined linear, approximate model. This technology, however, can accomplish the development of a validated high-fidelity, global, nonlinear model in as little as a single flight. The technology is part of the “Learn to Fly” project to develop real-time models and controllers for autonomous aircraft and spacecraft. For application in piloted aircraft, the system can provide alerts in dangerous conditions, such as tail icing, through detecting abnormal aircraft responses. Potential applications include aircraft, spacecraft, marine craft, and self-driving cars and trucks. NASA is seeking to license this technology commercially. US Patent pending. To express interest in this opportunity, please respond to LaRC-PatentLicensing@mail.nasa.gov with the title of this Technology Transfer Opportunity as listed in this FBO notice and your preferred contact information. Please also provide the nature of your interest in the technology along with a brief background of your company. For more information about licensing other NASA-developed technologies, please visit the NASA Technology Transfer Portal at http://technology.nasa.gov/. These responses are provided to members of NASA Langley’s Office of Strategic Analysis and Business Development “OSACB” for the purpose of promoting public awareness of NASA-developed technology products, and conducting preliminary market research to determine public interest in and potential for future licensing opportunities. If direct licensing interest results from this posting, OSACB will follow the required formal licensing process of posting in the Federal Register. No follow-on procurement is expected to result from responses to this Notice.
- Web Link
-
FBO.gov Permalink
(https://www.fbo.gov/spg/NASA/LaRC/OPDC20220/TT01192/listing.html)
- Record
- SN04217167-W 20160812/160810234656-7adeab50ef544a59f5c8d1163837a6d8 (fbodaily.com)
- Source
-
FedBizOpps Link to This Notice
(may not be valid after Archive Date)
| FSG Index | This Issue's Index | Today's FBO Daily Index Page |