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Particle Ingestion Engine Sensor

Award Information
Agency: Department of Defense
Branch: Navy
Contract: N68335-18-C-0389
Agency Tracking Number: N18A-023-0076
Amount: $125,000.00
Phase: Phase I
Program: STTR
Solicitation Topic Code: N18A-T023
Solicitation Number: 2018.0
Timeline
Solicitation Year: 2018
Award Year: 2018
Award Start Date (Proposal Award Date): 2018-06-04
Award End Date (Contract End Date): 2018-12-06
Small Business Information
2501 Earl Rudder Freeway South
College Station, TX 77845
United States
DUNS: 184758308
HUBZone Owned: N
Woman Owned: N
Socially and Economically Disadvantaged: N
Principal Investigator
 Sanil John
 (979) 764-2200
 sanil.john@lynntech.com
Business Contact
 Ms. McCord
Phone: (979) 764-2200
Email: contract@lynntech.com
Research Institution
 Texas A&M Univeristy
 Heather Henry
 
Texas Engineering Experiment S 400 Harvey Mitchell Parkway S.
College Station, TX 77845
United States

 (979) 845-6733
 Nonprofit college or university
Abstract

Sand, dust and ash particles have significant detrimental effects on turbine engine performance and durability. Currently there are no sensors capable of recording data such as dust composition, particle size distribution, total mass, etc. and have sufficient durability for flight conditions. Lynntech and Texas A&M University propose to develop a sensor system based on a spectroscopic method integrated with a tunable size classification technique for real-time characterization of particles. During the Phase I project, characterization of standard test dust particles will be performed to identify and quantify CMAS constituents and other species such as NaCl. Particle analysis will be performed with single composition particles as well as standard test dust. Lynntech will engage with experts from the aerospace industry and within NAVIR to discuss sensor specifications and integration with an engine platform. During the option period of the Phase I project, volcanic ash simulants will be analyzed using the sensor system and functioning of the size classification technique will be demonstrated. During Phase II, the sensor system will be validated and optimized close to near-engine velocities on a test rig at Lynntech. Optimization of the sensor system design and its packaging into a ruggedized breadboard unit is also envisioned.

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

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