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Motor Insulation Material Development for Improved Power Density

Award Information
Agency: Department of Defense
Branch: Navy
Contract: N65538-07-M-0060
Agency Tracking Number: N062-142-0365
Amount: $69,997.00
Phase: Phase I
Program: SBIR
Solicitation Topic Code: N06-142
Solicitation Number: 2006.2
Timeline
Solicitation Year: 2006
Award Year: 2006
Award Start Date (Proposal Award Date): 2007-01-10
Award End Date (Contract End Date): 2007-07-17
Small Business Information
9063 Bee Caves Road
Austin, TX 78733
United States
DUNS: 625120902
HUBZone Owned: No
Woman Owned: No
Socially and Economically Disadvantaged: No
Principal Investigator
 Brad Rix
 Principal Investigator
 (512) 263-2101
 brix@tri-austin.com
Business Contact
 Monte Fellingham
Title: Contracts Administration
Phone: (512) 263-2101
Email: mfellingham@tri-austin.com
Research Institution
N/A
Abstract

The propulsion motors under development for use on the U.S. Navy�s next generation destroyer, the DDG 1000, are in need of novel insulating resins that exceed the performance properties of the currently available vacuum-pressure impregnation (VPI) resins. Texas Research Institute Austin, Inc. proposes a new high temperature resin that exhibits excellent dielectric properties, can withstand short rise-time voltage surges associated with the use of a pulse width modulated (PWM) drive, and will dissipate thermal energy quickly away from the coils in order to effectively increase the power density for the motor. The resin will be engineered to be fully compatible with the VPI process. During Phase I, TRI/Austin will be teaming with a manufacturer of Naval advanced induction propulsion motors. This corporation will provide technical assistance and finite element analysis modeling of the proposed resin. An established supplier of VPI resins to the military will also be involved as a technical advisor and potential commercialization partner. The feasibility of the new resin will be proven through the generation of in-house experimental thermal, electrical, and physical property data followed by computational modeling to show predicted behavior in a large-scale motor.

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

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