USA flag logo/image

An Official Website of the United States Government

Advanced Data Processing, Storage and Visualization Algorithms for Structural…

Award Information

Agency:
Department of Defense
Branch:
Navy
Award ID:
95182
Program Year/Program:
2010 / STTR
Agency Tracking Number:
N10A-042-0385
Solicitation Year:
N/A
Solicitation Topic Code:
NAVY 10T042
Solicitation Number:
N/A
Small Business Information
Acellent Technologies, Inc.
835 Stewart Drive Sunnyvale, CA 94085-
View profile »
Woman-Owned: No
Minority-Owned: Yes
HUBZone-Owned: No
 
Phase 1
Fiscal Year: 2010
Title: Advanced Data Processing, Storage and Visualization Algorithms for Structural Health Monitoring Sensor Networks of Naval Assets
Agency / Branch: DOD / NAVY
Contract: N00014-10-M-0311
Award Amount: $70,000.00
 

Abstract:

Acellent Technologies Inc. and Prof. F. G. Yuan at North Carolina State University (NCSU) are proposing to develop a Hybrid Distributed Sensor Network Integrated with Self-learning Symbiotic Diagnostic Algorithms and Models to determine materials state awareness and its evolution, including identification of precursors, detection of microdamages and flaws near high stress area or in a distributed region. The SMART Layer concept will be used as a basis for the development of the hybrid distributed sensor network. The nonlinear behavior of microstructure defects (called micro-defects hereafter), which is intentionally eliminated or simply disregarded in the current conventional ultrasonic diagnosis, will be served as the basis for the development of nonlinear diagnostics for materials state awareness. The Self-learning Symbiotic Diagnostic Algorithms will employ nonlinear acoustic interpretation and statistical data driven analysis. The approach will be based on the principal physics of nonlinearity of materials and its effect on macro scale sensor signals together with an intelligent self instructing data driven algorithm as a wrapper program. Once developed, the sensor network permanently integrated with the structure can be used to accurately and robustly detect the precursors to damages that occur in the structure during scheduled stops or during scheduled maintenance intervals.

Principal Investigator:

X. Qing
Director of Sensor Techno
4087451188
peterq@acellent.com

Business Contact:

Vindhya Narayanan
VP Business
4087451188
vindhya@acellent.com
Small Business Information at Submission:

Acellent Technologies, Inc.
835 Stewart Drive Sunnyvale, CA 94085

EIN/Tax ID: 770377143
DUNS: N/A
Number of Employees:
Woman-Owned: No
Minority-Owned: No
HUBZone-Owned: No
Research Institution Information:
North Carolina State University
Department of Mech and Aero
1009 Capability Dr
Raleigh, NC 27695
Contact: F. G. Yuan
Contact Phone: 9195155947