Award Abstract # 9115380
Measurement of Strength-Related Micro-Stress Distribution in Fiber/Matrix/Interphase Systems

NSF Org: CMMI
Division of Civil, Mechanical, and Manufacturing Innovation
Recipient: VIRGINIA POLYTECHNIC INSTITUTE & STATE UNIVERSITY
Initial Amendment Date: September 5, 1991
Latest Amendment Date: September 5, 1991
Award Number: 9115380
Award Instrument: Standard Grant
Program Manager: HUSEYIN A. SEHITOGLU
CMMI
 Division of Civil, Mechanical, and Manufacturing Innovation
ENG
 Directorate for Engineering
Start Date: August 15, 1991
End Date: January 31, 1993 (Estimated)
Total Intended Award Amount: $56,610.00
Total Awarded Amount to Date: $56,610.00
Funds Obligated to Date: FY 1991 = $56,610.00
History of Investigator:
  • Kenneth Reifsnider (Principal Investigator)
    kenneth.reifsnider@uta.edu
  • Greg Carman (Co-Principal Investigator)
Recipient Sponsored Research Office: Virginia Polytechnic Institute and State University
300 TURNER ST NW
BLACKSBURG
VA  US  24060-3359
(540)231-5281
Sponsor Congressional District: 09
Primary Place of Performance: DATA NOT AVAILABLE
Primary Place of Performance
Congressional District:
Unique Entity Identifier (UEI): QDE5UHE5XD16
Parent UEI: X6KEFGLHSJX7
NSF Program(s): MATERIALS
Primary Program Source:  
Program Reference Code(s):
Program Element Code(s): 144600
Award Agency Code: 4900
Fund Agency Code: 4900
Assistance Listing Number(s): 47.041

ABSTRACT

The objectives of this research are to validate current and proposed composite micromechanical models by direct experimental measurements and to determine the resulting stress state near damaged regions in materials subjected to tensile, compressive, and bending loads. This will be accomplished with a unique test methodology which employs a geometrically scaled up version of an polymeric composite containing embedded sensors. These sensors provide a direct and systematic approach to measure strain redistribution in a material and provides previously unobtainable baseline data which are essential in validating existing models and developing new ones.

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