Electromagnetic Characterization of Advanced Composites by Voxel-Based Inverse Methods

Award Information
Agency: National Aeronautics and Space Administration
Branch: N/A
Contract: 80NSSC18C0151
Agency Tracking Number: 174099
Amount: $749,906.00
Phase: Phase II
Program: SBIR
Solicitation Topic Code: Z11
Solicitation Number: SBIR_17_P2
Timeline
Solicitation Year: 2017
Award Year: 2018
Award Start Date (Proposal Award Date): 2018-04-11
Award End Date (Contract End Date): 2020-04-10
Small Business Information
P.O. Box 7706, Bloomington, IN, 47407-7706
DUNS: 074529467
HUBZone Owned: N
Woman Owned: N
Socially and Economically Disadvantaged: N
Principal Investigator
 Harold Sabbagh
 (812) 360-3645
 has@sabbagh.com
Business Contact
 Harold Sabbagh
Phone: (812) 360-3645
Email: has@sabbagh.com
Research Institution
N/A
Abstract

The nondestructive
characterization of advanced composites, such as
carbon-fiber reinforced polymers (cfrp), by electromagnetic means is
well established [6]-[24].  What is
needed to advance the state of the art are sophisticated inversion
algorithms that allow layup and impact damage to be determined in
localized regions, which means that the more traditional methods of
model-based inverse methods must be replaced by voxel-based methods.
Thus, one will be able to better distinguish such things as
delaminations from fiber-breakage due to impact damage, or other
parameters that
characterize the mechanical state of the cfrp structure, such as
elastic modulus and Poisson's ratio on a
voxel-by-voxel basis. This information can then be input to damage
evolution models.  We describe
two such methods, bilinear conjugate-gradients and set-theoretic
estimation.  The challenge is to extend these methods to anisotropic
materials.  We do that in this project, and will develop the
algorithms for inclusion
in our proprietary eddy-current code, \vic, during Phase II.
In addition, we continue our program of discovering and exploiting
parallelism in VIC-3D(R) to speed up the modeling and processing of
problems that involve massive data generation.

 

 

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

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