Title page for ETD etd-08252010-121333


Type of Document Dissertation
Author Sunny, Mohammed Rabius
Author's Email Address sunny@vt.edu
URN etd-08252010-121333
Title Towards Structural Health Monitoring of Gossamer Structures Using Conductive Polymer Nanocomposite Sensors
Degree PhD
Department Aerospace and Ocean Engineering
Advisory Committee
Advisor Name Title
Kapania, Rakesh K. Committee Chair
Batra, Romesh C. Committee Member
Borggaard, Jeffrey T. Committee Member
Patil, Mayuresh J. Committee Member
Philen, Michael K. Committee Member
Keywords
  • Fuzzy Logic
  • Neural Network
  • Compensator
  • Preisach Model
  • Structural Health Monitoring
  • Hysteresis
  • Relaxation
  • Fractional Calculus Model
  • Gossamer Structures
  • Prestressed Membrane
  • Neuro-fuzzy System
Date of Defense 2010-06-25
Availability unrestricted
Abstract
The aim of this research is to calibrate conductive polymer nanocomposite materials for

large strain sensing and develop a structural health monitoring algorithm for gossamer

structures by using nanocomposites as strain sensors. Any health monitoring system works

on the principle of sensing the response (strain, acceleration etc.) of the structure to an

external excitation and analyzing the response to find out the location and the extent of

the damage in the structure. A sensor network, a mathematical model of the structure, and

a damage detection algorithm are necessary components of a structural health monitoring

system. In normal operating conditions, a gossamer structure can experience normal strain

as high as 50%. But presently available sensors can measure strain up to 10% only, as

traditional strain sensor materials do not show low elastic modulus and high electrical

conductivity simultaneously. Conductive polymer nanocomposite which can be stretched

like rubber (up to 200%) and has high electrical conductivity (sheet resistance 100 Ohm/sq.)

can be a possible large strain sensor material. But these materials show hysteresis and

relaxation in the variation of electrical properties with mechanical strain. It makes the

calibration of these materials difficult. We have carried out experiments on conductive

polymer nanocomposite sensors to study the variation of electrical resistance with time

dependent strain. Two mathematical models, based on the modified fractional calculus and

the Preisach approaches, have been developed to model the variation of electrical resistance

with strain in a conductive polymer. After that, a compensator based on a modified Preisach

model has been developed. The compensator removes the effect of hysteresis and relaxation

from the output (electrical resistance) obtained from the conductive polymer nanocomposite

sensor. This helps in calibrating the material for its use in large strain sensing. Efficiency of

both the mathematical models and the compensator has been shown by comparison of their

results with the experimental data. A prestressed square membrane has been considered

as an example structure for structural health monitoring. Finite element analysis using

ABAQUS has been carried out to determine the response of the membrane to an uniform

transverse dynamic pressure for different damage conditions. A neuro-fuzzy system has been

designed to solve the inverse problem of detecting damages in the structure from the strain

history sensed at different points of the structure by a sensor that may have a significant

hysteresis. Damage feature index vector determined by wavelet analysis of the strain history at different points of the structure are taken by the neuro-fuzzy system as input. The neuro-fuzzy system detects the location and extent of the damage from the damage feature index

vector by using some fuzzy rules. Rules associated with the fuzzy system are determined

by a neural network training algorithm using a training dataset, containing a set of known

input and output (damage feature index vectors, location and extent of damage for different

damage conditions). This model is validated by using the sets of input-output other than

those which were used to train the neural network.

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