Prasad Dharap

Nanotube film based on single-wall carbon nanotubes for strain sensing

Carbon nanotubes change their electronic properties when subjected to strains. In this study, the strain sensing characteristic of carbon nanotubes is used to develop a carbon nanotube film sensor that can be used for strain sensing on the macro scale. The carbon nanotube film is isotropic due to randomly oriented bundles of single-wall carbon nanotubes (SWCNTs). Using experimental results it is shown that there is a nearly linear change in voltage across the film when it is subjected to tensile and compressive stresses. The change in voltage is measured by a movable four-point probe in contact with the film. Multidirectional and multiple location strains can be measured by the isotropic carbon nanotube film.

Actuator Failure Detection Through Interaction Matrix Formulation

An ovel technique is introduced to detect and isolate the failures of multiple actuators connected to a system. The failure of actuator considered in this study could be any type of erroneous input that is different from the commanded one. The interaction matrix technique allows the development of input-output equations that are only influenced by one target input. These input-output equations serve as an effective tool to monitor the integrity of each actuator regardless of the status of the other actuators. Although the procedure requires the knowledge of analytical model of the system being tested, the analytical redundancy can be experimentally predetermined through standard input-output-based system identification algorithms such as observer/Kalman-filter identification (OKID) and eigensystem realization algorithm (ERA). This method is capable of real-time actuator failure detection and isolation under any type of input excitation. Both numerical simulations of a spring-mass-damper system and a laboratory experiment using eight-bay NASA truss structure verify the feasibility of the proposed method.

Real-Time Structural Damage Monitoring by Input Error Function

A newly developed structural damage monitoring technique is presented. The study focuses on capturing the initiation of multiple damages as they occur in a structure, which is similar to the concept of the fault detection filter. Previously, it has been shown that modified interaction matrix formulation provides a series of input error functions that generate a nonzero residual signal when the system experiences erroneous inputs. Error functions for each individual structural member are developed from the analogy between actuator failure and damageinduced residual force. When each individual error function is monitored, multiple damages as they occur in a structure can be simultaneously detected and isolated. Because the technique does not require frequency-domain measurements, it is readily applicable to online monitoring systems. This real-time technique also accommodates nonlinear breathing cracks and works for any type of excitation. A numerical simulation using a spring‐mass system and truss structure successfully demonstrates the proposed method.

Structural Health Monitoring using ARMarkov Observers

A new method based on a bank of ARMarkov observers is proposed in this study for determination of the extent of damage. The objective of this article is to present a new formulation using a predesigned set of ARMarkov observers to determine the extent of damage and track further changes in the stiffness of the damaged member. The primary advantages of the proposed formulation over the existing methods are: (1) ARMarkov observers are designed based on interaction matrix formulation so that knowledge about exact initial conditions is not necessary, and (2) noise statistics are not required a priori to design a bank of ARMarkov observers when compared to a bank of Kalman filters. The simulation results of several examples including a planar truss structure with progressive damage in a member are presented to highlight the capability of the proposed method. The proposed method works well in the case of either a full set or a limited number of available measurements. It is shown that sensitivity enhancing control (SEC) can be easily incorporated into the proposed method to enhance the sensitivity of structural damage. In case of noisy output measurements, it is shown that it is possible to distinguish between the errors due to structural damage and due to noise in output measurements.

Continuum field model of defect formation in carbon nanotubes

While considerable efforts in the form of (numerical) atomistic simulations have been expended to understand the mechanics of defect formation under applied strain, analogous analytical efforts have been rather few. In this work, based on the physics at the nanoscale, defect nucleation in single-walled carbon nanotubes is studied using both classical continuum field theory as well as gauge field theory of defects. Despite the inherent continuum assumption in our models, reasonably close qualitative and quantitative agreement with existing atomistic simulations is obtained. The latter lends credence to the belief that continuum formulations, with correct incorporation of the relevant physics, can be a powerful and yet simple tool for exploring nanoscale phenomena in carbon nanotubes. The results are more sensitive to chirality than to the size of the nanotubes.

Flexural strain sensing using carbon nanotube film

Strain sensing characteristic of carbon nanotubes has been established in the past at nanoscale. In this study, it is shown that the carbon nanotube film sensors, made up of randomly oriented carbon nanotubes, can be used as strain sensors at macro level. A nearly linear trend between the change in voltage, measured using a movable four point probe, and strains, measured using conventional electrical strain gage, indicates the potential of such carbon nanotube films for measuring flexural strains at macro level. Isotropic strain sensing capability of the carbon nanotube film sensors, due to randomly oriented carbon nanotubes, allows multidirectional and multi‐location measurements.