This paper presents a trailing edge flap actuation mechanism using a novel pre-stressed piezoelectric unimorph, PUMPS
(Piezoelectric Unimorph with Mechanically Pre-stressed Substrate). Experimental evaluation of actuation performance
such as force-displacement characteristics of PUMPS actuators showed that the performance of PUMPS satisfied the
requirements for trailing edge flap actuation. Subsequently, flap actuation mechanisms were designed and constructed
with several slot types in the flaps, and stacked PUMPS actuators were applied to the flap actuation mechanisms.
Experimental study of the test wing models with four flaps was accomplished, and the flap angle was achieved up to
±5.5° within 15Hz under maximum applicable voltage.
A method for impact and damage detection on a plate using strain responses from long continuous sensors and analysis by a neural network technique was presented and verified by numerical simulation. The response characteristics of continuous sensors, which are long ribbon-like sensors, were studied by simulation of wave propagation in a panel. The advantage of the continuous sensor is to improve damage detection by having a large coverage of sensors on the structure using a small number of channels of data acquisition. Strain responses from the continuous sensors were used to estimate the damage location using the neural network technique. Several numerical wave propagation simulation runs for a plate were carried out to train the neural network and verify the proposed method for damage localization. The identified damage locations agreed reasonably well with the exact damage locations. Overall, the approach presented is meant to simplify the instrumentation needed for damage detection by using continuous sensors, a small number of channels of data acquisition, and training a neural network to do the work of locating the damage source.
A small size prototype of a Structural Neural System (SNS) was tested in real time for damage detection in a
laboratory setting and the results are presented in this paper. The SNS is a passive online structural health
monitoring (SHM) system that can detect small propagating damages in real time before the overall failure of the
structure is realized. The passive SHM method is based on the concept of detecting acoustic emissions (AE) due to
damage propagating. Propagating cracks were identified near the vicinity of a sensor in a composite specimen
during fatigue testing. In the composite specimen, in additions to a propagating crack, fretting occurred because of
slipping contact between the load points and the composite specimen. The SNS was able to predict the location of
damage due to crack propagation and also detect signals from fretting simultaneously in real time.
This paper discusses the development of new multifunctional smart materials based on Carbon Nanofibers (CNF) and Multi-Wall Carbon Nanotubes (MWCNT). The material properties of CNF/MWCNT are a little lower than the properties of Single Wall Carbon Nanotubes (SWCNT). However, the CNF/MWCNT have the potential for more practical applications since their cost is lower. This paper discusses the development of four CNF/MWCNT-based sensors and actuators. These are: (i) an Electrochemical Wet Actuator for use in a liquid electrolyte, (ii) an Electrochemical Dry Actuator for use in a dry environment, (iii) a Bioelectronic sensor; and (iv) a MWCNT neuron for structural health monitoring. These materials are exciting because of their unique properties and many applications.
Structural Health Monitoring ideally would check the health of the structure in real time all the time. Simplifying the sensor system and the data acquisition equipment plays a very important role in achieving this goal. This paper discusses a practical technique that uses long continuous sensors and biomimetic signal processing to simplify health monitoring. The testing of a structural neural system with an updated analog processor module is discussed in this paper. A neuron is formed by connecting sensor elements to an analog processor. The structural neural system is formed by connecting multiple neurons to mimic the signal processing architecture of the neural system of the human body. This approach reduces the required number of data acquisition channels and still predicts the location of damage within a grid of miniature neurons. Different types of sensors can also be used. A piezoelectric ribbon sensor can sense damage due to impacts or crack growth because these damages generate Lamb waves that are detected by the neural system. The neuron can also receive diagnostic waves generated to check the structure on demand and when it is not in operation. In addition, new continuous multi-wall carbon nanotube sensors are being used as strain and crack detection neurons that operate during both static and dynamic loading. In general, the Structural Neural System may provide an advantage for the continuous monitoring of most large sensor systems in which anomalous events must be detected, and where it is impractical to have a separate channel of data acquisition for each sensor. Moreover, the data reduction technique and damage detection algorithm are easy to understand, simple to implement, reliable, and many sensor types can be used.
The committee technique for neural networks has been widely used for pattern recognitions in speech and vision studies. In this study, the committee technique is applied to damage estimation of structures for the purpose of structural health monitoring. The input to the neural networks consists of the modal parameters, and the output is composed of the element-level damage indices. Multiple neural networks are constructed and each individual neural networks is trained independently with different initial synaptic weights. Then, the estimated damage indices from different neural networks are averaged. Several committee methods were investigated and used to estimate the element-level damage locations and severities. The validity of the committee technique for damage estimation was examined on a frame structure through numerical simulation. Then experiments were carried out on a bridge model with a composite cross section subjected to vehicle loadings. It has been found that the estimated damage indices improve significantly by employing the committee technique.