Ablation therapy is used as an alternative to surgical resection of hepatic tumors. In ablation, tumors are
destroyed through heating by RF current, high intensity focused ultrasound (HIFU), or other energy sources.
Ablation can be performed with a linear array transducer delivering unfocused intense ultrasound (<10
W/cm<sup>2</sup>). This allows simultaneous treatment and imaging, a feature uncommon in RF ablation. Unfocused
ultrasound can also enable faster bulk tissue ablation than HIFU.
In the experiments reported here, a 32-element linear array transducer with a 49 mm aperture delivers 3.1
MHz continuous wave unfocused ultrasound at amplitudes 0.7-1.4 MPa during the therapy cycle. It also
operates in pulse-echo mode to capture B-scan images. Ex-vivo fresh bovine liver tissue placed in degassed
saline is exposed to continuous wave ultrasound interleaved with brief pulsed ultrasound imaging cycles.
Tissue exposures range between 5 to 20 minutes. The following measurements are made at intervals of 1 to 3
seconds: tissue temperature with a needle thermocouple, acoustic emissions with a 1 MHz passive unfocused
detector, and tissue echogenicity from image brightness.
Passively detected acoustic emissions are used to quantify cavitation activity in the ablation experiments
presented here. As severity and extent of tissue ablation are related to temperature, this paper will statistically
model temperature as a function of tissue echogenicity and cavitation. The latter two quantities can
potentially be monitored noninvasively and used as a surrogate for temperature, enabling improved image
guidance and control of ultrasound ablation.
Detecting and locating cracks in structural components and joints that have high feature densities is a challenging problem in the field of Structural Health Monitoring. There have been advances in piezoelectric sensors, actuators, wave propagation, MEMS, and optical fiber sensors. However, few sensor-signal processing techniques have been applied to the monitoring of joints and complex structural geometries. This is in part because maintaining and analyzing a large amount of data obtained from a large number of sensors that may be needed to monitor joints for cracks is difficult. Reliable low cost assessment of the health of structures is crucial to maintain operational availability and productivity, reduce maintenance cost, and prevent catastrophic failure of large structures such as wind turbines, aircraft, and civil infrastructure. Recently, there have also been advances in development of simple passive techniques for health monitoring including a technique based on mimicking the biological neural system using electronic logic circuits. This technique aids in reducing the required number of data acquisition channels by a factor of ten or more and is able to predict the location of a crack within a rectangular grid or within an arbitrarily arranged network of continuous sensors or neurons. The current paper shows results obtained by implementing this method on an aluminum plate and joint. The plates were tested using simulated acoustic emissions and also loading via an MTS machine. The testing indicates that the neural system can monitor complex joints and detect acoustic emissions due to propagating cracks. High sensitivity of the neural system is needed, and further sensor development and testing on different types of joints is required. Also indicated is that sensor geometry, sensor location, signal filtering, and logic parameters of the neural system will be specific to the particular type of joint (material, thickness, geometry) being monitored. Also, a novel piezoresistive carbon nanotube nerve crack sensor is presented that can become a neuron and respond to local crack growth.
This paper examines the use of continuous sensors to detect damage in composite materials. Continuous sensors contain multiple interconnected sensor nodes that can be integrated into an artificial neural system as an array of sensor nerves. The advantage of this passive health monitoring approach is that the sensor system is highly distributed and uses parallel processing allowing large structures to be monitored for damage using a small number of channels of data acquisition. In the paper, the continuous sensor system is modeled and simulated by solving the elastic response of a plate and the coupled piezoelectric constitutive equations. The model and simulation allow the sensor system to be optimized for a particular material and plate size. The simulation predicts that acoustic waves representative of damage growth can be detected anywhere in the plate using a simple artificial neural system. To improve the sensitivity of the continuous sensor, unidirectional active fiber composite sensors were built from piezoceramic ribbon preforms. Manufacturing of the active fiber composite sensors is also discussed in the paper. The continuous sensors were evaluated in a realistic test to show their ability detect acoustic emissions caused by damage to a composite material. The sensors were mounted on narrow glass fiber plates and tested to failure in a mechanical test machine. Results from the experiments are presented.
This paper explores concepts for new smart materials that have extraordinary properties based on nanotechnology. Carbon and boron nitride nanotubes in theory can be used to manufacture fibers that have piezoelectric, pyroelectric, piezoresistive, and electrochemical field properties. Smart nanocomposites designed using these fibers will sense and respond to elastic, thermal, and chemical fields in a positive human-like way to improve the performance of structures, devices, and possibly humans. Remarkable strength, morphing, cooling, energy harvesting, strain and temperature sensing, chemical sensing and filtering, and high natural frequencies and damping will be the properties of these new materials. Synthesis of these unique atomically precise nanotubes, fibers, and nanocomposites is at present challenging and expensive, however, there is the possibility that we can synthesize the strongest and lightest actuators and most efficient sensors man has ever made. A particular advantage of nanotube transducers is their very high load bearing capability. Carbon nanotube electrochemical actuators have a predicted energy density at low frequencies that is thirty times greater than typical piezoceramic materials while boron nitride nanotubes are insulators and can operate at high temperatures, but they have a predicted piezoelectric induced stress constant that is about twenty times smaller than piezoceramic materials. Carbon nanotube fibers and composites exhibit a change in electrical conductivity due to strain that can be used for sensing. Some concepts for nanocomposite material sensors are presented and initial efforts to fabricate carbon nanocomposite load sensors are discussed.
This paper discusses recent advances in modeling and simulation of an artificial neural system and simulation of wave propagation for designing structural health monitoring systems. An artificial neural system was modeled using piezoceramic nerves and electronic components. Wave propagation in a panel is modeled using classical plate theory and a closed-form solution of wave propagation and reflection is obtained. Equations representing a half sine input similar to a projectile impact or a tone burst excitation were added to the existing algorithm that predicts the response of the artificial neural system due to impulse inputs. Firing switches have been modeled in the simulation to predict the sequential firing of the neurons as the waves pass over them. Also, new active fiber sensors have been designed for use in the artificial neural system. Simulated responses of the artificial neural system are shown in this paper and indicate that large neural systems can be formed with hundreds of sensor nodes. Experiments were performed to study a small neural system on a glass fiber panel. Waves were induced in the panel due to a lead break to simulate a crack and due to an impact from an impact hammer. Testing showed the location of a crack could be determined within the unit cell of the neural system for an orthotropic panel.
This paper discusses the development of continuous Active Fiber Composite sensors to detect damage in composite materials. Continuous sensors contain multiple interconnected sensor nodes that can be integrated into an artificial neural system as an array of sensor nerves. Continuous sensors have demonstrated a possibility of damage detection in large structures when used as a part of Artificial Neural System. The advantage of this passive health monitoring approach is that the sensor system is highly distributed and uses parallel processing allowing large structures to be monitored for damage using a small number of channels of data acquisition. In the paper, the continuous sensor system is modeled and simulated by solving the elastic response of a plate and the coupled piezoelectric constitutive equations. The model and simulation allow the sensor system to be optimized for a particular material and plate size. The simulation predicts that acoustic waves representative of damage growth can be detected anywhere in the plate using a simple artificial neural system. To improve the sensitivity of the continuous sensor, unidirectional active fiber composite sensors were built from piezoceramic ribbon preforms. Manufacturing of the active fiber composite sensors is discussed in the paper. The continuous sensors were evaluated in a realistic test to show their ability detect acoustic emissions caused by damage to a composite material. The sensors were mounted on narrow glass fiber plates and tested to failure in a mechanical test machine. Results from the experiments are also presented.
This paper discusses the potential for using Piezoceramic and Nanotube materials to develop an artificial neural system for structural health monitoring. An artificial neural system array was modeled using piezoceramic nerves and electronic components. The neural system was simulated using one hundred dual-output sensor nodes on a four-foot square composite panel. The nodal outputs were combined into twenty neuron firing signals, one row time signal, and one column time signal. This system was able to detect and locate acoustic waves and large strains in the panel. Also discussed, is the potential for using nanotubes for building the artificial neural system. In carbon nanotubes, an electrochemical process can be used to achieve low voltage actuation at high strain, but the process velocity is slow and a structural polymer electrolyte must be used for ion exchange. Carbon and boron nitride nanotubes can be piezoelectric, and piezonanotechnolgy may be useful for building high bandwidth neural systems. The operating temperature of boron nitride is high and the amount of material needed to build artificial nerves is small, but the piezoelectric coefficients appear to be small. Nanotube molecular electronics and the change in conductance of nanotubes might also be used to develop artificial nerves.