The performance of the Acoustic Emission (AE) technique was investigated to establish its reliability in detecting and locating fatigue crack damage as well as distinguishing between different AE sources in potential SHM applications. Experiments were conducted to monitor the AE signals generated during fatigue crack growth in coupon 2014 T6 aluminium. The influence of stress ratio, stress range, sample geometry and whether or not the load spectrum was of constant or variable amplitude were all investigated. Timing filters were incorporated to eliminate extraneous AE signals produced from sources other than the fatigue crack. AE signals detected were correlated with values of applied cyclic load throughout the tests. Measurements of Time difference of arrival were taken for assessment of errors in location estimates obtained using time of flight algorithms with a 1D location setup. It was found that there was significant variability in AE Hit rates in otherwise identical samples and test conditions. However common trends characteristic of all samples could be observed. At the onset of crack growth high AE Hit rates were observed for the first few millimetres after which they rapidly declined to minimal values for an extended period of crack growth. Another peak and then decline in AE Hit rates was observed for subsequent crack growth before yet another increase as the sample approached final failure. The changes in AE signals with applied cyclic load provided great insights into the different AE processes occurring during crack growth. AE signals were seen to occur in the lower two-thirds of the maximum load in the first few millimetres of crack growth before occurring at progressively smaller values as the crack length increased. These emissions could be associated with crack closure. A separate set of AE signals were observed close to the maximum cyclic stress throughout the entire crack growth process. At the failure crack length AE signals were generated across the entire loading range. Novel metrics were developed to statistically characterise variability of AE generation with crack growth and at particular crack lengths across different samples. A novel approach for fatigue crack length estimation was developed based on monitoring applied loads to the sample corresponding with generated AE signals which extends the functionality of the AE technique in an area which was previously deficient. It is however limited by its sensitivity to changes in sample geometry. Experiments were also performed to validate the performance of the AE technique in detecting and locating fatigue crack in a representative wing-box structure. An acousto-ultrasonic method was used to calibrate the AE wave velocity in the structure which was used to successfully locate the 'hidden' fatigue crack. A novel observation was made in the series of tests conducted where the complex propagation paths in the structure could be exploited to perform wide area sensing coverage in certain regions using sensors mounted on different components of the structure. This also extends current knowledge on the capability of the AE technique.

No description available.

abstract: Non-Destructive Testing (NDT) is a branch of scientific methods and techniques used to evaluate the defects and irregularities in engineering materials. These methods conduct testing without destroying or altering material’s structure and functionality. Most of these defects are subsurface making them difficult to detect and access. SONIC INFRARED (IR) is a relatively new and emerging vibrothermography method under the category of NDT methods. This is a fast NDT inspection method that uses an ultrasonic generator to pass an ultrasonic pulse through the test specimen which results in a temperature variation in the test specimen. The temperature increase around the area of the defect is more because of frictional heating due to the vibration of the specimen. This temperature variation can be observed using a thermal camera. In this research study, the temperature variation in the composite laminate during the SONIC IR experimentation using an infrared thermal camera. These recorded data are used to determine the location, dimension and depth of defects through SONIC IR NDT method using existing defect detection algorithms. Probability of detection analysis is used to determine the probability of detection under specific experimental conditions for two different types of composite laminates. Lastly, the effect of the process parameters such as number of pulses, pulse duration and time delay between pulses of this technique on the detectability and probability of detection is studied in detail. / Dissertation/Thesis / Masters Thesis Mechanical Engineering 2019

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No description available.

Three spined, mussel species occur in the United States along the Atlantic slope; James spinymussel (Pleurobema collina), Tar spinymussel (Elliptio steinstansana), and Altamaha spinymussel (E. spinosa). The James spinymussel was listed as endangered in 1988, and was until recently considered to be endemic to the James River basin (Clarke and Neves 1984; USFWS 1990). Biologists with the North Carolina Department of Transportation (NCDOT) discovered spinymussel populations in the Dan and Mayo rivers in NC in 2000 and 2001, respectively. The U.S. Fish & Wildlife Service (USFWS) tentatively identified this species as Pleurobema collina. My project proposed by the Virginia Cooperative Fish and Wildlife Research Unit to the USFWS and the Virginia Transportation Research Council, determined where P. collina resides in VA and what the extent of its range is within the state. An informal preliminary survey design for P. collina was used during the summer of 2002 and simple random sampling was deployed in 2003-2004 surveys to provide a good basis for comparison to gauge the efficiency of the informal sampling design. In 2002, a total of 116 person-hours were spent surveying 39 localities on the Mayo, Dan, and Smith rivers. A total of 96 P. collina was observed in the South Fork of the Mayo River, Patrick and Henry counties, VA. A documented range of 24 rkm was established in the South Fork Mayo River. During the summers of 2003 and 2004, a total of 228 person-hours were spent surveying 38 equal-area river reaches (10, 000 m2) on the mainstems of the Dan, Smith, South Mayo, and Banister rivers. No specimens of P. collina (live or relic shells) were detected. A simple random sampling approach was designed to be easy, relatively quick and cost effective, applicable to most rivers, and to provide actual numbers for comparison. Negative results were only reported after 6 person-hours of searching within each randomly selected, equal-area river reach had been expended. P. collina was declared absent from the VA random sites surveyed in 2003-2004 with a confidence of ~90%. A genetic characterization of four extant populations of P. collina was conducted to assess its taxonomic affinity and to resolve conservation issues related to recovery planning and management actions. The populations were examined for phenotypic variation, and were characterized phylogenetically using DNA sequences. A comprehensive analysis was performed for both separate and combined mitochondrial (357 bp of cytochrome-b, 916 bp of ND-1) and nuclear (502 bp of ITS-1) DNA sequences. Based on comprehensive molecular, morphological, and life history data, populations of P. collina sampled from the Dan River sub-drainage do not warrant separate species designation from P. collina sampled from the James River drainage. / Master of Science

<div>The space around the Earth is becoming increasingly populated with a growth in number of launches and proliferation of debris. Currently, there are around 44,000 objects (with a minimum size of 10cm) orbiting the Earth as per the data made publicly available by the US strategy command (USSTRATCOM). These objects include active satellites and debris. The number of these objects are expected to increase rapidly in future from launches by companies in the private sector. For example, SpaceX is expected to deploy around 12000 new satellites in the LEO region to develop a space-based internet communication system. Hence in order to protect active space assets, tracking of all the objects is necessary. Probabilistic tracking methods have become increasingly popular for solving the multi-target tracking problem in Space Situational Awareness (SSA). This thesis studies one such technique known as the GM-PHD filter, which is an algorithm which estimates the number of objects and its states when non-perfect measurements (noisy measurements, false alarms) are available. For Earth orbiting objects, especially those in Geostationary orbits, ground based optical sensors are a cost-efficient way to gain information.In this case, the likelihood of gaining target-generated measurements depend on the probability of detection (p_D) of the target.An accurate modeling of this quantity is essential for an efficient performance of the filter. p_D significantly depends on the amount of light reflected by the target towards the observer. The reflected light depends on the relative position of the target with respect to the Sun and the observer, the shape, size and reflectivity of the object and the relative orientation of the object towards Sun and the observer. The estimation of the area and reflective properties of the object is in general, a difficult process. Uncontrolled objects, for example, start tumbling and no information regarding the attitude motion can be obtained. In addition, the shape can change because of disintegration and erosion of the materials. For the case of controlled objects, given that the object is stable, some information on the attitude can be obtained. But materials age in space which changes the reflective properties of the materials. Also, exact shape models for these objects are rare. Moreover,, area can never be estimated with optical measurements or any other measurements, as it is always albedo-area i.e., reflectivity times area that can be measured.</div><div> The purpose of this work is to design a variation of the GM-PHD filter which accounts for the uncertainty in p_D as the original GM-PHD filter designed by Vo and Ma assumes p_D as a constant. It is validated that the proposed method improves the filter performance when there is an uncertainty in area(hence uncertainty in p_D) of the targets. In the tested cases, the uncertainty in p_D was modeled as an uncertainty in area while assuming that the targets are spherical and that the reflectivity of the targets is constant. It is seen that a model mismatch in p_D affects the filter performance significantly and the proposed method improves the performance of the filter in all cases.</div>

Degradation of components and structures is a major threat to the safety and reliability of large engineering systems, such as the railway networks or the nuclear power plants. Periodic inspection and maintenance are thus required to ensure that the system is in good condition for continued service. A key element for the optimal inspection and maintenance is to accurately model and forecast the degradation progress, such that inspection and preventive maintenance can be scheduled accordingly. In recently years, probabilistic models based on stochastic process have become increasingly popular in degradation modelling, due to their flexibility in modelling both the temporal and sample uncertainties of the degradation. However, because of the often complex structure of stochastic degradation models, accurate estimate of the model parameters can be quite difficult, especially when the inspection data are noisy or incomplete. Not considering the effect of uncertain inspection data is likely to result in biased parameter estimates and therefore erroneous predictions of future degradation. The main objective of the thesis is to develop formal methods for the parameter estimation of stochastic degradation models using uncertain inspection data. Three typical stochastic models are considered. They are the random rate model, the gamma process model and the Poisson process model, among which the random rate model and the gamma process model are used to model the flaw growth, and the Poisson process model is used to model the flaw generation. Likelihood functions of the three stochastic models given noisy or incomplete inspection data are derived, from which maximum likelihood estimates can be obtained. The thesis also investigates Bayesian inference of the stochastic degradation models. The most notable advantage of Bayesian inference over classical point estimates is its ability to incorporate background information in the estimation process, which is especially useful when inspection data are scarce. A major obstacle for accurate parameter inference of stochastic models from uncertain inspection data is the computational difficulties of the likelihood evaluation, as it often involves calculation of high dimensional integrals or large number of convolutions. To overcome the computational difficulties, a number of numerical methods are developed in the thesis. For example, for the gamma process model subject to sizing error, an efficient maximum likelihood method is developed using the Genz's transform and quasi-Monte Carlo simulation. A Markov Chain Monte Carlo simulation with sizing error as auxiliary variables is developed for the Poisson flaw generation model, A sequential Bayesian updating using approximate Bayesian computation and weighted samples is also developed for Bayesian inference of the gamma process subject to sizing error. Examples on the degradation of nuclear power plant components are presented to illustrate the use of the stochastic degradation models using practical uncertain inspection data. It is shown from the examples that the proposed methods are very effective in terms of accuracy and computational efficiency.

In wireless Communication, the need of radio spectrum increases nowadays. But in the system we are losing approximately 82-86% of spectrum most of the time due to the absence of Primary User (PU). To overcome this issue Cognitive Radio (CR) is an admirable approach. The concept of cooperative communication needs to be considering because high data rate is the demand for wireless services. Cooperative diversity in the network realized by 3-hop Decode, Amplify and Forward (DAF) and Decode and Forward (DF) and in 2-hop DF and Amplify and Forward (AF) Protocols implemented in cognitive radio communication network using Orthogonal Space Time Block Coding (OSTBC). The communication between end points is accomplished by using Multiple Input and Multiple Output (MIMO) antenna arrangement. During the Propagation, Alamouti Space Time Block Coding is used to accomplish spatial diversity and the encoded data is transmitted through Rayleigh fading channel. CR decodes the transmitted signal using Maximum Likelihood (ML) decoding method. Afterward signal broadcast toward the destination. To check the energy level of signal, energy detection technique applies at the Cognitive Controller (CC). Finally, CC will take ultimate decision for the presence of primary user if the energy level of signal is greater than predefined threshold level, it means PU is present otherwise it is absent. The main objective of this thesis is to analyze the performance of 3-hop and 2-hop communication network using relays. The performance is compared on the bases of two parameters i.e. Bit Error Rate (BER) and Probability of Detection (PD). The results are processed and validated by MATLAB simulation.

Degradation of components and structures is a major threat to the safety and reliability of large engineering systems, such as the railway networks or the nuclear power plants. Periodic inspection and maintenance are thus required to ensure that the system is in good condition for continued service. A key element for the optimal inspection and maintenance is to accurately model and forecast the degradation progress, such that inspection and preventive maintenance can be scheduled accordingly. In recently years, probabilistic models based on stochastic process have become increasingly popular in degradation modelling, due to their flexibility in modelling both the temporal and sample uncertainties of the degradation. However, because of the often complex structure of stochastic degradation models, accurate estimate of the model parameters can be quite difficult, especially when the inspection data are noisy or incomplete. Not considering the effect of uncertain inspection data is likely to result in biased parameter estimates and therefore erroneous predictions of future degradation. The main objective of the thesis is to develop formal methods for the parameter estimation of stochastic degradation models using uncertain inspection data. Three typical stochastic models are considered. They are the random rate model, the gamma process model and the Poisson process model, among which the random rate model and the gamma process model are used to model the flaw growth, and the Poisson process model is used to model the flaw generation. Likelihood functions of the three stochastic models given noisy or incomplete inspection data are derived, from which maximum likelihood estimates can be obtained. The thesis also investigates Bayesian inference of the stochastic degradation models. The most notable advantage of Bayesian inference over classical point estimates is its ability to incorporate background information in the estimation process, which is especially useful when inspection data are scarce. A major obstacle for accurate parameter inference of stochastic models from uncertain inspection data is the computational difficulties of the likelihood evaluation, as it often involves calculation of high dimensional integrals or large number of convolutions. To overcome the computational difficulties, a number of numerical methods are developed in the thesis. For example, for the gamma process model subject to sizing error, an efficient maximum likelihood method is developed using the Genz's transform and quasi-Monte Carlo simulation. A Markov Chain Monte Carlo simulation with sizing error as auxiliary variables is developed for the Poisson flaw generation model, A sequential Bayesian updating using approximate Bayesian computation and weighted samples is also developed for Bayesian inference of the gamma process subject to sizing error. Examples on the degradation of nuclear power plant components are presented to illustrate the use of the stochastic degradation models using practical uncertain inspection data. It is shown from the examples that the proposed methods are very effective in terms of accuracy and computational efficiency.