31 research outputs found

    Advanced eddy current test signal analysis for steam generator tube defect classification and characterization

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    Eddy Current Testing (ECT) is a Non-Destructive Examination (NDE) technique that is widely used in power generating plants (both nuclear and fossil) to test the integrity of heat exchanger (HX) and steam generator (SG) tubing. Specifically for this research, laboratory-generated, flawed tubing data were examined The tubing data were acquired from the EPRI NDE Center, Charlotte, NC. The data are catalogued in the Performance Demonstration Database (POD) which is used as a training manual for certification. The specific subset of the data used in this dissertation has an Examination Technique Specification Sheet (ETSS) and a blueprint of the flawed tube specimens. The purpose of this dissertation is to develop and implement an automated method for the classification and an advanced characterization of defects in HX and SG tubing. These two improvements enhanced the robustness of characterization as compared to traditional bobbin-coil ECT data analysis methods. A more robust classification and characterization of the tube flaw insitu (while the SG is on-line but not when the plant is operating), should provide valuable information to the power industry. The following is a summary of the original contributions of this dissertation research. 1. Development of a feature extraction program acquiring relevant information from both the mixed, absolute and differential ECTD Flaw Signal (ECTDFS). 2. Application of the Continuous Wavelet Transformation (CWT) to extract more information from the mixed, complex differential ECTDFS. 3. Utilization of Image Processing (IP) techniques to extract the information contained in the generated CWT. 4. Classification of the ECTDFSs, using the compressed feature vector and a Bayes classification system. 5. Development of an upper bound for the probability of classification error, using the Bhattacharyya distance, for the Bayesian classification. 6. Tube defect characterization based on the classified flaw-type to enhance characterization 7. Development of a diagnostic software system EddyC and user\u27s guide. The important results of the application of the method are listed. The CWT contains at least enough information to correctly classify the flaws 64% of the time using the IP features. The Bayes classification system, using only the CWT generated features (after PCA compression), correctly identified 64% of the ECTD flaws. The Bayes classification system correctly identified 7 5% of the ECTD flaws using cross validation utilizing all the generated features after PCA compression. Initial template matching results (from the PDD database) yielded correct classification of 69%. The B-distances parallel and bound the percent misclassified cases. The calculated B-distance for 15 PCs were O and 14.22% bounding the 1.1% incorrectly classified. But, these Gaussian-based calculated B-distances may be inaccurate due to non-Gaussian features. The number of outliers seems to have an inverse relationship with the number of misclassifications. Characterization yielded an average error of 12.76 %. This excluded the results from flaw-type 1 (Thinning). The following are the conclusions reached from this research. A feature extraction program acquiring relevant information from both the mixed, absolute and differential data was successfully implemented. The CWT was utilized to extract more information from the mixed, complex differential data. Image Processing techniques used to extract the information contained in the generated CWT, classified the data with a high success rate. The data were accurately classified, utilizing the compressed feature vector and using a Bayes classification system. An estimation of the upper bound for the probability of error, using the Bhattacharyya distance, was successfully applied to the Bayesian classification. The classified data were separated according to flaw-type (classification) to enhance characterization. The characterization routine used dedicated, flaw-type specific ANNs that made the characterization of the tube flaw more robust. The inclusion of outliers may help complete the feature space so that classification accuracy is increased. Given that the eddy current test signals appear very similar, there may not be sufficient information to make an extremely accurate (\u3e 95%) classification or an advanced characterization using this system. It is necessary to have a larger database fore more accurate system learning

    Faculty Publications and Creative Works 2004

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    Faculty Publications & Creative Works is an annual compendium of scholarly and creative activities of University of New Mexico faculty during the noted calendar year. Published by the Office of the Vice President for Research and Economic Development, it serves to illustrate the robust and active intellectual pursuits conducted by the faculty in support of teaching and research at UNM

    INTER-ENG 2020

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    These proceedings contain research papers that were accepted for presentation at the 14th International Conference Inter-Eng 2020 ,Interdisciplinarity in Engineering, which was held on 8–9 October 2020, in Târgu Mureș, Romania. It is a leading international professional and scientific forum for engineers and scientists to present research works, contributions, and recent developments, as well as current practices in engineering, which is falling into a tradition of important scientific events occurring at Faculty of Engineering and Information Technology in the George Emil Palade University of Medicine, Pharmacy Science, and Technology of Târgu Mures, Romania. The Inter-Eng conference started from the observation that in the 21st century, the era of high technology, without new approaches in research, we cannot speak of a harmonious society. The theme of the conference, proposing a new approach related to Industry 4.0, was the development of a new generation of smart factories based on the manufacturing and assembly process digitalization, related to advanced manufacturing technology, lean manufacturing, sustainable manufacturing, additive manufacturing, and manufacturing tools and equipment. The conference slogan was “Europe’s future is digital: a broad vision of the Industry 4.0 concept beyond direct manufacturing in the company”

    Advances in Binders for Construction Materials

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    The global binder production for construction materials is approximately 7.5 billion tons per year, contributing ~6% to the global anthropogenic atmospheric CO2 emissions. Reducing this carbon footprint is a key aim of the construction industry, and current research focuses on developing new innovative ways to attain more sustainable binders and concrete/mortars as a real alternative to the current global demand for Portland cement.With this aim, several potential alternative binders are currently being investigated by scientists worldwide, based on calcium aluminate cement, calcium sulfoaluminate cement, alkali-activated binders, calcined clay limestone cements, nanomaterials, or supersulfated cements. This Special Issue presents contributions that address research and practical advances in i) alternative binder manufacturing processes; ii) chemical, microstructural, and structural characterization of unhydrated binders and of hydrated systems; iii) the properties and modelling of concrete and mortars; iv) applications and durability of concrete and mortars; and v) the conservation and repair of historic concrete/mortar structures using alternative binders.We believe this Special Issue will be of high interest in the binder industry and construction community, based upon the novelty and quality of the results and the real potential application of the findings to the practice and industry

    Neural Network Potential Simulations of Copper Supported on Zinc Oxide Surfaces

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    Heterogeneous catalysis is an area of active research, because many industrially relevant reactions involve gaseous reactants and are accelerated by solid phase catalysts. In recent years, activity in the field has become more intense due to the development of surface science and simulation techniques that allow for acquiring deeper insight into these catalysts, with the goal of producing more active, cheaper and less toxic catalytic materials. One particularly crucial case study for heterogeneous catalysis is the synthesis of methanol from synthesis gas, composed of H2, CO and CO2. The reaction is catalyzed by a mixture of Cu and ZnO nanoparticles with Al2O3 as a support material. This process is important not only due to methanol’s many uses as a solvent, raw material for organic synthesis, and possible energy and carbon capture material, but also as an example for many other metal/metal oxide catalysts. A plethora of experimental studies are available for this catalyst, as well as for simpler model systems of Cu clusters supported on ZnO surfaces. Unfortunately, there is still a lack of theoretical studies that can support these experi- mental results by providing an atom-by-atom representation of the system. This scarcity of atomic level simulations is due to the absence of fast but ab-initio level accurate potentials that would allow for reaching larger systems and longer simulated time scales. A promising possibility to bridge this gap in potentials is the rise of machine learning potentials, which utilize the tools of machine learning to reproduce the potential energy surface of a system under study, as sampled by an expensive electronic structure reference method of choice. One early and fruitful example of such machine learning force fields are neural network potentials, as initially developed by Behler and Parrinello. In this thesis, a neural network potential of the Behler-Parrinello type has been constructed for ternary Cu/Zn/O systems, focusing on supported Cu clusters on the ZnO(10-10) surface, as a model for the industrial catalyst. This potential was subsequently utilized to perform a number of simulations. Small supported Cu clusters between 4 and 10 atoms were optimized with a genetic algorithm, and a number of structural trends observed. These clusters revealed the first hints of the structure of the Cu/ZnO interface, where Cu prefers to interact with the support through configurations in the continuum between Cu(110) and Cu(111). Simulated annealing runs for Cu clusters between 200 and 500 atoms reinforced this observation, with these larger clusters also adopting this sort of interface with the support. Additionally, in these simulations the effect of strain induced by the support can be observed, with deviations from ideal lattice constants reaching the top of all of the clusters. To further investigate the influence of strain in this system, large coincident surfaces of Cu were deposited on ZnO supports. Due to the lattice mismatch present between the two materials, this requires straining the Cu overlayer. This analysis confirmed once again that Cu(110) and Cu(111) are the most stable surfaces when de- posited on ZnO(10-10). During this thesis a number of new algorithm and programs were developed. Of particular interest is the bin and hash algorithm, which was designed to aid in the construction and curating of reference sets for the neural network potential, and can also be used to evaluate the quality of atomic descriptor sets.2021-10-0

    Mining Safety and Sustainability I

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    Safety and sustainability are becoming ever bigger challenges for the mining industry with the increasing depth of mining. It is of great significance to reduce the disaster risk of mining accidents, enhance the safety of mining operations, and improve the efficiency and sustainability of development of mineral resource. This book provides a platform to present new research and recent advances in the safety and sustainability of mining. More specifically, Mining Safety and Sustainability presents recent theoretical and experimental studies with a focus on safety mining, green mining, intelligent mining and mines, sustainable development, risk management of mines, ecological restoration of mines, mining methods and technologies, and damage monitoring and prediction. It will be further helpful to provide theoretical support and technical support for guiding the normative, green, safe, and sustainable development of the mining industry

    Entwicklung von Einmalsensoren zur schnellen Multianalytdetektion: Acteylcholinesterase- und mikrobielle Biosensoren

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    Der Gegenstand dieser Arbeit war die Entwicklung von neuen Biosensoren zum simultanen Nachweis und der Klassifizierung von Stoffgemischen in der Umweltanalytik. Diese neuartigen Systeme sollten zur Detektion Acetylcholinesterase hemmender Insektizide aus der Gruppe der Organophosphate und Carbamate sowie halogenorganischer Stoffe eingesetzt werden. Diese Substanzen spielen insbesondere in der Überwachung der Wasserqualität eine entscheidende Rolle. Das Sensorprinzip basierte auf der Idee, biologische Rezeptorkomponenten, die unterschiedliche Spezifität für die Zielananlyten aufweisen, zu kombinieren, und deren Signalmuster durch künstliche neuronale Netze (KNN) auszuwerten. Alle entwickelten Systeme verwendeten durch Siebdruck hergestellte Dickschicht-Multielektroden als Sensorbasis. Zur Mustererkennung von Insektiziden wurden Biosensoren eingesetzt, welche Acetylcholinesterase-Varianten variierender Selektivität enthielten. Die Detektion halogenorganischer Verbindungen in Mischungen erfolgte mittels Sensoren, die mit Ralstonia eutropha JMP 134 Zellen bestückt waren. Die verschiedenen Selektivitäten dieser Sensoren wurden durch Kultivierung der Mikroorganismen auf unterschiedlichen Wachstumssubstraten erreicht.The objective of this work was to develop new biosensors for multianalyte detection. Target analytes were cholinesterase inhibiting insecticides and chlorinated aromatic hydrocarbons. The task was fulfilled by a combination of biological receptors bearing different selectivities for the desired analytes and data evaluation by feed-forward artificial neural networks (ANN). The basic sensor design consisted of a four-electrode thick film electrode which was fabricated by screen printing. As biological receptors for insecticide detection, variants of acetylcholinesterase (AChE) and for chlorinated aromatics Ralstonia eutropha JMP 134 cells were selected
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