On-line Structural Integrity Monitoring and Defect Diagnosis of Steam Generators Using Analysis of Guided Acoustic Waves

Integrity monitoring and flaw diagnostics of flat beams and tubular structures was investigated in this research using guided acoustic signals. The primary objective was to study the feasibility of using imbedded sensors for monitoring steam generator and heat exchanger tubing. A piezo-sensor suite was deployed to activate and collect Lamb wave signals that propagate along metallic specimens. The dispersion curves of Lamb waves along plate and tubular structures were generated through numerical analysis. Several advanced techniques were explored to extract representative features from acoustic time series. Among them, the Hilbert-Huang transform (HHT) is a recently developed technique for the analysis of non-linear and transient signals. A moving window method was introduced to generate the local peak characters from acoustic time series, and a zooming window technique was developed to localize the structural flaws. The dissertation presents the background of the analysis of acoustic signals acquired from piezo-electric transducers for structural defect monitoring. A comparison of the use of time-frequency techniques, including the Hilbert-Huang transform, is presented. It also presents the theoretical study of Lamb wave propagation in flat beams and tubular structures, and the need for mode separation in order to effectively perform defect diagnosis. The results of an extensive experimental study of detection, location, and isolation of structural defects in flat aluminum beams and brass tubes are presented. The time-frequency analysis and pattern recognition techniques were combined for classifying structural defects in brass tubes. Several types of flaws in brass tubes were tested, both in the air and in water. The techniques also proved to be effective under background/process noise. A detailed theoretical analysis of Lamb wave propagation was performed and simulations were carried out using the finite element software system ABAQUS. This analytical study confirmed the behavior of the acoustic signals acquired from the experimental studies. The results of this research showed the feasibility of on-line detection of small structural flaws by the use of transient and nonlinear acoustic signal analysis, and its implementation by the proper design of a piezo-electric transducer suite. The techniques developed in this research would be applicable to civil structures and aerospace structures

Associations between gut microbiota and sleep: a two-sample, bidirectional Mendelian randomization study

IntroductionPrevious research has reported that the gut microbiota performs an essential role in sleep through the microbiome–gut–brain axis. However, the causal association between gut microbiota and sleep remains undetermined.MethodsWe performed a two-sample, bidirectional Mendelian randomization (MR) analysis using genome-wide association study summary data of gut microbiota and self-reported sleep traits from the MiBioGen consortium and UK Biobank to investigate causal relationships between 119 bacterial genera and seven sleep-associated traits. We calculated effect estimates by using the inverse-variance weighted (as the main method), maximum likelihood, simple model, weighted model, weighted median, and MR-Egger methods, whereas heterogeneity and pleiotropy were detected and measured by the MR pleiotropy residual sum and outlier method, Cochran’s Q statistics, and MR-Egger regression.ResultsIn forward MR analysis, inverse-variance weighted estimates concluded that the genetic forecasts of relative abundance of 42 bacterial genera had causal effects on sleep-associated traits. In the reverse MR analysis, sleep-associated traits had a causal effect on 39 bacterial genera, 13 of which overlapped with the bacterial genera in the forward MR analysis.DiscussionIn conclusion, our research indicates that gut microbiota may be involved in the regulation of sleep, and conversely, changes in sleep-associated traits may also alter the abundance of gut microbiota. These findings suggest an underlying reciprocal causal association between gut microbiota and sleep

Development of an incipient fault detection and isolation method for the steam generator system of a nuclear power plant

For a complex system such as a nuclear power plant, safe and efficient control operation requires reliable and timely information about the state of the process. Therefore, fault detection and isolation (FDI), aiming at finding abnormal sensors and field devices at early stages of degradation, is very important in monitoring the status and improving the overall productivity of nuclear power plants. Many FDI techniques have been developed in recent years. They generally involve the use of data-driven modeling, multivariate statistical analysis, stochastic estimation methods (such as the Kalman filter), applied artificial intelligence methods, and others. Most of the approaches focus on the fault detection at the device level. A large-scale system, such as a power plant, consists of distributed control loops with interaction among system level devices. This thesis considers the effect of inherent control loop feedback at the system level, nonlinear characteristics of power plant components, and fault detection and isolation both at steady state and during transient operations. The independent research presented in this thesis addresses these issues . with application to the steam generator system of a typical PWR. The new FDI algorithm is able to diagnose not only abnormal sensors and actuators, but also process anomalies under both static and transient conditions. The integrated FDI algorithm developed in this study is divided into model prediction residual generation modules and residual analysis modules. Static and temporal system characterization has been developed using the Group Method of Data Handling (GMDH) modeling method to extract information from plant measurements. The residuals from GMDH modules are analyzed using the principal component analysis (PCA) for fault classification, along with a nearest neighbor approach. Simple rules are generated to understand the behavior of process variables and control functions during normal and faulty device operations. A large database of steam generator (SG) operation has been created using a full-scope PWR simulation code developed by the North Carolina State University (NCSU). The performance of the FDI method and the associated algorithms is evaluated using test data from operation at several different power levels. Multiple model prediction residual analysis techniques have been implemented in order to increase the robustness of sensor and field device fault isolation. These include a rule-based system, directional analysis of residuals using the PCA, and nearest neighbor pattern classification. The confidence level is quantified as a function of the correspondence between the postulated fault types and the test cases. The FDI system has also been successfully implemented for monitoring other subsystem components such as the feed pump. Even though this demonstration is limited in scope, it illustrates the potential application of the developed techniques to include a larger system boundary. An independent MATLAB-based graphical user interface (GUI) module has been developed to demonstrate the implementation of FDI algorithms during static and transient FDI conditions. This demonstration provides the implementation of the FDI system in an interactive fashion. The FDI modules developed in this thesis can potentially be incorporated into current or future nuclear power plants for on-line monitoring and for making decisions about the maintenance and/or replacement of critical devices

Research on Multirobot Pursuit Task Allocation Algorithm Based on Emotional Cooperation Factor

Multirobot task allocation is a hot issue in the field of robot research. A new emotional model is used with the self-interested robot, which gives a new way to measure self-interested robots’ individual cooperative willingness in the problem of multirobot task allocation. Emotional cooperation factor is introduced into self-interested robot; it is updated based on emotional attenuation and external stimuli. Then a multirobot pursuit task allocation algorithm is proposed, which is based on emotional cooperation factor. Combined with the two-step auction algorithm recruiting team leaders and team collaborators, set up pursuit teams, and finally use certain strategies to complete the pursuit task. In order to verify the effectiveness of this algorithm, some comparing experiments have been done with the instantaneous greedy optimal auction algorithm; the results of experiments show that the total pursuit time and total team revenue can be optimized by using this algorithm

On-Line Monitoring and Diagnostics of the Integrity of Nuclear Plant Steam Generators and Heat Exchangers, Volumes 1, 2.

The overall purpose of this Nuclear Engineering Education Research (NEER) project was to integrate new, innovative, and existing technologies to develop a fault diagnostics and characterization system for nuclear plant steam generators (SG) and heat exchangers (HX). Issues related to system level degradation of SG and HX tubing, including tube fouling, performance under reduced heat transfer area, and the damage caused by stress corrosion cracking, are the important factors that influence overall plant operation, maintenance, and economic viability of nuclear power systems. The research at The University of Tennessee focused on the development of techniques for monitoring process and structural integrity of steam generators and heat exchangers. The objectives of the project were accomplished by the completion of the following tasks. All the objectives were accomplished during the project period. This report summarizes the research and development activities, results, and accomplishments during June 2001 ÃÂÃÂÃÂÃÂÃÂÃÂÃÂÃÂÃÂÃÂÃÂÃÂÃÂÃÂÃÂàSeptember 2004. ÃÂÃÂÃÂÃÂÃÂÃÂÃÂÃÂÃÂÃÂÃÂÃÂÃÂÃÂÃÂ÷ Development and testing of a high-fidelity nodal model of a U-tube steam generator (UTSG) to simulate the effects of fouling and to generate a database representing normal and degraded process conditions. Application of the group method of data handling (GMDH) method for process variable prediction. ÃÂÃÂÃÂÃÂÃÂÃÂÃÂÃÂÃÂÃÂÃÂÃÂÃÂÃÂÃÂ÷ Development of a laboratory test module to simulate particulate fouling of HX tubes and its effect on overall thermal resistance. Application of the GMDH technique to predict HX fluid temperatures, and to compare with the calculated thermal resistance. ÃÂÃÂÃÂÃÂÃÂÃÂÃÂÃÂÃÂÃÂÃÂÃÂÃÂÃÂÃÂ÷ Development of a hybrid modeling technique for process diagnosis and its evaluation using laboratory heat exchanger test data. ÃÂÃÂÃÂÃÂÃÂÃÂÃÂÃÂÃÂÃÂÃÂÃÂÃÂÃÂÃÂ÷ Development and testing of a sensor suite using piezo-electric devices for monitoring structural integrity of both flat plates (beams) and tubing. Experiments were performed in air, and in water with and without bubbly flow. ÃÂÃÂÃÂÃÂÃÂÃÂÃÂÃÂÃÂÃÂÃÂÃÂÃÂÃÂÃÂ÷ Development of advanced signal processing methods using wavelet transforms and image processing techniques for isolating flaw types. ÃÂÃÂÃÂÃÂÃÂÃÂÃÂÃÂÃÂÃÂÃÂÃÂÃÂÃÂÃÂ÷ Development and implementation of a new nonlinear and non-stationary signal processing method, called the Hilbert-Huang transform (HHT), for flaw detection and location. This is a more robust and adaptive approach compared to the wavelet transform. ÃÂÃÂÃÂÃÂÃÂÃÂÃÂÃÂÃÂÃÂÃÂÃÂÃÂÃÂÃÂ÷ Implementation of a moving-window technique in the time domain for detecting and quantifying flaw types in tubular structures. A window zooming technique was also developed for flaw location in tubes. ÃÂÃÂÃÂÃÂÃÂÃÂÃÂÃÂÃÂÃÂÃÂÃÂÃÂÃÂÃÂ÷ Theoretical study of elastic wave propagation (longitudinal and shear waves) in metallic flat plates and tubing with and without flaws. ÃÂÃÂÃÂÃÂÃÂÃÂÃÂÃÂÃÂÃÂÃÂÃÂÃÂÃÂÃÂ÷ Simulation of the Lamb wave propagation using the finite-element code ABAQUS. This enabled the verification of the experimental results. The research tasks included both analytical research and experimental studies. The experimental results helped to enhance the robustness of fault monitoring methods and to provide a systematic verification of the analytical results. The results of this research were disseminated in scientific meetings. The journal manuscript titled, "Structural Integrity Monitoring of Steam generator Tubing Using Transient Acoustic Signal Analysis," was published in IEEE Trasactions on Nuclear Science, Vol. 52, No. 1, February 2005. The new findings of this research have potential applications in aerospace and civil structures. The report contains a complete bibliography that was developed during the course of the project

Abnormalities of hippocampus and frontal lobes in heart failure patients and animal models with cognitive impairment or depression: A systematic review

Aims This systematic review aimed to study the hippocampal and frontal changes of heart failure (HF) patients and HF animal models with cognitive impairment or depression. Methods A systematic review of the literature was conducted independently by reviewers using PubMed, Web of Science, Embase, and the Cochrane Library databases. Results and conclusions 30 studies were included, involving 17 pieces of clinical research on HF patients and 13 studies of HF animal models. In HF patients, the hippocampal injuries were shown in the reduction of volume, CBF, glucose metabolism, and gray matter, which were mainly observed in the right hippocampus. The frontal damages were only in reduced gray matter and have no difference between the right and left sides. The included HF animal model studies were generalized and demonstrated the changes in inflammation and apoptosis, synaptic reduction, and neurotransmitter disorders in the hippocampus and frontal lobes. The results of HF animal model studies complemented the clinical observations by providing potential mechanistic explanations of the changes in the hippocampus and frontal lobes