Iterative blind deconvolution and its application in characterization of eddy current NDE signals

Eddy current techniques are widely used to detect and characterize the defects in steam generator tubes in nuclear power plants. Although defect characterization is crucial for the successful inspection of defects, it is often difficult due to due to the finite size of the probes used for inspection. A feasible solution is to model the defect data as the convolution of the defect surface profile and the probe response. Therefore deconvolution algorithms can be used to remove the effect of probe on the signal. This thesis presents a method using iterative blind deconvolution algorithm based on the Richardson-Lucy algorithm to address the defect characterization problem. Another iterative blind deconvolution method based on Wiener filtering is used to compare the performance. A preprocessing algorithm is introduced to remove the noise and thus enhance the performance. Two new convergence criterions are proposed to solve the convergence problem. Different types of initial estimate of the PSF are used and their impacts on the performance are compared. The results of applying this method to the synthetic data, the calibration data and the field data are presented

A PROBABILISTIC MECHANISTIC APPROACH FOR ASSESSING THE RUPTURE FREQUENCY OF SMALL MODULAR REACTOR STEAM GENERATOR TUBES USING UNCERTAIN INPUTS FROM IN-SERVICE INSPECTIONS

One of the significant safety issues in nuclear power plants is the rupture of steam generator tubes leading to the loss of radioactive primary coolant inventory and establishment of a path that would bypass the plant's containment structure. Frequency of steam generator tube ruptures is required in probabilistic safety assessments of pressurized water reactors to determine the risks of radionuclide release. The estimation of this frequency has traditionally been based on non-homogeneous historical data that are not applicable to small modular reactors consisting of new steam generator designs. In this research a probabilistic mechanistic-based approach has been developed for assessing the frequency of steam generator tube ruptures. Physics-of-failure concept has been used to formulate mechanistic degradation models considering the underlying degradation conditions prevailing in steam generators. Uncertainties associated with unknown or partially known factors such as material properties, manufacturing methods, and model uncertainties have been characterized, and considered in the assessment of rupture frequency. An application of the tube rupture frequency assessment approach has been demonstrated for tubes of a typical helically-coiled steam generator proposed in most of the new small modular reactors. The tube rupture frequency estimated through the proposed approach is plant-specific and more representative for use in risk-informed safety assessment of small modular reactors. Information regarding the health condition of steam generator tubes from in-service inspections may be used to update the pre-service estimates of tube rupture frequency. In-service inspection data are uncertain in nature due to detection uncertainties and measurement errors associated with nondestructive evaluation methods, which if not properly accounted for, can result in over- or under-estimation of tube rupture frequency. A Bayesian probabilistic approach has been developed in this research that combines prior knowledge on defects with uncertain in-service inspection data, considering all the associated uncertainties to give a probabilistic description of the real defect size and density in the tubes. An application of the proposed Bayesian approach has been provided. Defect size and density estimated through the proposed Bayesian approach can be used to update the pre-service estimates of tube rupture frequency, in order to support risk-informed maintenance and regulatory decision-making

Index to NASA Tech Briefs, 1975

This index contains abstracts and four indexes--subject, personal author, originating Center, and Tech Brief number--for 1975 Tech Briefs

Index to 1984 NASA Tech Briefs, volume 9, numbers 1-4

Short announcements of new technology derived from the R&D activities of NASA are presented. These briefs emphasize information considered likely to be transferrable across industrial, regional, or disciplinary lines and are issued to encourage commercial application. This index for 1984 Tech B Briefs contains abstracts and four indexes: subject, personal author, originating center, and Tech Brief Number. The following areas are covered: electronic components and circuits, electronic systems, physical sciences, materials, life sciences, mechanics, machinery, fabrication technology, and mathematics and information sciences

Trans-Gaussian Kriging in a Bayesian framework : a case study

In the context of Gaussian Process Regression or Kriging, we propose a full-Bayesian solution to deal with hyperparameters of the covariance function. This solution can be extended to the Trans-Gaussian Kriging framework, which makes it possible to deal with spatial data sets that violate assumptions required for Kriging. It is shown to be both elegant and efficient. We propose an application to computer experiments in the field of nuclear safety, where it is necessary to model non-destructive testing procedures based on eddy currents to detect possible wear in steam generator tubes