Weld Residual Stress Profiles for Structural Integrity Assessment

Economic and safe management of operating nuclear power plants can be highly dependent on the structural integrity assessments for safety critical pressure vessels and piping components. In engineering fracture assessment procedures the full (3-D) residual stress field at a welded joint is usually simplified by considering a representative onedimensional profile through the wall-thickness of the stress tensor component acting normal to the crack face. The stress intensity factor, calculated from this estimated through-thickness stress profile, is used directly in the fracture assessment. Therefore, assessments of defects in welds can be highly sensitive to the through-thickness residual stress profiles assumed in the calculations. There is a need for reliable characterisation of residual stresses in welded structures such as in stainless steel girth welded pipes as there are a lot of discrepancies in the current methodologies used. For example, bounding residual profiles found in fitness for service assessment procedures have been based on examination of residual stress measurements, finite element weld simulation and expert judgment. This approach suffers from the drawback that the upper bound curve can increase as more measurements and data scatter are obtained. The consequence of this is that structural integrity assessments of defective plant can be over-conservative by a large margin, and may lead to unnecessary and costly repair or inspection. This thesis illustrates how a neural network model, can be developed and applied to predict through-thickness residual stress profiles in austenitic stainless steel pipe girth welds for simplified fracture assessments. The model is validated by comparing predictions with new experimental measurements made using neutron diffraction and contour method. The new measurements were undertaken by fabricating six pipe girth welds with a range of wall-thickness, weld heat input and weld groove geometries. The robustness of the developed artificial neural network (ANN) approach is demonstrated by sensitivity studies in input variables and training data. The performance and suitability of the ANN approach is discussed by comparison with stress profiles recommended in defect assessment procedures. This is followed by an evaluation of whether the use of neural network bounding profiles can lead to non-conservative estimates of stress intensity factor in fracture assessments. The neural network approach shows sufficient potential to be developed into an alternative prediction tool for use in fracture assessment of welded components

Analysis of Surface Roughness Influence in Non-Destructive Magnetic Measurements Applied to Reactor Pressure Vessel Steels

The influence of surface roughness on magnetic measurements of Reactor Pressure Vessel Steels was investigated by applying two types of magnetic, non-destructive measurement on nuclear reactor pressure vessel steel samples: magnetic adaptive testing (MAT) and magnetic Barkhausen noise measurement (MBN). The surface roughness was modified by primary and secondary machine cutting forces. Different settings of machine cutting produced different surface conditions. It was found that for both measurements a monotonic correlation was found to exist between magnetic parameters and surface roughness. Results of the MAT measurements found that the correlation depends on the speed (i.e., on the applied slew rate) of the magnetizing current. In a similar fashion, results from the MBN method show good agreement with MAT, where the response diminishes with an increase in surface roughness. The results show the importance of accounting for surface condition in the interpretation of results of non-destructive magnetic testing

Machine-Learning Approach to Determine Surface Quality on a Reactor Pressure Vessel (RPV) Steel

Surface quality measures such as roughness, and especially its uncertain character, affect most magnetic non-destructive testing methods and limits their performance in terms of an achievable signal-to-noise ratio and reliability. This paper is primarily focused on an experimental study targeting nuclear reactor materials manufactured from the milling process with various machining parameters to produce varying surface quality conditions to mimic the varying material surface qualities of in-field conditions. From energising a local area electromagnetically, a receiver coil is used to obtain the emitted Barkhausen noise, from which the condition of the material surface can be inspected. Investigations were carried out with the support of machine-learning algorithms, such as Neural Networks (NN) and Classification and Regression Trees (CART), to identify the differences in surface quality. Another challenge often faced is undertaking an analysis with limited experimental data. Other non-destructive methods such as Magnetic Adaptive Testing (MAT) were used to provide data imputation for missing data using other intelligent algorithms. For data reinforcement, data augmentation was used. With more data the problem of ‘the curse of data dimensionality’ is addressed. It demonstrated how both data imputation and augmentation can improve measurement datasets

A comparison of machine learning methods to classify radioactive elements using prompt-gamma-ray neutron activation data

Abstract The detection of illicit radiological materials is critical to establishing a robust second line of defence in nuclear security. Neutron-capture prompt-gamma activation analysis (PGAA) can be used to detect multiple radioactive materials across the entire Periodic Table. However, long detection times and a high rate of false positives pose a significant hindrance in the deployment of PGAA-based systems to identify the presence of illicit substances in nuclear forensics. In the present work, six different machine-learning algorithms were developed to classify radioactive elements based on the PGAA energy spectra. The model performance was evaluated using standard classification metrics and trend curves with an emphasis on comparing the effectiveness of algorithms that are best suited for classifying imbalanced datasets. We analyse the classification performance based on Precision, Recall, F1-score, Specificity, Confusion matrix, ROC-AUC curves, and Geometric Mean Score (GMS) measures. The tree-based algorithms (Decision Trees, Random Forest and AdaBoost) have consistently outperformed Support Vector Machine and K-Nearest Neighbours. Based on the results presented, AdaBoost is the preferred classifier to analyse data containing PGAA spectral information due to the high recall and minimal false negatives reported in the minority class