Prediction of reheat cracking behaviour in a service exposed 316H steam header

Reheat cracking in an ex-service Type 316H stainless steel steam header component has been investigated in this study. The examined steam header was in service for 87,790" role="presentation" style="display: inline; line-height: normal; word-spacing: normal; overflow-wrap: normal; white-space: nowrap; float: none; direction: ltr; max-width: none; max-height: none; min-width: 0px; min-height: 0px; border-width: 0px; border-style: initial; position: relative;">h and the cracks in this component were found in the vicinity of the weld toe. The root cause of this type of failure was due to the welding residual stresses. The welding-induced residual stresses had been present in the header at the early stage of the operation and were released during service. In this paper, a novel technique has been proposed to simulate the residual stress distribution normal to the crack direction by applying remote fixed displacement boundary conditions in an axisymmetric model. This approach can simulate the presence of residual stresses in actual components without the need to develop full weld simulation to quantify them. The predicted residual stress levels and distributions normal to the crack direction have been found in good agreement with the measured residual stresses available in the literature for a similar header. The creep crack growth (CCG) rates have been characterized using the fracture mechanics C∗" role="presentation" style="display: inline; line-height: normal; word-spacing: normal; overflow-wrap: normal; white-space: nowrap; float: none; direction: ltr; max-width: none; max-height: none; min-width: 0px; min-height: 0px; border-width: 0px; border-style: initial; position: relative;">C∗C∗ parameter and estimated using predictive models

Determination of long-term creep properties for 316H steel using short-term tests on pre-strained material

Determination of long-term creep rupture properties for 316H steel is both costly and time-consuming and given the level of scatter in the data would need substantial number of tests to be performed. The primary objective of this study is to estimate the long-term creep properties of cross-weld (XW) and as-received (AR) 316H stainless steel by performing accelerated tests on pre-compressed (PC) material. In this work, uniaxial creep rupture tests have been performed on XW specimens and the results have been used to establish a correlation with accelerated test results on the PC material. Moreover, tensile tests have been performed on XW specimens at room temperature and 550 °C to examine the pre-conditioning effects on the mechanical response of the material. Similar power-law creep properties have been found for the creep strain rate and rupture time behaviour of the XW and PC specimens. It also has been found that the creep ductility data points obtained from XW and PC specimens fall upon the estimated trend for the AR material at 550 °C when the data are correlated with the applied stress normalised by 0.2% proof stress. The results show that the long-term creep properties of the XW and AR material can be estimated in much shorter time scales simply by performing tests on the PC material state

DUAL-GLOW: Conditional Flow-Based Generative Model for Modality Transfer

Positron emission tomography (PET) imaging is an imaging modality for diagnosing a number of neurological diseases. In contrast to Magnetic Resonance Imaging (MRI), PET is costly and involves injecting a radioactive substance into the patient. Motivated by developments in modality transfer in vision, we study the generation of certain types of PET images from MRI data. We derive new flow-based generative models which we show perform well in this small sample size regime (much smaller than dataset sizes available in standard vision tasks). Our formulation, DUAL-GLOW, is based on two invertible networks and a relation network that maps the latent spaces to each other. We discuss how given the prior distribution, learning the conditional distribution of PET given the MRI image reduces to obtaining the conditional distribution between the two latent codes w.r.t. the two image types. We also extend our framework to leverage 'side' information (or attributes) when available. By controlling the PET generation through 'conditioning' on age, our model is also able to capture brain FDG-PET (hypometabolism) changes, as a function of age. We present experiments on the Alzheimers Disease Neuroimaging Initiative (ADNI) dataset with 826 subjects, and obtain good performance in PET image synthesis, qualitatively and quantitatively better than recent works

A continuum damage approach for predicting creep crack growth failures in components containing residual stresses

Components in advanced gas cooled reactor (AGR) operating at elevated temperatures in the range of 500-650°C are typically susceptible to the initiation and growth of cracks due to creep. Type 316H stainless steel steam headers after a long term service are susceptible to reheat cracking in the vicinity of the weld driven by the presence of welding residual stresses. For this reason, this research has focused on developing pragmatic numerical methods for predicting creep crack growth behaviour of welded components containing residual stresses, using a simplified continuum damage and fracture mechanics method. The work presented had three main aims. The first was to derive a comprehensive set of plastic η factors for standard fracture mechanics geometries containing welds. The impetus for this was to improve material crack growth characterisation for welds by improving the creep C* solutions for these geometries that are presently recommended in standard codes of practice for creep crack growth testing. The second part was to experimentally examine appropriate creep material properties for as-received and service-exposed 316H stainless steels containing welds for use in numerical modelling and predictive methods for creep crack growth in a real component. The third was to develop and validate a simplified method of simulating residual stresses and creep crack growth behaviour in an ex-service AISI 316H weld header with reheat cracking. This approach simulates the presence of residual stresses using appropriate loading and boundary conditions in actual components that undergo reheat cracking without the need to develop full weld simulations to quantify them. The creep crack growth behaviour was studied using two methods based on the theories of fracture mechanics and continuum damage mechanics. Fracture mechanics parameter C* was firstly used to examine the approximate crack growth rate using the reference stress approach and approximate NSW model. The second method was to predict long term cracking by using a simplified continuum damage mechanics model, with a consideration of stress relaxation. For this purpose, a simplified multi-axial ductility exhaustion model was developed and implemented in an Abaqus user subroutine, taking into account the changes in the ex-service creep properties and the effect of reduction in creep ductility under low loads and long term operation at service temperatures. Resulting from the findings, the task was to identify the geometric and the material reasons of how and why the crack growth follows a path of least resistance and higher constraint which did not necessarily mean growing through the welds or the heat affected zone region.Open Acces

Near-weld creep crack growth behaviour in type 316H steel ex-service components

Creep crack growth is known to be the dominant failure mechanism in high-temperature components. Particularly in welded structures operating at elevated temperatures, cracks are often found to initiate and propagate in the vicinity of the weld region which can eventually penetrate into the base material after a long period of operation. In this study, creep crack growth tests have been performed on specimens extracted from an ex-service 316 H welded component to examine the crack initiation and growth behaviour in near-weld regions. The results show that the cracking behaviour of the base metal in near-weld specimens is similar to the as-received 316 H data set, suggesting that the material inhomogeneity would not influence the crack propagation behaviour in service-exposed components. Moreover, the test results show that the crack initiation and growth behaviour of the HAZ specimens can be estimated in much shorter time scales by performing tests on pre-compressed material

Gstring: A novel approach for efficient search in graph databases

Graphs are widely used for modeling complicated data, including chemical compounds, protein interactions, XML documents, and multimedia. Information retrieval against such data can be formulated as a graph search problem, and finding an efficient solution to the problem is essential for many applications. A popular approach is to represent both graphs and queries on graphs by sequences, thus converting graph search to subsequence matching. State-of-the-art sequencing methods work at the finest granularity – each node (or edge) in the graph will appear as an element in the resulting sequence. Clearly, such methods are not semantic conscious, and the resulting sequences are not only bulky but also prone to complexities arising from graph isomorphism and other problems in searching. In this paper, we introduce a novel sequencing method to capture the semantics of the underlying graph data. We find meaningful components in graph structures and use them as the most basic units in sequencing. It not only reduces the size of resulting sequences, but also enables semantic-based searching. In this paper, we base our approach on chemical compound databases, although it can be applied to searching other complicated graphs, such as protein structures. Experiments demonstrate that our approach outperforms state-ofthe-art graph search methods. 1

Accurate image capturing control of bottle caps based on iterative learning control and Kalman filtering

This paper investigates camera control for capturing bottle cap target images in the fault-detection system of an industrial production line. The main purpose is to identify the targeted bottle caps accurately in real time from the images. This is achieved by combining iterative learning control and Kalman filtering to reduce the effect of various disturbances introduced into the detection system. A mathematical model, together with a physical simulation platform is established based on the actual production requirements, and the convergence properties of the model are analyzed. It is shown that the proposed method enables accurate real-time control of the camera, and further, the gain range of the learning rule is also obtained. The numerical simulation and experimental results confirm that the proposed method can not only reduce the effect of repeatable disturbances but also non-repeatable ones. </jats:p

Live line strain clamp's DR image anomaly detection based on unsupervised learning

Abstract Due to the high‐risk working environment of high‐voltage transmission lines, defect samples of strain clamps cannot be fully and completely collected. As a result, the deep learning method based on defect sample tags cannot effectively identify all abnormalities. To solve this problem, an unsupervised anomaly detection method based on knowledge distillation is proposed, which only requires a small number of normal samples to drive the model for anomaly detection. ResNet is the framework of the teacherstudent model, and the feature activation layer after ResBlock is used for knowledge transfer. Residual‐assisted attention and pyramid‐splitting attention were used to enhance the spatial perception and multi‐scale information utilization ability of the model. This model only transmits the information of normal samples and is sensitive to abnormal samples. The proposed model outperformed the baseline by 23% and individual categories by 78% on the MVTec AD (Anomaly Detection Dataset) and outperformed the baseline by 45% and individual categories by 10% on the CIFAR10 and is also reliable for Mnist and Fashion Mnist. This method performs best (82.71%) over the existing method on the self‐built data set

Design and Modeling of a Test Bench for Dual-Motor Electric Drive Tracked Vehicles Based on a Dynamic Load Emulation Method

Dual-motor Electric Drive Tracked Vehicles (DDTVs) have attracted increasing attention due to their high transmission efficiency and economical fuel consumption. A test bench for the development and validation of new DDTV technologies is necessary and urgent. How to load the vehicle on a DDTV test bench exactly the same as on a real road is a crucial issue when designing the bench. This paper proposes a novel dynamic load emulation method to address this problem. The method adopts dual dynamometers to simulate both the road load and the inertia load that are imposed on the dual independent drive systems. The vehicle&rsquo;s total inertia equivalent to the drive wheels is calculated with separate consideration of vehicle body, tracks and road wheels to obtain a more accurate inertia load. A speed tracking control strategy with feedforward compensation is implemented to control the dual dynamometers, so as to make the real-time dynamic load emulation possible. Additionally, a MATLAB/Simulink model of the test bench is built based on a dynamics analysis of the platform. Experiments are finally carried out on this test bench under different test conditions. The outcomes show that the proposed load emulation method is effective, and has good robustness and adaptability to complex driving conditions. Besides, the accuracy of the established test bench model is also demonstrated by comparing the results obtained from the simulation model and experiments