scholarly journals Creep-Rupture Reliability Analysis

1985 ◽  
Vol 107 (3) ◽  
pp. 339-346 ◽  
Author(s):  
A. Peralta-Duran ◽  
P. H. Wirsching
Keyword(s):  
Master Curve ◽  
State Of The Art ◽  
Creep Rupture ◽  
Probabilistic Design ◽  
Modeling Error ◽  
Short Term ◽  
Rupture Data ◽  
Reliability Methods ◽  
Long Term Behavior

A probablistic approach to the correlation and extrapolation of creep–rupture data is presented. Time–temperature parameters (TTP) are used to correlate the data, and an analytical expression for the master curve is developed. The expression provides a simple model for the statistical distribution of strength and fits neatly into a probabilistic design format. The analysis focuses on the Larson–Miller and on the Manson–Haferd parameters, but it can be applied to any of the TTP’s. A method is developed for evaluating material dependent constants for TTP’s. It is shown that “optimized” constants can provide a significant improvement in the correlation of the data, thereby reducing modeling error. Attempts were made to quantify the performance of the proposed method in predicting long-term behavior. Bias and uncertainty in predicting long-term behavior from short-term tests were derived for several sets of data. Examples are presented which illustrate the theory and demonstrate the application of state-of-the-art reliability methods to the design of components under creep.

scholarly journals Learning from Interventions Using Hierarchical Policies for Safe Learning

2020 ◽  
Vol 34 (06) ◽  
pp. 10352-10360
Author(s):  
Jing Bi ◽  
Vikas Dhiman ◽  
Tianyou Xiao ◽  
Chenliang Xu
Keyword(s):  
Reaction Time ◽  
State Of The Art ◽  
The State ◽  
Policy Framework ◽  
Asymptotic Performance ◽  
Short Term ◽  
Learning From Demonstrations ◽  
Hierarchical Levels ◽  
Long Term Behavior

Learning from Demonstrations (LfD) via Behavior Cloning (BC) works well on multiple complex tasks. However, a limitation of the typical LfD approach is that it requires expert demonstrations for all scenarios, including those in which the algorithm is already well-trained. The recently proposed Learning from Interventions (LfI) overcomes this limitation by using an expert overseer. The expert overseer only intervenes when it suspects that an unsafe action is about to be taken. Although LfI significantly improves over LfD, the state-of-the-art LfI fails to account for delay caused by the expert's reaction time and only learns short-term behavior. We address these limitations by 1) interpolating the expert's interventions back in time, and 2) by splitting the policy into two hierarchical levels, one that generates sub-goals for the future and another that generates actions to reach those desired sub-goals. This sub-goal prediction forces the algorithm to learn long-term behavior while also being robust to the expert's reaction time. Our experiments show that LfI using sub-goals in a hierarchical policy framework trains faster and achieves better asymptotic performance than typical LfD.

Methodology of Creep Data Analysis for Advanced High Cr Ferritic Steel

2007 ◽  
Author(s):  
Kouichi Maruyama ◽  
Kyosuke Yoshimi
Keyword(s):  
Data Analysis ◽  
Rupture Life ◽  
Creep Rupture ◽  
Creep Data ◽  
Short Term ◽  
Data Set ◽  
Region Analysis ◽  
Rupture Data ◽  
Term Creep

Long term creep rupture life is usually evaluated from short term data by a time-temperature parameter (TTP) method. The apparent activation energy Q for rupture life of steels sometimes changes from a high value of short term creep to a low value of long term creep. However, the conventional TTP analyses ignore the decrease in Q, resulting in the overestimation of rupture life recognized recently in advanced high Cr ferritic steels. A multi region analysis of creep rupture data is applied to a creep data set of Gr.122 steel; in the analysis a creep rupture data is divided into several data sets so that Q value is unique in each divided data set. The multi region analysis provides the best fit to the data and the lowest value of 105 h creep rupture strength among the three ways of data analysis examined. The conventional single region analysis cannot correctly represent the data points and predicts the highest strength. A half of 0.2% proof stress could not be an appropriate boundary for dividing data to be used in the multi region analysis. In the 2001 Edition of ASME Code an F average concept has been proposed as a substitution for the safety factor of 2/3 for average rupture stress. The allowable stress of Gr.122 steel may decrease significantly when the F average concept and the multi region analysis are adopted.

A Study for Proposal of Welded Joint Strength Reduction Factors of Modified 9Cr-1Mo Steel for Japan Sodium Cooled Fast Reactor (JSFR)

2013 ◽  
Author(s):  
Takashi Wakai ◽  
Takashi Onizawa ◽  
Takehiko Kato ◽  
Shingo Date ◽  
Koichi Kikuchi ◽  
...  
Keyword(s):  
Base Metal ◽  
Creep Strength ◽  
Welded Joints ◽  
Welded Joint ◽  
Polynomial Equation ◽  
Creep Rupture ◽  
Short Term ◽  
Type Iv ◽  
Rupture Data

This paper proposes provisional welded joint strength reduction factors (WJSRF) of modified 9Cr-1Mo steel (ASME Gr.91) applicable to the structural designing of “Japan sodium cooled fast reactor (JSFR)”. In the welded joints of creep strength enhanced ferritic steels including modified 9Cr-1Mo steel, creep strength may obviously degrade especially in long-term region. This phenomenon is known as “Type-IV” damage. The authors had proposed provisional allowable stress for the welded joints made of the steel in PVP 2010 conference, taking creep strength degradation due to “Type-IV” damage into account. Available creep rupture data of the welded joints made of the steel provided by Japanese steel venders were collected. The temperature range was from 500 to 650°C. The database was analyzed by stress range partitioning method. The creep rupture data were divided into two regions of short-term and long-term and those were individually evaluated by regression analyses with Larson Miller Parameter (LMP). The difference in the creep failure mechanisms between short-term and long-term regions is taken into account in this method. Boundary between these regions was half of 0.2% proof stress of the base metal at corresponding temperature. First order polynomial equation of logarithm stress was applied. For conservativeness, allowable stress was proposed provisionally considering design factor for each region. JSME (Japan Society of Mechanical Engineers) published a revised version of the elevated temperature design code in last year. Modified 9Cr-1Mo steel was officially registered in the code as a new structural material for sodium cooled fast reactors. The creep rupture curve for the base metal of the steel was standardized by employing stress range partitioning method, same as for the welded joint. However, second order polynomial equation of logarithm stress was applied in the analysis for the base metal. In addition, the creep rupture data obtained at 700°C were included in the database and data ruptured in very short term, i.e. smaller than 100 hours, were excluded from the analysis. Thus, there are some differences between the procedures to determine the creep rupture curves for base metal and welded joint made of modified 9Cr-1Mo steel. This paper discusses the most feasible procedure to determine the creep rupture curve of the welded joint of the steel by performing some case studies to focus on physical adequacy and harmonization with the determination procedure of the creep rupture curve for the base metal. Then, the WJSRF are provisionally proposed based on the design creep rupture stress intensities. In addition, the design of JSFR pipes was reviewed taking WJSRF into account.

Influence of Data Analysis Method and Allowable Stress Criterion on Allowable Stress of Gr.122 Heat Resistant Steel

2006 ◽  
Vol 129 (3) ◽  
pp. 449-453 ◽  
Author(s):  
Kouichi Maruyama ◽  
Kyosuke Yoshimi
Keyword(s):  
Data Analysis ◽  
Rupture Strength ◽  
Rupture Life ◽  
Creep Rupture ◽  
Allowable Stress ◽  
Short Term ◽  
Rupture Data ◽  
Asme Code ◽  
Term Creep

Long-term creep rupture life is usually evaluated from short-term data by a time-temperature parameter (TTP) method. The allowable stress of Gr.122 steel listed in the ASME code has been evaluated by this method and is recognized to be overestimated. The objective of the present study is to understand the causes of the overestimation and propose appropriate methodology for avoiding the overestimation. The apparent activation energy Q for rupture life of the steel changes from a high value of short-term creep to a low value of long-term creep. However, the decrease in Q is ignored in the conventional TTP analyses, resulting in the overestimation of rupture life. A multiregion analysis of creep rupture data is employed to avoid the overestimation; in the analysis creep rupture data are divided into a couple of regions so that the Q value is unique in each divided region. The multiregion analysis provides a good fit to the data and the lowest value of 105h creep rupture strength among the three ways of data analysis examined. A half of 0.2% proof stress cannot provide an appropriate boundary for dividing data to be used in the multiregion analysis. In the 2001 edition of the ASME code an F average concept has been proposed as a substitution for the safety factor of 2∕3 for average rupture stress. The allowable stress of Gr.122 steel changes significantly depending on the allowable stress criteria as well as the methods of rupture data analysis: i.e., from 74MPato48MPa.

Prediction of Long Term Stress Rupture Data for 2124

2006 ◽  
Vol 519-521 ◽  
pp. 1041-1046 ◽  
Author(s):  
Brian Wilshire ◽  
H. Burt ◽  
N.P. Lavery
Keyword(s):  
Q Methodology ◽  
Fracture Behavior ◽  
Stress Rupture ◽  
Creep Data ◽  
Short Term ◽  
Rupture Data ◽  
Term Creep ◽  
Term Data ◽  
Short Term Creep

The standard power law approaches widely used to describe creep and creep fracture behavior have not led to theories capable of predicting long-term data. Similarly, traditional parametric methods for property rationalization also have limited predictive capabilities. In contrast, quantifying the shapes of short-term creep curves using the q methodology introduces several physically-meaningful procedures for creep data rationalization and prediction, which allow straightforward estimation of the 100,000 hour stress rupture values for the aluminum alloy, 2124.

scholarly journals (M,β)-Stability of Positive Linear Systems

2016 ◽  
Vol 2016 ◽  
pp. 1-11 ◽  
Author(s):  
Octavian Pastravanu ◽  
Mihaela-Hanako Matcovschi
Keyword(s):  
Linear System ◽  
Linear Systems ◽  
Transient Behavior ◽  
Short Term ◽  
Time Dynamics ◽  
The Right ◽  
Long Term Behavior ◽  
Stability Concept

The main purpose of this work is to show that the Perron-Frobenius eigenstructure of a positive linear system is involved not only in the characterization of long-term behavior (for which well-known results are available) but also in the characterization of short-term or transient behavior. We address the analysis of the short-term behavior by the help of the “(M,β)-stability” concept introduced in literature for general classes of dynamics. Our paper exploits this concept relative to Hölder vectorp-norms,1≤p≤∞, adequately weighted by scaling operators, focusing on positive linear systems. Given an asymptotically stable positive linear system, for each1≤p≤∞, we prove the existence of a scaling operator (built from the right and left Perron-Frobenius eigenvectors, with concrete expressions depending onp) that ensures the best possible values for the parametersMandβ, corresponding to an “ideal” short-term (transient) behavior. We provide results that cover both discrete- and continuous-time dynamics. Our analysis also captures the differences between the cases where the system dynamics is defined by matrices irreducible and reducible, respectively. The theoretical developments are applied to the practical study of the short-term behavior for two positive linear systems already discussed in literature by other authors.

scholarly journals Learning Long- and Short-Term User Literal-Preference with Multimodal Hierarchical Transformer Network for Personalized Image Caption

2020 ◽  
Vol 34 (05) ◽  
pp. 9571-9578 ◽  
Author(s):  
Wei Zhang ◽  
Yue Ying ◽  
Pan Lu ◽  
Hongyuan Zha
Keyword(s):  
State Of The Art ◽  
Natural Extension ◽  
Target Image ◽  
Short Term ◽  
Image Representations ◽  
High Level ◽  
Image Descriptions ◽  
Shed Light ◽  
Image Caption

Personalized image caption, a natural extension of the standard image caption task, requires to generate brief image descriptions tailored for users' writing style and traits, and is more practical to meet users' real demands. Only a few recent studies shed light on this crucial task and learn static user representations to capture their long-term literal-preference. However, it is insufficient to achieve satisfactory performance due to the intrinsic existence of not only long-term user literal-preference, but also short-term literal-preference which is associated with users' recent states. To bridge this gap, we develop a novel multimodal hierarchical transformer network (MHTN) for personalized image caption in this paper. It learns short-term user literal-preference based on users' recent captions through a short-term user encoder at the low level. And at the high level, the multimodal encoder integrates target image representations with short-term literal-preference, as well as long-term literal-preference learned from user IDs. These two encoders enjoy the advantages of the powerful transformer networks. Extensive experiments on two real datasets show the effectiveness of considering two types of user literal-preference simultaneously and better performance over the state-of-the-art models.

scholarly journals Long-term target tracking combined with re-detection

2020 ◽  
Author(s):  
Juanjuan Wang ◽  
HaoRan Yang ◽  
Ning Xu ◽  
Chengqin Wu ◽  
ZengShun Zhao ◽  
...  
Keyword(s):  
Correlation Energy ◽  
State Of The Art ◽  
Tracking Algorithm ◽  
Correlation Filters ◽  
Short Term ◽  
Tracking Method ◽  
Tracking Tasks ◽  
Svm Model ◽  
Confidence Degree

Abstract The long-term visual tracking undergoes more challenges and is closer to realistic applications than short-term tracking. However, the performances of most existing methods have been limited in the long-term tracking tasks. In this work, we present a reliable yet simple long-term tracking method, which extends the state-of-the-art Learning Adaptive Discriminative Correlation Filters (LADCF) tracking algorithm with a re-detection component based on the SVM model. The LADCF tracking algorithm localizes the target in each frame and the re-detector is able to efficiently re-detect the target in the whole image when the tracking fails. We further introduce a robust confidence degree evaluation criterion that combines the maximum response criterion and the average peak-to correlation energy (APCE) to judge the confidence level of the predicted target. When the confidence degree is generally high, the SVM is updated accordingly. If the confidence drops sharply, the SVM re-detects the target. We perform extensive experiments on the OTB-2015 and UAV123 datasets. The experimental results demonstrate the effectiveness of our algorithm in long-term tracking.

Experimental study of the short-term and long-term behavior of perfobond connectors

2018 ◽  
Vol 150 ◽  
pp. 462-474 ◽  
Author(s):  
Jing Zhang ◽  
Xiamin Hu ◽  
Liya Kou ◽  
Bing Zhang ◽  
Yuchen Jiang ◽  
...  
Keyword(s):  
Experimental Study ◽  
Short Term ◽  
Long Term Behavior

scholarly journals Neural Architecture Search for a Highly Efficient Network with Random Skip Connections

2020 ◽  
Vol 10 (11) ◽  
pp. 3712
Author(s):  
Dongjing Shan ◽  
Xiongwei Zhang ◽  
Wenhua Shi ◽  
Li Li
Keyword(s):  
State Of The Art ◽  
Cell Structure ◽  
Frequency Discrimination ◽  
Search Space ◽  
Short Term ◽  
Cell Parameters ◽  
Neural Architecture ◽  
Proposed Model ◽  
Initialization Scheme

Regarding the sequence learning of neural networks, there exists a problem of how to capture long-term dependencies and alleviate the gradient vanishing phenomenon. To manage this problem, we proposed a neural network with random connections via a scheme of a neural architecture search. First, a dense network was designed and trained to construct a search space, and then another network was generated by random sampling in the space, whose skip connections could transmit information directly over multiple periods and capture long-term dependencies more efficiently. Moreover, we devised a novel cell structure that required less memory and computational power than the structures of long short-term memories (LSTMs), and finally, we performed a special initialization scheme on the cell parameters, which could permit unhindered gradient propagation on the time axis at the beginning of training. In the experiments, we evaluated four sequential tasks: adding, copying, frequency discrimination, and image classification; we also adopted several state-of-the-art methods for comparison. The experimental results demonstrated that our proposed model achieved the best performance.

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