On the Correlation of Crack Resistance Curves on Upper Shelf Energy for German RPV Steels

2007 ◽  
Author(s):  
Dieter Siegele ◽  
Jo¨rg Hohe ◽  
Gerhard Nagel
Keyword(s):  
Crack Resistance ◽  
Material Model ◽  
Transition Curve ◽  
Good Representation ◽  
Shelf Energy ◽  
Mean Values ◽  
Surveillance Programs ◽  
Fracture Appearance ◽  
Resistance Curves ◽  
Crack Resistance Curves

J-crack resistance (J-R) curves, numerically determined and based on correlation formulas with the upper shelf energy level (USE) of the Charpy transition curve have been compared with measured J-R curves for German RPV steels, determined from single specimen tests. The USE were determined as mean values of the results above 95% ductile fracture appearance, as available from usual application in irradiation surveillance programs and averaging the temperature effect. The J-R curves numerically determined by the Gurson material model give a good representation of the measured crack resistance curves and the application of the correlation with USE in U.S. NRC Regulatory Guide 1.161 results in enveloping approximations with conservativeness increasing with crack growth.

Estimating J-R Curve From CVN Upper Shelf Energy and its Application

2016 ◽  
Author(s):  
Samarth Tandon ◽  
Ming Gao ◽  
Ravi Krishnamurthy ◽  
Richard Kania ◽  
Gabriela Rosca
Keyword(s):  
Fracture Resistance ◽  
Theoretical Background ◽  
Shelf Energy ◽  
Ductile Tearing ◽  
Failure Pressure ◽  
Tearing Instability ◽  
Integrity Management ◽  
Level 3 ◽  
Resistance Curves ◽  
Crack Resistance Curves

Accuracy in predictions of burst pressures for cracks in pipelines has significant impact on the pipeline integrity management decisions. One of the fracture mechanics models used for failure pressure prediction is API 579 Level 3 FAD ductile tearing instability analysis that requires J-R curves, i.e., crack resistance curves, for the assessment. However, J-R curves are usually unavailable for most pipelines. To overcome this technical barrier, efforts have been made to estimate the J-R curve indirectly from commonly available toughness data, such as the Charpy V-notched Impact Energy CVN values, by correlating the upper-shelf CVN value (energy) to the ductile fracture resistance J-R curve. In this paper, the theoretical background and studies made by various researchers on this topic are reviewed. Attempts made by the present study to establish correlations between CVN and J-R curves for linepipe materials are then presented. Application of this CVN-JR correlation to API 579 Level 3 FAD tearing instability assessment for failure pressure predictions is demonstrated with examples. The accuracy of the correlation is analyzed and reported.

Machine learning-based prediction of the translaminar R-curve of composites from simple tensile test of pre-cracked samples

2021 ◽  
pp. 2050017
Author(s):  
Cheng Qiu ◽  
Yuzi Han ◽  
Logesh Shanmugam ◽  
Zhidong Guan ◽  
Zhong Zhang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Fracture Mechanics ◽  
Crack Resistance ◽  
Experimental Studies ◽  
Basalt Fiber ◽  
Learning Model ◽  
Machine Learning Model ◽  
Finite Fracture Mechanics ◽  
Resistance Curves ◽  
Crack Resistance Curves

A novel approach to determine the translaminar crack resistance curve of composite laminates by means of a machine learning model is presented in this paper. The main objective of the proposed method is to extract hidden information of crack resistance from strength values of center-cracked laminates. Compared to traditional measurements, the notable advantage is that only tensile strength values are required which can be obtained by a rather simpler experimental procedure. This is achieved by the incorporation of the finite fracture mechanics, which links crack resistance with strength values. In order to get training dataset, a semi-analytical method using both finite element method and finite fracture mechanics is employed to generate strength values of center-cracked specimens with different random R-curves, which serve as inputs for our artificial neural network. Regarding the outputs, principal component analysis is performed to reduce dimensionality and find suitable descriptors for crack resistance curves. After successfully training machine learning model, experimental studies on basalt fiber reinforced laminates are conducted as validation. Results have proven the effectiveness of the proposed strategy for predicting crack resistance curves, as well as the feasibility of using machine learning-based framework to find out more information about composites from simple experimental data.

Crack resistance curves of ceramics determined on notched and precracked bend bars

1991 ◽  
Vol 10 (18) ◽  
pp. 1090-1092 ◽  
Author(s):  
M. Saadaoui ◽  
G. Orange ◽  
C. Olagnon ◽  
G. Fantozzi
Keyword(s):  
Crack Resistance ◽  
Resistance Curves ◽  
Crack Resistance Curves
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