• evaluation of fracture toughness and its scatter in the dbtt region of different types of pressure vessel steels

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    • تاریخ ارائه: 1390/01/01
    • تاریخ انتشار در تی پی بین: 1390/01/01
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     safety analysis of critical components and the overall plant is a major and important task in the field of mechanical engineering. therefore, it is essential to know the allowable loads and the corresponding failure behaviour (e.g., crack initiation, growth and instability) of the component. most of the safety critical components, such as pressure vessels of nuclear reactors, are made of ferritic grade low-alloy steels. when these components operate in the ductile-to-brittle transition (dbtt) regime of the material (due to irradiation embrittlement and other material aging and degradation mechanisms taking place), the two mechanisms that compete with each other in the failure process are: ductile and cleavage fracture. from fracture mechanics point of view, fracture toughness of the material must be adequate to prevent the failure. however, there is considerable scatter observed in the fracture toughness data and the analyst must account for this in the safety analysis. master curve approach according to astm e-1921 standard is popularly employed for this purpose. however, it requires the data for transition temperature t0, which is dependent upon the specimen geometry and loading configurations employed in the laboratory tests. its transferability to safety analysis of components is questionable. in this work, a combined model for ductile and cleavage fracture is used to predict the fracture toughness scatter and its variation with temperature in the dbtt range. it is demonstrated that the above data for fracture toughness can be predicted once we know the material stress–strain data at different temperatures and a single set of weibull statistics parameters for cleavage fracture. extensive experimental investigations have been carried out on two types of pressure vessel steels in the dbtt region using different kinds of specimens to validate the predictions of the model.

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