Artificial neural networks and the effects of loading conditions on fatigue life of carbon and low-alloy steels

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The ASME Boiler and Pressure Vessel Code contains rules for the construction of nuclear power plant components. Figure 1-90 of Appendix I to Section III of the Code specifies fatigue design curves for structural materials. However, the effects of light water reactor (LWR) coolant environments are not explicitly addressed by the Code design curves. Recent test data indicate significant decreases in the fatigue lives of carbon and low-alloy steels in LWR environments when five conditions are satisfied simultaneously. When applied strain range, temperature, dissolved oxygen in the water, and sulfur content of the steel are above a minimum threshold level, ... continued below

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13 p.

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Pleune, T. T. & Chopra, O. K. November 1996.

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  • Pleune, T. T. Massachusetts Institute of Technology, Cambridge, MA (United States). Nuclear Engineering Dept.
  • Chopra, O. K. Argonne National Lab., IL (United States)

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Description

The ASME Boiler and Pressure Vessel Code contains rules for the construction of nuclear power plant components. Figure 1-90 of Appendix I to Section III of the Code specifies fatigue design curves for structural materials. However, the effects of light water reactor (LWR) coolant environments are not explicitly addressed by the Code design curves. Recent test data indicate significant decreases in the fatigue lives of carbon and low-alloy steels in LWR environments when five conditions are satisfied simultaneously. When applied strain range, temperature, dissolved oxygen in the water, and sulfur content of the steel are above a minimum threshold level, and the loading strain rate is below a threshold value, environmentally assisted fatigue occurs. For this study, a data base of 1036 fatigue tests was used to train an artificial neural network (ANN). Once the optimal ANN was designed, ANN were trained and used to predict fatigue life for specified sets of loading and environmental conditions. By finding patterns and trends in the data, the ANN can find the fatigue lifetime for any set of conditions. Artificial neural networks show great potential for predicting environmentally assisted corrosion. Their main benefits are that the fit of the data is based purely on data and not on preconceptions and that the network can interpolate effects by learning trends and patterns when data are not available.

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13 p.

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INIS; OSTI as DE97007105

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  • American Society of Mechanical Engineers (ASME) pressure vessel and piping conference, Orlando, FL (United States), 27 Jul - 1 Aug 1997

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  • Other: DE97007105
  • Report No.: ANL/ET/CP--91268
  • Report No.: CONF-970726--22
  • Grant Number: AC05-76OR00033;W-31109-ENG-38
  • Office of Scientific & Technical Information Report Number: 505728
  • Archival Resource Key: ark:/67531/metadc697303

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Office of Scientific & Technical Information Technical Reports

Reports, articles and other documents harvested from the Office of Scientific and Technical Information.

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  • November 1996

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  • Aug. 14, 2015, 8:43 a.m.

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  • April 19, 2016, 1:35 p.m.

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Pleune, T. T. & Chopra, O. K. Artificial neural networks and the effects of loading conditions on fatigue life of carbon and low-alloy steels, article, November 1996; Tennessee. (digital.library.unt.edu/ark:/67531/metadc697303/: accessed October 17, 2018), University of North Texas Libraries, Digital Library, digital.library.unt.edu; crediting UNT Libraries Government Documents Department.