Exploring the effects of low amplitude fatigue in crack growth rates in high temperature aqueous solution/metal systems
- Pennsylvania State Univ., University Park, PA (United States)
- National Aeronautics and Space Administration, Greenbelt, MD (United States). Goddard Space Flight Center
In this work, the authors explore the use of artificial neural networks (ANN, net) in sorting and interpreting the impact of mechanical variables [such as applied stress intensity factor (K{sub max}), amplitude and frequency of loading ({Delta}K, {omega})] and environmental parameters [e.g., the corrosion potential (ECP)] on fatigue crack growth in steels in high temperature aqueous systems. In doing so, they reviewed and collected fatigue crack growth rate (FCGR) data from the open literature, constructed a suitable database (mainly from data obtained from the Argonne National Laboratory) for use (as inputs) with the Artificial Neural Network (ANN), designed an ANN and trained it on the data base, and used the ANN to extrapolate the range of input variables. They discuss the predictions of the ANN, and compare and contrast the findings with known and expected trends.
- OSTI ID:
- 367724
- Report Number(s):
- CONF-960389--
- Country of Publication:
- United States
- Language:
- English
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