Manufactured nuclear components under stresses induced through normal operations cause mechanical fatigue and strain. Depending on their magnitude and distribution they can contribute to increasing the expected life of a component or for its premature failure. Using Barkhausen noise we can analyze the microstructural characteristics without damaging the sample through magnetization or acoustics. The samples in our case are ferromagnetic metals, also known as ferrous metals, from heat treated and rolled steel. A Rollscan 300 instrument and Microscan 600 software were used to acquire Barkhausen noise data from fatigued steel samples. MATLAB software and R software were used to evaluate results of the Microscan 600 to better understand the signal processing algorithms. In order to find a correlation we used a two random variable probability distribution function (PDF). plot We found the difference between the three positions taken on the given sample at each strain level, and with a 95% confidence level we created a plot of data points that found a loose correlation in the data results between both perpendicular and parallel testing. Using these results we can compare older sets of data and create an accurate prediction of stress levels induced upon nuclear components. We hope to create more precise predictions in the near future using alternative methods, such as statistical calibration techniques to find closer one‐to‐one correlations.


Jeff Griffin

Lab site

Pacific Northwest National Laboratory (PNNL)

Funding Acknowledgement

This material is based upon work supported by the S.D. Bechtel, Jr. Foundation and by the National Science Foundation under Grant No. 0952013. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the S.D. Bechtel, Jr. Foundation or the National Science Foundation. This project has also been made possible with support of the National Marine Sanctuary Foundation. The STAR program is administered by the Cal Poly Center for Excellence in Science and Mathematics Education (CESaME) on behalf of the California State University (CSU).



URL: https://digitalcommons.calpoly.edu/star/158


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