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Abstract
In this study, 157 irradiated and non-irradiated Charpy specimens
[1] manufactured from six different steel alloys used in the reactor
pressure vessels (18MND5-W, 22NiMoCr37, A508-B, 15Kh2NMFA,
HSST03 and A508-Cl2) were measured. The measurements included
determining several non-destructively measurable electric, magnetic
and elastic parameters. The applied non-destructive methods were
Direct Current-Reversal Potential Drop (resistivity) [2], 3MA (eddy
current impedance loop shape) [3], TEP (Seebeck Coefficient) [4], MIRBE (Barkhausen noise) [5], MAT (magnetic hysteresis loop shape) [5] and sound velocity. After the non-destructive measurements, the ductile-brittle transition temperature (DBTT) was determined destructively using the ISO-standard method [1]. Several different regression algorithms, including neural network regression and support vector regression, were applied to the data. The algorithms were implemented with TensorFlow and scikit-learn using Python 3.7. With these algorithms, it was possible to estimate the DBTT with the mean absolute error smaller than 20 °C. Based on the results, the method can be seen as a potential candidate for estimating neutron-induced embrittlement non-destructively.
[1] manufactured from six different steel alloys used in the reactor
pressure vessels (18MND5-W, 22NiMoCr37, A508-B, 15Kh2NMFA,
HSST03 and A508-Cl2) were measured. The measurements included
determining several non-destructively measurable electric, magnetic
and elastic parameters. The applied non-destructive methods were
Direct Current-Reversal Potential Drop (resistivity) [2], 3MA (eddy
current impedance loop shape) [3], TEP (Seebeck Coefficient) [4], MIRBE (Barkhausen noise) [5], MAT (magnetic hysteresis loop shape) [5] and sound velocity. After the non-destructive measurements, the ductile-brittle transition temperature (DBTT) was determined destructively using the ISO-standard method [1]. Several different regression algorithms, including neural network regression and support vector regression, were applied to the data. The algorithms were implemented with TensorFlow and scikit-learn using Python 3.7. With these algorithms, it was possible to estimate the DBTT with the mean absolute error smaller than 20 °C. Based on the results, the method can be seen as a potential candidate for estimating neutron-induced embrittlement non-destructively.
| Original language | English |
|---|---|
| Title of host publication | European NDE Symposium for NPP |
| Subtitle of host publication | Event booklet |
| Publisher | NOMAD project |
| Pages | 16 |
| Number of pages | 1 |
| Publication status | Published - 5 May 2021 |
| MoE publication type | Not Eligible |
| Event | European Symposium on NDE for NPP - Franhofer Institute, Saarbruchen, Germany Duration: 4 May 2021 → 5 May 2021 |
Conference
| Conference | European Symposium on NDE for NPP |
|---|---|
| Country/Territory | Germany |
| City | Saarbruchen |
| Period | 4/05/21 → 5/05/21 |
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Dive into the research topics of 'Machine learning and multimethod-NDE for estimating neutron-induced embrittlement'. Together they form a unique fingerprint.Projects
- 1 Finished
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NOMAD: Non-destructive Evaluation (NDE) System for the Inspection of Operation-Induced Material Degradation in Nuclear Power Plants
Rinta-aho, J. (Manager), Grönroos, S. (Participant), Koskinen, A. (Participant), Koskinen, T. (Participant), Jessen-Juhler, O. (Participant) & Lappalainen, P. (Participant)
1/06/17 → 31/05/21
Project: EU project