Effects of nickel and manganese on the embrittlement of low-copper pressure vessel steels

Zelenty, J

Abstract:: Solute clustering is known to play a significant role in the embrittlement of reactor pressure vessel (RPV) steels. When precipitates form they impede the movement of dislocations, causing an increase in hardness and a shift in the ductile-brittle transition temperature. Over time this can cause the steel to become brittle and more susceptible to fracture. Thus, understanding precipitate formation is of great importance to the nuclear industry.

The first part of this thesis aims to ...
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APA Style

Zelenty, J. (2016). Effects of nickel and manganese on the embrittlement of low-copper pressure vessel steels [PhD thesis]. University of Oxford.

MLA Style

Zelenty, J. Effects of Nickel and Manganese on the Embrittlement of Low-Copper Pressure Vessel Steels. 2016. University of Oxford, PhD thesis.

Chicago Style

Zelenty, J. 2016. “Effects of Nickel and Manganese on the Embrittlement of Low-Copper Pressure Vessel Steels.” PhD thesis, University of Oxford.
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Files:: Zelenty_Thesis_Dissemenination.pdf

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Authors

+ Zelenty, J More by this author

Division:: MPLS
Department:: Materials
Department:: University of Oxford
Role:: Author

Contributors

+ Moody, M

Department:: University of Oxford
Role:: Supervisor

+ Smith, G

Department:: University of Oxford
Role:: Supervisor

+ Hyde, J

Department:: National Nuclear Laboratory
Role:: Supervisor

+ Rolls-Royce Plc. More from this funder

Funding agency for:: Zelenty, J

DOI:: 10.5287/ora-2nmnyjvqx
Type of award:: DPhil
Level of award:: Doctoral
Awarding institution:: University of Oxford

Language:: English
Keywords:: RPV steels

Clustering

Thermal ageing

Gaussian mixture model

Atom probe tomography
UUID:: uuid:28b9151f-1644-470b-abc7-48ff82bcffdd
Deposit date:: 2018-05-05
ARK identifier:: ark:/29072/ora_28b9151f1644470babc748ff82bcffdd

Related item:: Detecting Clusters in Atom Probe Data with Gaussian Mixture Models
Description:: Accurately identifying and extracting clusters from atom probe tomography (APT) reconstructions is extremely challenging, yet critical to many applications. Currently, the most prevalent approach to detect clusters is the maximum separation method, a heuristic that relies heavily upon parameters manually chosen by the user. In this work, a new clustering algorithm, Gaussian mixture model Expectation Maximization Algorithm (GEMA), was developed. GEMA utilizes a Gaussian mixture model to probabilistically distinguish clusters from random fluctuations in the matrix. This machine learning approach maximizes the data likelihood via expectation maximization: given atomic positions, the algorithm learns the position, size, and width of each cluster. A key advantage of GEMA is that atoms are probabilistically assigned to clusters, thus reflecting scientifically meaningful uncertainty regarding atoms located near precipitate/matrix interfaces. GEMA outperforms the maximum separation method in cluster detection accuracy when applied to several realistically simulated data sets. Lastly, GEMA was successfully applied to real APT data.
Related item:: Understanding thermally induced embrittlement in low copper RPV steels utilising atom probe tomography
Description:: Embrittlement is the primary factor limiting the lifetime of reactor pressure vessels (RPVs). This literature review provides an overview of the causes of hardening and non-hardening embrittlement in RPV steels, with a specific emphasis on precipitation hardening caused by manganese–nickel rich precipitates (MNPs) in thermally aged RPV steels. Several models used to describe the hardening mechanism are discussed. The role of atom probe tomography (APT) in investigating solute clustering in RPV steels is also discussed. The characterisation of nanoscale clusters is enabled by APT’s unique combination of high mass and 3D spatial resolution. This technique, its limitations and the relevant statistical analysis methods available are reviewed.
Related item:: Secondary precipitation within the cementite phase of reactor pressure vessel steels
Description:: Solute clustering is known to play a significant role in the embrittlement of reactor pressure vessel (RPV) steels. In this study, previously unobserved manganese-, nickel-, and silicon-rich precipitates in the cementite phase of a thermally aged, low copper RPV steel are characterised at the atomic scale via atom probe tomography. Additionally, copper-rich precipitates are observed in the cementite phase of a thermally aged, high copper RPV steel. Due to their secluded nature, these secondary precipitates could be utilised as indicators of the time–temperature exposure of RPVs and assist in assessing remnant lifetime.

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Copyright holder:: Zelenty, J

Licence:: Terms and Conditions of Use for Oxford University Research Archive

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Effects of nickel and manganese on the embrittlement of low-copper pressure vessel steels

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Effects of nickel and manganese on the embrittlement of low-copper pressure vessel steels

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