Seismic performance of degraded AGR cores
Lead Research Organisation:
University of Bristol
Department Name: Civil Engineering
Abstract
This research is exploring the behaviour and contact dynamics of graphite cored nuclear reactors.
The research will examine the correlation between experimental data and the analytical data. This
will lead to an identification of the useful questions that need to be answered in terms strengthening
the safety case. The work will also look at how to improve the use of numerical tools to inform safety
case arguments. In particular it will compare existing experimental data with GCORE, SOLFEC,
3DEC and other potential modelling approaches. This research will also aim to identify ways to
predict the types of crack patterns that will lead to poorer dynamic performance of the array.
Identifying these patterns could allow targeted core inspections focused on looking for the specific
types of cracking that would be more critical to the overall core performance.
Key outputs from the research:
Further validation and verification of the numerical methodology, enhanced ways of interpreting the
data, deeper understanding of the fundamental mechanics of the aging core, identification of critical
crack patterns.
Timeline:
Year 1: Study of data from tests to date and development of tools to aid comparison between
experimental and analytical results. Key deliverable - End of year report focussing on data
processing tools and initial findings from the research.
Year 2: Identification of features that make particular crack cluster patterns more susceptible to
damage in seismic events. Key deliverable - Report identifying types of patterns that could lead to
poorer core performance.
Year 3: Study of alternative core modelling techniques that could be used to better predict array
behaviour under seismic loading. Key deliverable - Report identifying the limitations of the current
analysis techniques and some ways that the analytical modelling can be improved.
The research will examine the correlation between experimental data and the analytical data. This
will lead to an identification of the useful questions that need to be answered in terms strengthening
the safety case. The work will also look at how to improve the use of numerical tools to inform safety
case arguments. In particular it will compare existing experimental data with GCORE, SOLFEC,
3DEC and other potential modelling approaches. This research will also aim to identify ways to
predict the types of crack patterns that will lead to poorer dynamic performance of the array.
Identifying these patterns could allow targeted core inspections focused on looking for the specific
types of cracking that would be more critical to the overall core performance.
Key outputs from the research:
Further validation and verification of the numerical methodology, enhanced ways of interpreting the
data, deeper understanding of the fundamental mechanics of the aging core, identification of critical
crack patterns.
Timeline:
Year 1: Study of data from tests to date and development of tools to aid comparison between
experimental and analytical results. Key deliverable - End of year report focussing on data
processing tools and initial findings from the research.
Year 2: Identification of features that make particular crack cluster patterns more susceptible to
damage in seismic events. Key deliverable - Report identifying types of patterns that could lead to
poorer core performance.
Year 3: Study of alternative core modelling techniques that could be used to better predict array
behaviour under seismic loading. Key deliverable - Report identifying the limitations of the current
analysis techniques and some ways that the analytical modelling can be improved.
Organisations
People |
ORCID iD |
Adam Crewe (Primary Supervisor) | |
Liam Turton (Student) |
Studentship Projects
Project Reference | Relationship | Related To | Start | End | Student Name |
---|---|---|---|---|---|
EP/N509619/1 | 30/09/2016 | 29/09/2021 | |||
1794075 | Studentship | EP/N509619/1 | 30/09/2016 | 31/12/2021 | Liam Turton |