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Record W7062642186

Uncertainty Analysis in Modelling for CANDU and Pressurized Water Reactors

2023· dissertation· en· W7062642186 on OpenAlexaboutno aff

Bibliographic record

VenueMacSphere (McMaster University) · 2023
Typedissertation
Languageen
FieldPhysics and Astronomy
TopicParticle Detector Development and Performance
Canadian institutionsnot available
Fundersnot available
KeywordsBenchmark (surveying)Neutron transportNuclear powerNuclear dataUncertainty analysisNuclear power plantWork (physics)Uncertainty quantification
DOInot available

Abstract

fetched live from OpenAlex

This thesis documents significant contributions to the quantification of input and modelling uncertainties in the simulation of nuclear power plants. This work is intended to support the simulations that are performed to demonstrate the safety of nuclear power plants in general, and in CANDU reactors specifically. The work presented in this thesis extends the methodologies for uncertainty propagation established internationally to CANDU plants and pioneers the integration of these tools with important plant features in CANDUs, such as online fueling. This thesis documents a series of simulation studies performed to quantify the impact of uncertainties (primarily nuclear data uncertainties), on simulations of CANDU stations and light water reactors (LWRs). The novel part of this work includes quantifying the role of operational feedbacks such as online refuelling and reactor control systems, and important modelling uncertainties, on CANDU simulations. To achieve this objective, this thesis examines 4 important areas as documented in journal papers. To demonstrate understanding of the tools developed for the UAM-LWR benchmark and to support the ongoing international effort, select studies from the UAM-LWR benchmark study exercises were performed and published in the first journal paper. Time-dependent PWR neutronics exercises, considering both nuclear data and manufacturing uncertainties, were completed. This work found that the relative importance of nuclear data uncertainties and manufacturing uncertainties depended on whether the parameter of interest was “local”, such as pin power factors, or “global”, such as homogenized assembly properties. The second publication in this thesis documents the adaption of the tools from the first paper to consider CANDU specific features, such as spatial control systems and online refuelling. This paper demonstrated the significant effect that consistent feedback from fuelling operations has on reducing the total uncertainty in core level simulations of CANDU plants. The tools developed for this work were used to support downstream studies by generating extensive sets of realistic initial conditions for many different possible nuclear datasets. The next publications utilized the tools developed above and then extends the methods to include operational aspects of CANDUs in the assessments for the first time. In the third paper these methods were then used to demonstrate the tools’ capabilities to simulate an operational transient (a power maneuver from 100% full power to 59% full power) in a CANDU station and compared the resultant prediction and uncertainties to measure plant responses. A further study, on the role of nuclear data and initial burnup distribution uncertainty on a CANDU plant’s response to perturbations to liquid zone controller levels, was also performed to examine the effect of the commonly used “superposition principle” utilized in industry to make safety analysis of CANDU’s various fueling states more tractable. In both cases the role of nuclear data uncertainties was generally found to be similar in magnitude to the role of uncertainty in the core initial conditions. The results of this work support the continued safe operation of CANDU nuclear generating stations in Canada by quantifying the role of select uncertainties on safety simulation outputs, informing future BEPU analysis for CANDU plants and demonstrating the exceptional flexibility of the CANDU reactor design. This is reflected in one of the major conclusions of these works, which demonstrates that the natural feedbacks in CANDU operation help to minimize the effect of uncertainties in the outcome of many safety analysis.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.018
Threshold uncertainty score0.035

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0030.002
Open science0.0010.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0020.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.017
GPT teacher head0.217
Teacher spread0.200 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2023
Admission routes1
Has abstractyes

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