Bibliographic record
Abstract
Passive safety measures in nuclear reactors can reliably mitigate or prevent accident scenarios.This thesis considered two passive safety measures: plug formation and heat pipes. During a reactor core meltdown, the molten corium material can access cooling pipe connections.There is a chance that the passive plugging of melt flow due to solidification can occur, provided there is an adequate heat sink.A numerical model was created to simulate corium flow through an empty vertical pipe.The numerical model was validated through experimental work using gallium and verified using a previously built analytical model.The numerical model predicted the penetration length of gallium with an average percent error of 10.3% compared to the experimental penetration length results of gallium.The model was then modified to predict the corium penetration length during a severe nuclear accident.Numerous sensitivity studies were also conducted to better understand how certain variables impact the penetration length.Heat pipes are passive, two-phase heat exchangers with excellent heat transfer capabilities.They can be used in passive reactor core cooling and spent fuel pool cooling.Heat pipes have different operating limits that impact their operating conditions and heat transfer capabilities.A numerical approach was used to determine the operational limits of a liquid metal heat pipe that can be used in nuclear applications.The algorithms used to determine the operating limits were presented along with typical results from different operating scenarios.iii Acknowledgments Firstly, I would like to thank my supervisors, Prof. Tarik Kaya and Prof. Edgar Matida, for all their help, guidance, and support during my graduate studies.They served as true mentors to me, which helped me develop personally and put me on the proper path to achieving my future objectives.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".