Investigations on Flow and Flow-Induced Vibration of CANDU Fuel Bundles
Bibliographic record
Abstract
Excitations induced by three-dimensional unsteady flows of ordinary water coolant through a string of CANDU fuel bundles in a fuel channel are investigated in this thesis. Several comprehensive computational fluid dynamics (CFD) models are developed and solved by means of large eddy simulation (LES), high performance computers and parallel processing scheme. The 12-bundle flow model is the first ever developed concerning flow in a very complex CANDU fuel channel. The lateral fluid flow and flow-induced excitations on every fuel bundle are obtained and analyzed for various combinations of bundle angular positions. The coherent nature of the flow through the multiple bundles inside the fuel channel exhibiting fluid excitations of frequencies spreaded over a wide band in the power spectra is a source of bundle lateral vibration. The flow features of different bundle regime are correlated both in time and frequency domain and they are sensitive to the bundle-to-bundle angular position. This finding directs that, to study the flow and flow-induced excitations and vibrations of a bundle string, it is necessary to include all bundles for fluid-structure interactions. Results from the computational model reveal that the misaligned interface changes the flow pattern in the fuel channel. The mean lateral fluid forces increase by an order of magnitude and their RMS values raise about 3 to 4 times at some configurations compared to fully aligned situation. Experiments are also performed using the simulated CANDU bundles in an out-reactor setup to verify the computational results. An analysis of a complete fuel channel of a nuclear reactor using LES is at the forefront of current research worldwide and this study is a major step forward towards understanding and unfolding the fuel bundle vibration phenomenon.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
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 teacher head, 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".