Bibliographic record
Abstract
Microchannel heat exchangers are being considered for use in the Generation IV nuclear reactors for their ability to provide increased thermal efficiency in a small volume relative to other types of heat exchangers via an extremely high surface area-to-volume ratio.Three distinct analysis methods that may be used to evaluate the technology are presented in this work: finite element method modelling, application of smoothed particle hydrodynamics, and Kriging-based optimization.The finite element method model generated for a single pair of channels for the hot and cold working fluids yields results that agree with those produced using the effectiveness-number of transfer units method, and is a suitable base for the optimization performed.More complex free surface flows are effectively modelled using smoothed particle hydrodynamics in a number of demonstrative cases, including boiling flow through a heated channel.The I would first like to express the most sincere gratitude to my supervisors, Dr. John Goldak and Dr. Tarik Kaya of Carleton University, for their excellent guidance and seemingly endless patience throughout this process.Furthermore, I owe a debt of gratitude to the Goldak Technologies Inc c staff, which has included Dan Downey, Stanislav Tchernov, and Jianguo Zhou, for their technical support throughout my endeavours with the VrSuite software.Their assistance has been tremendous.I offer
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".