Numerical Simulation of a Canadian Well With One Circumferential Row of Internal Vortex Generators
Bibliographic record
Abstract
Abstract Canadian wells are used for heating and cooling in residential buildings, agriculture and industry. They rely on the quasi-stable underground temperature at a certain depth throughout the year. One way to enhance the performance of this type of heat exchangers, is to implement internal Vortex Generators (VGs). The VGs contribute in disrupting the thermal boundary layer, intensifying turbulence and increasing the heat transfer coefficient. Series of numerical simulations, using ANSYS FLUENT, were conducted to mimic the variable seasonal operational conditions of Canadian Wells during the year. One circumferential row of parallelepiped Vortex Generators was implemented in a real U-shaped tube Canadian Well geometry. The yearly ground and underground temperatures were implemented as sinusoidal functions of time and depth. The VGs were placed immediately downstream of the first bend close to the inlet. The Reynolds number was in the range 14975–42785. The ambient conditions were considered for the city of Constantine (Algeria) at an altitude of 600m over the sea level. The VGs yielded an improvement of up to 8% of the heat transfer coefficient for different Reynolds numbers. The bend, upstream of the VGs, and the wake, downstream of them, play a key role in affecting the heat transfer locally.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".