Computational investigation of fluid flow and heat transfer of conical Taylor–Couette flow with radial‐temperature difference
Bibliographic record
Abstract
Abstract This study numerically investigated fluid flow and heat transfer characteristics of conical Taylor–Couette flow with a radial temperature difference. The computational conditions were limited to a moderate Reynolds number ( Re ) regime. The Rayleigh number Ra , which characterizes the buoyancy‐driven flow, was varied using two types of fluids: water for the higher Ra (= 61,437.7) and 40 wt.% glycerol aqueous solution for the lower Ra (= 18,373.2). The flow pattern of the conical Taylor–Couette flow was confirmed to be significantly affected by Ra . In particular, at the higher Ra tested ( Ra = 61,437.7), large‐distorted Taylor cells were observed at the highest Re within the computational conditions of this study. Additionally, the time‐series data of the velocity fluctuation were analyzed through continuous wavelet transform analysis, and consequently, logarithmic expectation and information entropy were obtained. The results indicated that the velocity fluctuation exhibited a fractal structure. Furthermore, the area‐ and time‐averaged Nusselt numbers ( Nu t ) of the conical and cylindrical systems were compared based on the power consumption per volume. The conical system achieved comparable heat transfer performance with a substantially lower power input under higher Ra , demonstrating its superior energy efficiency.
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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) | 0.000 | 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 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".