Turbulent Heat Transfer in Elliptical tube with Dimples
Bibliographic record
Abstract
Heat exchangers play an essential role in heating and cooling in many industrial applications such as cooling towers, aerospace, oil and gas production, and chemical processing.Various heat transfer enhancement technologies are developed to overcome the limitations of conventional heat exchangers and minimize energy consumption.These technologies are classified into active methods, which use external energy, and passive methods, which include geometric modifications like fins, ribs, twisted taps, wire coils, insert devices, and dimples.Passive methods are efficient, reliable, cost-effective, and can be easily implemented, but they come with a penalty in pressure drop.The dimples on the surface have a relatively lowpressure penalty, hence widely studied [1].The objective of this study is to numerically investigate the thermo-hydraulic performance of an Elliptical tube with tear-drop dimples.A turbulent convective heat transfer with Reynolds number ranging from 5000 to 30,000 along with variation in depth of the tear-drop dimples is simulated and results are compared.Flow characteristics and heat transfer mechanisms of different cases are investigated under the single-phase condition.This study provides suggestions for the potential application of the elliptical tube with tear-drop dimples.The simulations are performed using steady-state conditions.Governing equations of continuity, momentum, and energy are solved to predict velocity and temperature fields.
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 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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".