An analytical correlation for conjugate heat transfer in fin and tube heat exchangers
Bibliographic record
Abstract
Conjugate heat transfer of liquid-gas fin and tube heat exchangers is widely used in industry. However, heat transfer characteristics of such system is difficult to predict as the limiting heat rate can be from either side. This paper aims to quantify the conjugate heat transfer performance of fin and tube heat exchangers via mathematical modeling . Three-dimensional conjugate fluid flow and heat transfer model is developed and validated against the state-of-the-art experimental data and existing analytical correlations. Turbulent k-ε model has been employed for the fluid flow and heat transfer modeling. Statistical method, i.e. data reduction and multivariate nonlinear regression techniques , is implemented to analyse the results and to quantify the interaction between parameters. Wide range of parametric studies and simulations is further carried out to evaluate the significance of design, geometrical and operating parameters. According to the results of the study, larger fin length factor and fin pitch and the smaller tube diameter are in favor of fin and tube heat exchangers within the Reynolds number range of 3000–12,000. Finally, a novel conjugate heat transfer correlation for liquid-gas finned tube heat exchangers is proposed to assist engineers for practical designs and applications. The results suggest that our new correlation gives rise to a more accurate conjugate heat transfer prediction as compared to that of traditional non-conjugate counterpart.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".