A cost‐effective approach to generate accurate correlations via analyzing a minimum number of data points; Case study on the convection heat transfer problem
Bibliographic record
Abstract
Abstract It is very common in the heat transfer area to analyze and design heat equipment using the past available heat correlations. Basically, demanding higher‐accuracy correlations enforces the heat laboratories to test and collect larger banks of laboratory data. However, this conversely affects the laboratory cost. Therefore, it becomes challenging to create new approaches that let the correlation developers use smaller experimental datasets and provide correlations with sufficient accuracies. To surmount this challenge, the present work develops a new approach that benefits from the computational fluid dynamics method as a reliable and cheap tool and adequately enriches the original, insufficient dataset. Then, suitable enhanced correlations are developed using the new enriched experimental‐numerical‐based dataset. In parallel, the artificial neural network (ANN) is used to enrich the original insufficient dataset separately. Using this experimental‐ANN‐based dataset, it provides a totally ANN‐based correlation. It is shown that the results of enhanced correlations are as accurate as those of the ANN‐based correlation. However, the point is that the use of the present approach is about 100 times faster than using the ANN. The typical forced convection heat transfer through a pipe is examined here to show the capabilities of the current approach.
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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 | 0.003 | 0.009 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".