Prediction and analysis of thermal‐hydraulic performance of tubes with teardrop dimples based on artificial neural networks
Bibliographic record
Abstract
Abstract In this study, the prediction and analysis of the thermal‐hydraulic performance of tubes with teardrop dimples were carried out. First, a numerical model of dimpled tubes using ANSYS 17.0 was established and verified by comparison with the Nusselt number (Nu) and friction factor (f) in the literature. Second, artificial neural networks (ANNs) were developed with five neurons in the input layer, the hidden layers of various neurons, and one neuron in the output layer. The five neurons are the diameter of the windward sphere (d1), diameter of the leeward sphere (d2), dimple depth (hd), spacing between two axially adjacent dimples (l), and quantity of dimples in a transverse cross‐section (N). After careful comparison, the model with the 5‐2‐6‐1 structure was selected for predicting both f and Nu, while the model with the 5‐6‐1 structure was selected for performance evaluation criteria (PEC). Finally, the influence of d2/d1 on the heat transfer and pressure drop is discussed when l/d1 or N changes. The results showed when N is moderate or l/d1 is large within the scope of the design, the heat transfer performance in the windward part of the teardrop dimples is better than that of the spherical dimples. In addition, as N increases or l/d1 decreases, the influence of dimples on the main flow increases, making the mixture of hot and cold working fluid better but causing a greater pressure drop. This study may provide ideas and guidance for the design, selection, and optimization of dimpled tubes.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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".