A Machine Learning Approach to Predicting the Heat Convection and Thermodynamics of an External Flow of Hybrid Nanofluid
Bibliographic record
Abstract
Abstract This study numerically investigates heat convection and entropy generation in a hybrid nanofluid (Al2O3–Cu–water) flowing around a cylinder embedded in porous media. An artificial neural network is used for predictive analysis, in which numerical data are generated to train an intelligence algorithm and to optimize the prediction errors. Results show that the heat transfer of the system increases when the Reynolds number, permeability parameter, or volume fraction of nanoparticles increases. However, the functional forms of these dependencies are complex. In particular, increasing the nanoparticle concentration is found to have a nonmonotonic effect on entropy generation. The simulated and predicted data are subjected to particle swarm optimization to produce correlations for the shear stress and Nusselt number. This study demonstrates the capability of artificial intelligence algorithms in predicting the thermohydraulics and thermodynamics of thermal and solutal systems.
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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 | 0.001 | 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".