Computing neural network to analyze heat and mass transfer in the flow of nanofluid between two disks
Bibliographic record
Abstract
The model of copper nanoparticles which are suspended in the engine oil (EO) and rotated between two stretchable disks is analyzed. The flow, heat, and mass transmission phenomena of nanofluid with magnetohydrodynamics (MHD) have a vital role in many industries. A magnetic field in the vertical direction is imposed in the flow of the nanofluid and Dufour and Soret (DS) effects are discussed in the equations of energy and concentration. The main equations of motion and energy are converted into a set of nonlinear ordinary differential equations (ODEs) after applying the similarity conversions. A popular semi-analytical approach, namely the differential transform method (DTM) is used to get the solution of velocity, temperature, and concentration profiles. The effect of the various parameters on all profiles is graphically presented and explained. The present data of shear stress, the Nusselt, and the Sherwood numbers calculated by DTM are matched and verified by numerical method data and literature for the novelty of the work. The strength of the work is to analyze the validation, training, and testing by using Levenberg-Marquardt artificial neural network (ANN). This ANN is verified by mean square error, error histogram, and regression analysis.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".