Machine-Learning Enhanced Analysis of Mixed Biothermal Convection of Single Particle and Hybrid Nanofluids within a Complex Configuration
Bibliographic record
Abstract
Transport phenomena in a hybrid or single-particle nanofluid over a conical body embedded inside a porous medium are investigated. The fluid contains homogeneously mixed nanoparticles and live cells that are able to migrate, collectively sculpturing a thermo-biosolutal system. Transport processes including mixed convection as well as species and cell transfer are simulated using a similarity technique. As the problem involves a large number of parameters with complicated interactions, machine learning is applied to predict a wide range of parametric variations. The simulation data are used to build an intelligent tool based on an artificial neural network to predict the behavior of the system. This also aids the development of precise correlations for nondimensional parameters dominating the transport phenomena. The results indicate that lower values of the motile Lewis number and a higher mixed convection parameter enhance the Nusselt number. However, it is contained respectively by the increment of the Peclet number and increases in the bio Rayleigh number. It is further shown that an increase in the Prandtl number enhances the Sherwood number and makes the motile microorganisms more uniform. The Peclet number directly influences the transport of heat, mass, and microorganisms. This study clearly demonstrates the abilities of combining numerical simulations with machine learning to significantly extend and enrich analysis of problems with large numbers of variables. The findings also pave the way for predicting behaviors of complex thermo-biosolutal systems without resorting to computationally demanding simulations.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 teacher head, 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".