Enhanced analysis of MHD radiative hybrid nanofluid flow over a spinning disc with hall currents via advanced computational techniques
Bibliographic record
Abstract
This study examines the Hall current characteristics in hybrid nanofluid flow over a rotating disc, incorporating the effects of magnetic fields and nonlinear thermal radiation . The hybrid nanofluid is a novel blend of copper (Cu) and titanium dioxide (TiO 2 ) nanoparticles in water, with the flow behavior further enhanced by adding single-wall carbon nanotubes (SWCNT) and multi-wall carbon nanotubes (MWCNT) with CoFe 2 O 4 . The study uniquely addresses the impact of nanoparticle shapes on flow dynamics, crucial in the evolving field of nanotechnology, where carbon nanotubes (CNTs) find applications in energy storage, fracture toughness, and electromagnetic interactions. Advanced machine learning techniques, such as physics-informed neural networks and hybrid models, are employed to improve predictions, using synthetic data based on governing partial differential equations. Solutions are derived via the new iterative method (NIM) and the bvp 4 c function in Mathematica. The modified New Iterative Method (NIM) integrated with Physics-Informed Neural Networks (PINNs) to address challenges in modeling nonlinear hybrid nanofluid dynamics. This novel approach marks a significant advancement in predictive fluid dynamics. The findings reveal intricate interactions within the nanofluid flow, with graphical analyses illustrating the influence of varied parameters on component behavior and heat transmission. This integration of computational and machine learning methods enhances the understanding of complex flow dynamics, marking a significant advancement in fluid dynamics research.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".