Stagnation point flow of magnetized Cu–Cuo–water nano liquid via a porous dissipative stretching surface: A theoretical investigation
Bibliographic record
Abstract
Nanotechnology is progressively being used to increase heat transfer rates by employing an efficient homogenous combination of nanoparticles. Inspired by these developments, a simulation investigation of porous media subjected to stagnation point flow under the effects of dissipation and uneven thermal sink/generation boundary layer is performed. Dimensionless forms of the governing equations are obtained by adopting a model of Tiwari–Das nanofluid to study the fluid flow considering water-based Cu and Cuo nanoparticles. A coupled ordinary derivative invariant model is obtained from the transformed partial differentiation equations. A computational shooting method with a fourth-order Runge–Kutta scheme is used to offer solutions to the ODEs (Ordinary Differential Equations). This study was done to understand the impacts of pertinent physical terms on the flow characteristics in porous media. Additionally, the wall quantities, thermal, and concentration diffusion are examined and discussed, and the output is presented in plots and tables. The limiting cases are considered and briefly addressed as compared with the existing results. The solution outputs revealed that the heat propagation is momentously influenced by the volume and size of the nanoparticles. The fluid molecular bond is strengthened by the rising induced magnetic field. This investigation is treasured in extensive applications that are not restricted to the physical sciences and engineering.
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 | 0.000 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".