Magnetohydrodynamic (MHD) mixed convection stagnation point flow of a nanofluid over a vertical plate with viscous dissipation
Bibliographic record
Abstract
In this study, magnetohydrodynamic effects on the mixed convection flow of nanofluid particles, namely, Cu (copper) and Al 2 O 3 (alumina) near a stagnation region over a vertical plate in the presence of viscous dissipation is investigated. The governing equations of the nanofluid flow model proposed by Tiwari and Das (Int. J. Heat Mass Transfer, 50, 2002 (2007). doi:10.1016/j.ijheatmasstransfer.2006.09.034) are converted into a dimensionless nonlinear system of ordinary differential equations by using the similarity transformation. The solution of the resulting equations is obtained numerically by using a very efficient implicit scheme known as the Keller box method. A comparison with previous studies is shown in tabular form and excellent agreement is found. The effects of pertinent parameters like magnetic parameter M, Eckert number Ec, and volume fraction parameter ϕ on velocity, temperature, skin friction coefficient, and local Nusselt number with fixed value of Prandtl number Pr = 6.2 are shown graphically and discussed. These results show that the skin friction coefficient increases for both nanoparticles in assisting and opposing flow cases because of increasing absolute values of M and Ec, on the other hand heat transfer rate is enhanced in the opposing flow case and reduced in the assisting flow case. The values of skin friction coefficient for both nanoparticles, namely, Cu and Al 2 O 3 increase with the increase in volume fraction parameter ϕ in both assisting and opposing flow cases and Cu has a higher value than Al 2 O 3 . The same behavior is observed for local Nusselt number in opposing flow, but in assisting flow the value of local Nusselt number decreases with the increase of ϕ in the presence of magnetic and viscous dissipation effects and Cu has a smaller value than Al 2 O 3 .
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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 | 0.000 |
| 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".