Chemically radiative and mixed convection solute transfer in boundary‐layer flow of Jeffrey nanofluid along an inclined stretching cylinder with joule heating and double stratification impacts
Bibliographic record
Abstract
Abstract An endeavor is consummate to study the steady mixed convection solute transfer in two‐dimensional viscous fluid. A joule heating incompressible flow along an inclined stretching cylinder and double stratification impact on Jeffrey Nanofluid is scrutinized. Acquired nanomaterial pattern comprises the phenomena of thermophoresis and Brownian motion. By applying rule of approximation transformation, the nonlinear PDEs converted into ODEs. Shooting via Newton Raphson technique is used to solve the ODE'S with boundary conditions into initial conditions, also applying Runge–Kutta–Fehlberg technique of fourth order for numerical purpose. Computation for skin friction, () for Nusselt, and for Sherwood number are fetched in table format by using mathematical software Matlab. The results reveal that M (0, 0.3, 0.6, 0.9), γ (0.1, 0.3, 0.7, 1.3), and α will increase upto 7.51, 8.55, and 10.27%, respectively. The behavior of on and falling off upto 11.20 and 28.38%, respectively with the increase of , but in shows opposite reaction on Sherwood number. It is culminated that for velocity augments with the value of Deborah number of four different sizes (0.8, 1.1, 1.4, and 1.7) upto 14.28, 16.6, and 20% while situation is reversed in case of magnetic factor (0, 0.3, 0.6, 0.9) upto 20, 28.12, and 39.13%. Effect of for temperature enhanced with the value of Brownian motion on (0.0, 0.2, 0.4, and 0.6), about 16.66, 20, and 25%, respectively. The maximum effect percentage with radiation factor on is 39 and minimum percentage is 25.71. Eckert number on ( − 4, 0, 4, 8) and thermophoresis on diminished with pile‐up Prandtl number on (1.2, 1.5, 1.8, and 2.1). Impact of for concentration and local Sherwood number showed reverse situation on Schmidt number.
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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 | 0.000 |
| 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) | 0.001 | 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".