Numerical Analysis of Temperature-Dependent Thermal Boundary Layers in Falkner-Skan Flow of Viscoelastic Fluids
Bibliographic record
Abstract
This study numerically investigates the Falkner-Skan thermal boundary layer for viscoelastic fluids, focusing on the influence of temperature-dependent material properties on flow and heat transfer dynamics. Using a second-order viscoelastic constitutive model, coupled heat transfer and boundary layer equations were solved to account for variations in viscosity, thermal conductivity, and specific heat with temperature. Realistic boundary conditions, including constant temperature and constant heat flux, were implemented. Results indicate that increasing the first normal stress coefficient from 0.5 to 1.5 expands the thermal boundary layer thickness by 25%, while Prandtl numbers ranging from 1 to 50 reduce boundary layer thickness by up to 40%. Favorable pressure gradients enhance heat transfer, leading to a 30% increase in the local Nusselt number along the wedge. These findings provide critical insights into the thermal behavior of viscoelastic fluids, with applications in optimizing heat transfer processes in polymer extrusion, chemical reactors, and industrial coating systems.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".