Heat transfer in fully developed Couette–Poiseuille flow of power‐law fluids with viscous dissipation
Bibliographic record
Abstract
Abstract This study investigates fully developed, steady, laminar forced convective heat transfer in Couette–Poiseuille flow of power‐law fluids between heated parallel plates, relevant to dynamic wall heat exchangers, microfluidic devices, and polymer processing. The analysis examines the influence of the flow rate ratio between the shear‐driven (Couette) and total imposed flow on heat transfer for Newtonian and power‐law fluids, with variations in power‐law index , upper plate velocity , and Brinkman number ( or ) including viscous dissipation. A semi‐analytical velocity profile is derived, while the temperature distribution and Nusselt number are obtained analytically. These solutions provide insights into flow and heat transfer mechanisms, allow quick evaluation without extensive computations, and serve as reliable references for validating numerical simulations. Results are validated against ANSYS Fluent simulations and literature data. Findings reveal an optimal shear‐driven component opposing the pressure‐driven flow that maximizes heat transfer for a moving insulated plate. For negligible viscous dissipation , shear‐thinning fluids enhance heat transfer under purely pressure‐driven flow, while shear‐thickening fluids reach a maximum Nusselt number comparable to Newtonian fluids but at lower shear‐driven motion, reducing energy demand. The novelty lies in identifying the optimal flow rate ratio between Couette and Poiseuille components in non‐Newtonian fluids, offering a framework to maximize heat transfer while minimizing energy input. The findings aid thermal management in systems with combined flow‐driving mechanisms, for example, dynamic wall heat exchangers.
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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 | 0.001 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".