Effects of superhydrophobicity and Lorentz force on <scp>MWCNT</scp> ‐ <scp> AL <sub>2</sub> O <sub>3</sub> </scp> ‐ <scp>CuO</scp> /(10:90) <scp>EG.W</scp> flow in a microtube with viscous dissipation
Bibliographic record
Abstract
Abstract Advancements in the chemical engineering field, particularly in small‐scale processes, have led to an increasing demand for high‐performance heat‐dissipating microsystems. Thus, in the present study, a MWCNT‐Al 2 O 3 ‐CuO/(10:90) EG.W ternary hybrid nanoliquid flow in a microtube with viscous dissipation subjected to a constant wall heat flux is investigated numerically. The flow is under a uniform magnetic field with slip velocity at the wall due to wall superhydrophobicity. The set of governing equations and the corresponding boundary conditions were solved by using the finite volume method and the SIMPLER algorithm. The developed code was validated with experimental data from the literature. The main purpose of the present study is to explore the combined effects of Lorentz force and wall superhydrophobicity on the thermohydraulic performance and entropy generation of the ternary hybrid nanoliquid flow. Results showed that increasing the slip parameter enhances the convective heat transfer and decreases the pressure drop with a performance index greater than unity. All entropy production components decrease for high values of slip velocity, while the Bejan number shows opposite behaviour. Higher values of the Hartmann number make convective heat transfer and pressure drop increase with a performance index greater than unity. Magnetic, friction, and global entropy production components increase for high Hartmann values, while thermal entropy production and the Bejan number present opposite behaviour. More details on local and average variations of thermohydraulic performance and irreversibility components are presented and discussed.
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 | 0.000 |
| 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".