Investigation of Heat Transfer in Microchannels With Asymmetric and Periodic Slip at the Walls
Bibliographic record
Abstract
Recent development of microfluidic applications in various areas like cooling of silicon chips, VLSI, aircraft avionics, X-Ray and laser equipments has led to an increased study of coupled fluid flow and heat transfer in microchannels. A major issue in the mathematical modeling of these phenomena is the applicability of the no-slip boundary condition at solid-fluid interfaces. Most of such micro-scale investigations consider a slip velocity at solid boundaries, which has been observed in a number of experiemntal studies [1], [2]. In cases involving the heating of substrate and/or transport fluid, definite formation of nanobubbles from the fluid has been established [3]. These bubbles migrate to the channel walls and deposit on them in the form of random clusters. As a result, due to the minimized shear resistance offered by the surfaces of such bubbles to the fluid, variation in slip length is observed along the channel walls. Hence, in order that theoretical studies lead to physically acceptable results required for the fabrication of microfluidic devices, such variation of slip length encountered by a fluid in a microchannel must be included in these analysis.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| 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".