Investigation Of Porous Carbon Foamed Surface Under A Circular Air Jet Impingement For Uniform Heat Transfer
Bibliographic record
Abstract
Impinging jets are widely adopted for applications that require high heat transfer rates.The major disadvantage of jet impingement heat transfer is non-uniformity in the heat transfer from the impingement surface.The present study investigates the targeted surface with attached and detached porous carbon foam under circular air jet impingement.Experiments are performed to measure the local temperature of the targeted plate using the thin metal foil technique and IR thermography.A carbon foam having 8 mm height and 85% porosity is used.The results of the targeted surface with porous carbon foam are compared with the targeted surface without foam for Nusselt number and coefficient of variance (COV).The Reynolds number and distance between targeted surfaces and nozzle exit are the varying parameters.The local Nusselt number of a targeted surface with carbon foam drops in comparison with the smooth surface.However, the carbon foam on a targeted surface offers more uniform heat transfer.The average Nusselt number and the COV of a targeted surface with carbon foam drop with an increase in the distance between the targeted surface and nozzle exit.A comparison of the heat transfer of the targeted surface with an attached and detached carbon foam suggests that the carbon foam pasted on the targeted surface offers conduction heat transfer.The conduction heat transfer offered by the foam is responsible for the uniformity in the heat transfer
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".