Conjugate Effect on the Thermal Characteristics of Air Impinging Jet
Bibliographic record
Abstract
A computational fluid dynamics (CFD) investigation to determine the conjugate heat transfer (CHT) effect on the stagnation and local thermal characteristics due to an impinging process has been carried out in this study using STAR-CCM+ - Siemens PLM commercial code. The transient Navier-Stokes’s equations are numerically solved using a finite volume approach with k-ω SST eddy viscosity as the turbulence model. A fully developed circular air jet with different Reynolds numbers, impinging vertically onto a heated flat disc with different metals, thicknesses, and boundary heat fluxes are employed in the current study to examine the thermal characteristics and provide an enhanced picture for the convection mechanism that used in jet cooling technology. It is found that the thermal characteristics are influenced by the thermal conductivity and thickness of the target upon using air as a cooling jet. The CHT process enhances the local convective heat transfer at the fluid-solid interface due to the variation in transverse and axial conductive heat transfer inside the metal up to a certain redial extent from the stagnation region compared to the process with no CHT. The extent of the radial enhancement depends on the thermal conductivity of the metal. For a given thermal conductivity, the CHT process acts to increase the temperature and convective heat flux of the stagnation region as the metal thickness increases.
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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 | 0.000 | 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".