Effects of the Conjugate Heat Transfer and Heat Flux Strength on the Thermal Characteristics of Impinging Jets
Bibliographic record
Abstract
A numerical study using the conjugate heat transfer approach has been performed to investigate the effects of boundary heat flux, conduction effect, and working fluid on the thermal characteristics due to the jet impingement process. Air and water are used in this study as working fluids. For the water jet, the volume of fluid method is used to capture and track the interface in the multiphase flow. It is found that the wall conduction may change the fluid-solid interfacial thermal characteristics compared with no conduction or pure convection process. The amount of influence depends on the working fluid, nozzle size, metal thermal conductivity, metal thickness, and boundary heat flux. The conduction inside the solid wall tends to reorganize the uniform heat flux distribution at the boundary to a non-uniform heat flux distribution at the fluid-solid interface. This is mainly attributed to the conjugate effect of the solid. For a given jet Reynolds number and boundary heat flux, the conjugate heat transfer results divulge that the convective heat flux removed from the stagnation point is higher for the air jet than for the water jet. Contrary to the air jet, the effect of thermal boundary on the stagnation Nusselt number profile is negligible for the water jet. The disc material and thickness have no obvious effect on the stagnation Nusselt number profile for both air and water fluids.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".