Correlation Between Wall Heat Transfer And Characteristics Of Pulsating Flow In A Rectangular Tube Toward An Automobile Exhaust System
Bibliographic record
Abstract
The objective of this study is to investigate experimentally the effect of pulsation frequency on the heat transfer characteristics and the mechanism of the pulsation flow, which is representative of the operating conditions of the engine exhaust flow.The experimental apparatus consists of a rotating disk with holes that converts steady hot air flow rate into a pulsating flow to exchange heat energy with external air.The fluid temperature is measured by thermocouples, and the wall temperature is measured by thermography.It is found that heat transfer enhancement due to pulsation does not occur at frequencies below 25 Hz, even though the velocity amplitude is large.In order to investigate the cause of this phenomenon, the flow field is measured by PIV(Particle Image Velocimetry) and the turbulent kinetic energy is evaluated.It is clarified that the turbulent kinetic energy near the wall is small at frequencies below 30 Hz, despite the large velocity amplitude.From the time series of velocity data, it was observed that the turbulence is extremely small during the acceleration phase of the fluid.As a result, the turbulent mixing during the acceleration phase is suppressed, and the time-averaged turbulent kinetic energy becomes small, which is thought to have suppressed heat transfer enhancement.This is the first attempt to experimentally link heat transfer and flow structure fluctuations in a pulsating flow, which is achieved by unsteady measurement of the flow field using PIV and calculation of the turbulent kinetic energy.
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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 | 0.000 |
| 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".