Heat Transfer Impact of Synthetic Jets for Air-Cooled Array of Fins
Bibliographic record
Abstract
Free convection air cooling from a vertically placed heat sink is enhanced by upward concurrent pulsated air flow generated by mesoscale synthetic jets. The cooling enhancement is experimentally studied. An enhancement factor is introduced and defined as the ratio of convection heat transfer coefficients for jet-on (enhanced convection) to jet-off (natural convection) cooling conditions. To obtain the two coefficients, heat transfer by radiation is excluded. A high-resolution infrared (IR) camera is used to capture detailed local temperature distribution on the heat sink surface under both cooling conditions. Analysis is carried out to obtain local convection heat transfer coefficients based on measured local surface temperatures. The enhancement of convectional cooling by synthetic jets can be then quantified both locally and globally for the entire heat sink. Two categories of thermal tests are conducted. First, tests are conducted with a single jet to investigate the effects of jet placement and orifice size on cooling enhancement, while multiple jets are tested to understand how cooling performance changes with the number of jets. It is found that the cooling enhancement is considerably sensitive to jet placement. Jet flow directly blowing on fins provides more significant enhancement than blowing through the channel between fins. When using one jet, the enhancement ranges from 1.6 to 1.9 times. When multiple jets are used, the heat transfer enhancement increases from 3.3 times for using three jets to 4.8 times for using five jets. However, for practical thermal designs, increasing the number of jets increases the power consumption. Hence, a new parameter, “jet impact factor (JIF),” is defined to quantify the enhancement contribution per jet. JIF is found to change with the number of jets. For example, the four-jet configuration shows higher JIF due to higher contribution per jet than both three-jet and five-jet configurations.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".