Signal-to-noise ratio analysis on saw-tooth vortex generator in vehicle radiators
Bibliographic record
Abstract
Fin-and-flat tube heat exchangers are widely applied to construction vehicles due to native advantages. An elementary unit out of such one was numerically compared with the corresponding experimental validation to secure accuracy, then, saw-tooth vortex generators were introduced to the rear of the tube, and further analyzed under the same configuration. JF factors from two models were utilized to confirm the initial expectation for a higher comprehensive performance, which also encouraged the following L18(3 6 ) orthogonal test on wing width ( w w ), wing height ( s w ), blade height ( s b ), flow attack angle ( G f ), installation angle ( G i ), and saw-tooth number ( N s ) for signal-to-noise ratio and contribution rate (CR). The results stated that the numerical implementation could be capable of the following analyses with the maximum errors of 5.00% for heat transfer coefficient and 5.33% for pressure loss, and also corroborate performance enhancement with a JF increment of 29.9% in the following comparison. The CRs of G i , N s , s w , w w , G f , and s b are 30.39%, 19.61%, 15.69%, 13.73%, 10.78%, and 9.80%, respectively.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.002 |
| 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".