Application of a Flow Optimizer in a Limited Space to Increase Series Fan Performance
Bibliographic record
Abstract
The performance increase of cooling for a 1U SunFire 4100 server through the introduction of multiple flow optimizers is investigated in this article. We found that the power consumption of cooling fans could be decreased by 17–33% depending on operating conditions. The use of flow optimizers was found to reduce noise produced by cooling fans by at least 5.3 dB(A). We also discuss the use of the increased performance to increase thermal head room by increasing the flow rate of cooling air by 6.5–20.4%. In addition, we found that a primary fan used with an exit stator allowed the overall fan module length to be reduced without a loss in performance. Reducing the length allowed the flow optimizers to fit into the standard 56-mm space previously occupied by the original series fans. CFD analysis was performed to better understand the effect of this stator on airflow.
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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 | 0.000 |
| 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 | 0.001 |
| 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".