The Effect of Blade Count on Body Force Model Performance for Axial Fans
Bibliographic record
Abstract
Abstract Body force models enable inexpensive numerical simulations of turbomachinery. The approach replaces the blades with sources of momentum/energy. Such models capture a “smeared out” version of the blades’ effect on the flow, reducing computational cost. The body force model used in this paper has been widely used in aircraft engine applications. Its implementation for low speed, low solidity (few blades) turbomachines, such as automotive cooling fans, enables predictions of cooling flows and component temperatures without calibrated fan curves. Automotive cooling fans tend to have less than 10 blades, which is approximately 50% of blade counts for modern jet engine fans. The effect that has on the body force model predictions is unknown, and the objective of this paper is to quantify how varying blade count affects the accuracy of the predictions for both uniform and non-uniform inflow. The key findings are that reductions in blade metal blockage combined with spanwise flow redistribution drives the body force model to more accurately predict work coefficient as the blade count decreases and that reducing the number of blades is found to have negligible impacts on upstream influence and distortion transfer in non-uniform inflow until extremely low blade counts (such as 2) are applied.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".