Prediction of the Low-Reynolds Number Flows around the Airfoil and Bluff Body Components of an Automotive Cooling Fan Module
Bibliographic record
Abstract
<div class="htmlview paragraph">A numerical model to assess the aerodynamic performance of typical automotive cooling fan stators or support arms is presented. Under real operating conditions, the flow over stators or support arms resembles bluff body flow. Hence, the time and spatial resolution are selected based on previous numerical simulations for the flow around a normal flat plate. Turbulence modeling is based on the Unsteady Reynolds-averaged Navier-Stokes (URANS) equations retaining the Boussinesq eddy-viscosity hypothesis. The ability of the URANS model to predict the periodic nature of the flow is demonstrated here. Furthermore, comparison with experimental data shows that the proposed numerical model can predict the global flow parameters, namely lift and drag within good accuracy.</div> <div class="htmlview paragraph">As a first attempt to assess the interaction of the cooling fan with its system environment, the proposed numerical model is expanded to model the interaction of the fan blades with the adjacent stators or support arms. First, the boundary conditions are modified by prescribing the wake generated by the fan blade as the inlet conditions to a cascade of stators and support arms. These conditions are based on Laser Doppler Velocimetry (LDV) data taken in the flow downstream of an automotive cooling fan. The numerically calculated lift and drag forces acting on stators or support arms are then used to estimate the pressure change across the automotive cooling fan shroud. The potential efficiency gain associated with the use of stators is demonstrated.</div>
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".