Suitability Assessment of an Uncalibrated Body Force Based Fan Modeling Approach to Predict Automotive Underhood Airflows
Bibliographic record
Abstract
<div class="section abstract"><div class="htmlview paragraph">The automotive fan is a critical component of the cooling module, providing the majority of the cooling airflow over the heat exchangers and to underbody components at low speed, idle, and key-off conditions. Accurately predicting the performance of the automotive cooling fan is critical for sizing heat exchangers and ensuring that underhood and underbody components remain below target temperatures. This is normally done with computational fluid dynamics, but in a full-vehicle simulation it is impractical to model the rotation of the fan blades using a sliding mesh approach. Thus, simplified models which capture the fan behavior are employed. In this paper, a body force-type fan modeling approach is adopted and assessed. Many industrial fan models are calibrated based on experiments or higher-fidelity simulations. This can slow the design process. The approach employed eliminates this step, requiring only fan geometry information and no a-priori performance data. An existing body force modeling approach is used. It has been shown to be suitable for fans of the type typically used in automotive cooling systems. The model is analyzed and validated against computations including the blades. The model is then applied to simulations of the flow around and through an entire vehicle at a variety of speeds. The model predicts the flow rate through the radiator to within 8% of the experimentally-measured value at idle. At high vehicle speed, the accuracy improves to 1%. The accuracy of the uncalibrated model in predicting the radiator flow rate is comparable to the current best-practice calibrated fan modeling techniques used in the industry. The impact of the findings is a reduction in the overall effort involved in simulating under-hood and underbody flows.</div></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 | 0.001 |
| 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 | 0.001 |
| 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".