Evaluation of the Thermofluid Performance of an Automotive Engine Cooling-Fan System Motor
Bibliographic record
Abstract
Experimental tests and computational fluid dynamics (CFD) simulations using the commercial code FLUENT were carried out to investigate the effects of the fan support hub geometry on the component heat transfer and cooling air flow through a simplified model of an electric motor for an automotive cooling-fan system, since little is known about the thermofluid dynamics of such machines. It has been found that the presence of radial ribs on the fan hub has a significant effect on drawing cooling air through the motor, particularly at lower air flowrates, regardless of the rotational speed. In addition, the rotational speed, hub diameter, fin height, and rib width are important parameters for inducing flow inside the hub while the tip gap and hub depth are not as influential. Increasing the number of ribs or fins has little impact on the performance of the hub. Good agreement was found between the experimental and predicted temperatures from heat transfer simulations of the motor for representative underhood environmental conditions. The present work shows that a valuable CFD tool can be developed to predict the temperature distribution inside the motor and offers a guide to the methodology whereby design modifications may be made to improve motor performance for a given application.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".