Aeroacoustic investigation of automotive engine cooling modules using the Lattice-Boltzmann Method
Bibliographic record
Abstract
The present study focuses on the aeroacoustic aspects of engine cooling modules used in both classical and electrical vehicles (EV). These modules aim at cooling down the engine in combustion cars and the electrical components in EVs. During the fast charging process of such vehicles, a huge amount of power is transferred to the battery in a short period of time that causes its overheating. Several fans are installed in order to cool down these components, thus multiplying the noise sources that are propagated both outside and inside the cabin and can be harmful for the pedestrians as well as the passengers. Lattice-Boltzmann simulations are performed on both the classical and the electrical module in order to understand the noise mechanisms and highlight the difference between them. The results are compared with the experimental measurements achieved in the anechoic wind tunnel at Université de Sherbrooke. Medium grid simulations have shown a good flow behavior and establishment for the operating point considered with some possibly noise sources localisation. The direct farfield noise computations also show a good overall match with the experimental data. Modal analysis performed on the electrical module shows the duct role in the acoustic response of the module to an external noise source that mimic the car environment.
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 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 | 0.000 | 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".