Design and analysis of a car radiator fan assembly to mitigate the effect of aeroacoustic dipole noise
Bibliographic record
Abstract
Engine cooling fans contribute a major portion of the total noise generated by the different mechanisms in a car. Therefore the design aspect of the fan blades and housing need impeccable research and careful machining to limit the noise level to a bare minimum. The noise generated from the design aspect of the fan can be grouped into two categories, rotational and irrotational. The rotational domain takes into consideration the effect of turbulence and inflow distortion while the irrotational domain considers the effects of laminar boundary layer vortex shedding, blade interaction with tip clearance and the phenomena of blade stall. The objective is to analyze the acoustic behavior and response from the fan blade and housing when exposed to incoming airflow in a car. The process due to which the aeroacoustic dipole noise is generated is investigated and the parameters affecting the noise level are assessed. The flow and acoustic analysis are carried out on SOLID WORKS flow simulation and the generated data for our particular case is represented on a graphical scale against frequency. The causes behind noise generation in a fan assembly is studied and major emphasis is laid on the aerodynamic factors, which affect the noise generation in the radiator cooling fans. After validating the theoretical procedure, an attempt is made to redesign the existing fan assembly structure by adding grills and MPP dampeners and source modification is done to reduce noise levels.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".