Aeroacoustic impact of heat exchanger installation in an industrial engine cooling module
Bibliographic record
Abstract
Engine cooling fan noise becomes even more difficult to predict when considering the presence of the heat exchanger upstream. This paper investigates the impact of different heat exchanger installations on the noise sources of an industrial engine cooling fan using high-fidelity lattice-Boltzmann simulations. The heat exchanger is simulated using an equivalent porous medium upstream of the fan. Four configurations are analyzed at free discharge: full cooling module (i) without and (ii) with a gap between the heat exchanger and its casing, (iii) Fan+Frame, and (iv) Fan. For the sake of comparison, configurations (iii) and (iv) are simulated by imposing a pressure difference that matches the one across the heat exchanger, to ensure that the same operating point is maintained. Results show negligible differences in the overall aerodynamic performance since the same average pressure rise is achieved. The presence of the porous medium affects the spatial distribution of the pressure field upstream of the fan, thus causing a higher tonal content compared to the configurations (iii) and (iv). Further subharmonic humps are caused by the interaction of the blades with vortex structures generated by flow separation at the tip and the casing’s support structures. The presence of a geometry transition from square to round in the casing causes inflow distortions, increasing low-frequency broadband noise.
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 | 0.000 | 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".