Effects of Boundary Layer and Local Volumetric Cells Refinements on Compressor Direct Noise Computation
Bibliographic record
Abstract
The use of turbochargers with downsized internal combustion engines improves road vehicles’ energy efficiency but introduces additional sound sources of strong acoustic annoyance on the turbocharger’s compressor side. In the present study, direct noise computations (DNC) are carried out on a passenger vehicle turbocharger compressor. The work focuses on assessing the influence of grid parameters on the acoustic predictions, to further advance the maturity of the acoustic modelling of such machines with complex three-dimensional features. The effect of the boundary layer mesh structure, and of the spatial resolution of the mesh, on the simulated acoustic signatures is investigated on detached eddy simulations (DES). Refinements in the core mesh are applied in areas of major acoustic production, to generate cells with sizes proportional to the local Taylor microscale values. Such an educated guess allows for quality enhancement with a smaller increase in computational costs as compared to more general overall refinements. The reflection-free simulation results are validated against experiments. The experimental data were post-processed with methods from the two-port theory to represent pure acoustic source power density for the acoustic modes, cleaned from test-domain-specific reflections. A detailed comparison between experiments and numerical simulations is carried out. As a result of this study, the most critical parameters for the numerical prediction of turbocharger noise are presented. The results can, furthermore, be used to improve the understanding of grid construction when predicting noise signature for compressor flows.
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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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".