Time-domain simulations of the noise propagation in porous media and its surroundings using the Discontinuous Galerkin Method
Bibliographic record
Abstract
The analysis of noise mitigation mechanisms requires efficient numerical models. Accurate simulations of the noise propagation in porous media and its interactions with the surroundings are for this purpose essential. Many modeling approaches have been implemented in a classical Finite Element (FE) framework for frequency-domain simulations. Those are accurate and well suited for the design of acoustic treatments, but they have some inherent limitations when the size of the acoustic domain is large (volume larger than 10-100 m³) and at high frequencies (above 1000 Hz). The proposed time-domain approach complements the Actran DGM software where the Linearized Euler Equations (LEE) are solved using discontinuous Galerkin method and is suited for predicting the noise propagation in complex physical conditions. Auxiliary differential equations are used to model the porous material response with a multipole decomposition. The main advantage with this model is its ability to be used by any equivalent fluid models. Functional validation of this development is shown which includes performance tests and qualification of the model at the level of precision and robustness. Particularly, an industrial test bench from Safran Aero Boosters is simulated, comprising an acoustic source and a set of baffles in a 3.6m long duct, followed by an anechoic room.
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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 | 0.000 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".