Assessment of a Hybrid Patch Transfer-Green Functions Method for Predicting the Vibroacoustic Response of Curved Systems with Attached Noise Control Treatments
Bibliographic record
Abstract
<div class="section abstract"><div class="htmlview paragraph">This article aims to assess and discuss the performances of a hybrid methodology by considering the radiation of a curved structure-cavity system with attached noise control treatments. The hybrid method uses a Patch Transfer Functions (PTF) approach to couple the standard finite element method of the curved structure and cavity with an analytical model of the sound package, i.e. Green functions based model. First, the used approach is presented. Then, the accuracy of the proposed methodology is assessed for two different curved noise control treatments, namely (i) light foam and (ii) light foam with a mass layer. The obtained results are systematically compared to three models, namely full Finite Element/Boundary Element (FEM/BEM) strategies, and to two sub-structuring approaches where the sound package is modeled by (i) a locally reacting model and (ii) FEM. It is shown that the proposed method predicts accurately and efficiently the dynamic behavior of curved trimmed vibroacoustic systems. Moreover, such hybrid approach is suitable for speeding up and facilitating the integration of acoustic treatments, especially at the early stage of the design process where several configurations of the acoustic treatments are tested with the same structure and cavity.</div></div>
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".