Improving acoustic absorption modeling of additively manufactured microperforated panels through kriging-based approaches
Bibliographic record
Abstract
Microperforated panels (MPPs) have emerged as good acoustic absorbers, with the Maa model being commonly utilized for predicting their acoustic absorption and impedance. However, discrepancies arise when applying this model to MPPs fabricated through additive manufacturing. Additive manufacturing offers design flexibility, but it challenges traditional acoustic models by not achieving ideal geometric characteristics. For instance, non-circular perforations resulting from the additive manufacturing, deviate from the perfect circular shape assumed in the Maa model. This non-ideal geometry poses challenges in predicting MPP acoustic absorption, particularly at lower frequencies and in wideband scenarios. Microscopic examinations reveal microporosity within panel solids, adding complexities not usually accounted for by models assuming a solid structure around perforations. This study investigates the observed disparity between Maa model predictions and experimental results due to manufacturing defects, microporosity within the MPPs and panel vibration. We address these challenges by introducing Kriging, a spatial interpolation technique, to refine the absorption model. The Kriging model demonstrates remarkable agreement with experimental data, offering a more accurate representation of the acoustic performance of additively manufactured MPPs. The proposed methodology not only contributes to advancing the understanding of acoustic absorption in MPPs but is also promising for optimizing their performance in real-world applications.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".