Compact segmented meta-liners for enhanced acoustic absorption with grazing flow
Bibliographic record
Abstract
This work demonstrates the inherent limitations of conventional acoustic liners in achieving efficient low-frequency absorption in one-dimensional transmission problems with grazing flow. We show that absorption bounds generally exist when the acoustic treatment is modeled by a uniform impedance. Additionally, flow-induced non-reciprocity makes the design of absorbers more challenging for incident waves propagating with the flow compared to those propagating against the flow. To address these challenges, we propose a segmented meta-liner and a corresponding design methodology. The meta-liner consists of perforated faceplates backed with Helmholtz resonators, which incorporate embedded tilted necks. Wiremesh is placed at the neck openings to introduce additional acoustic losses. Our design methodology combines a numerical model with experimental impedance data. This method avoids errors introduced by theoretical or empirical impedance models and simplifies geometric design for practical implementation, thereby providing robust solutions for sound absorption under grazing flow. Experimental results confirm the deep subwavelength absorption of 3D-printed samples. All designs surpass the absorption limits of uniform impedance boundaries, a common assumption in conventional liner design. Furthermore, experimental results indicate that these designs exhibit robust absorption across low flow Mach numbers between 0 and 0.2.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".