Low-frequency vibro-acoustic response of an optimized fiber-reinforced graphite truss sandwich panel filled with wood-based material
Bibliographic record
Abstract
Conventional metallic sandwich panels are widely used for noise control owing to their good noise control capabilities and excellent mechanical strength-to-weight ratio property. Furthermore, sound-absorbing products consisting of glass or mineral fiber materials are generally filled into the sandwich structures to lower the vibration response in resonance frequency and to enhance the structural noise attenuation capacity. In the present study, a fiber-reinforced graphite material is used as an alternative to its metallic counterparts. Moreover, a wood-based renewable absorption material is used as the absorption material and is filled into the sandwich structural core. The vibro-acoustic characteristics of the panel with such a design are numerically investigated using Actran. The findings of the research indicate that the proposed sandwich structure achieves advanced low-frequency noise control performance in comparison with other conventional metallic sandwich panels. Approximately 7 dB increase in sound transmission loss in the audible-frequency range is achieved in addition to a reduced panel weight and more stable vibration with reduced amplitude. The existing data available in the literature are employed for validating and illustrating the accuracy and reliability of the proposed approach.
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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 | 0.000 | 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".