Vibro-Acoustic Characterization and Optimization of Periodic Cellular Material Structures (PCMS) for NVH Applications
Bibliographic record
Abstract
NVH (Noise, vibration, and harshness) performance has been identified as having a significant influence in the purchase considerations of most automobile purchasers. Periodic cellular material structures (PCMS) are recently introduced multi-functional structures, commonly in sandwich form, that facilitate a wide variety of engineering purposes. Although literature concerning many PCMS properties is abundant, information about their vibration and acoustic responses is scanty, to the best knowledge of the present authors. This article documents a basic investigation of the vibration and acoustic behaviors of some PCMS through practical, analytical and numerical approaches, in order to evaluate the possibilities for minimizing, the transmission of noise and vibration. Some of these materials were made and investigated over frequency ranges which include several commonly-encountered vibration and acoustic frequencies of automotive and some other structures. Observations from this work are therefore expected to contribute towards design inputs to obtain better performances. A novel investigation of the effects upon vibration response of even slight inaccuracies of cutting out samples from larger blanks of such materials has also been made.
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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".