Optimization and Modeling of Sound-Absorption Properties of Natural Fibers for Acoustical Application
Bibliographic record
Abstract
Kenaf fibers have long been utilized because of their remarkable properties, such as availability, durability, and strength. Recently, they have also been used as sound-absorbing composites for noise control purposes. This paper investigates the possibility of improving the tensile strength of kenaf fibers through optimizing the alkaline treatment process. It also considers the effects of such optimization on the acoustic absorption coefficients of the samples fabricated from these treated kenaf fibers as well as the applicability of the numerical model to predict the acoustic absorption. Having employed the response surface methodology (RSM) to optimize the alkaline treatment process and achieve optimal conditions for the kenaf fibers, the scanning electron microscopy (SEM) and tensile test were used to study and compare the morphological and tensile properties of raw fibers (nonoptimal) and the fibers treated in optimal conditions. Several cylindrical samples with constant thickness and density (30 mm and 200 kg/m3) were then made of fibers treated in optimal conditions. The sound absorption coefficient, porosity, and airflow resistivity of these samples were measured based using ISO 10534–2 (impedance tube system), SEM and ASTM C423-09A, respectively. The results demonstrated that the tensile strength of optimally treated fibers increased by 182.39%. The acoustic absorption coefficients of the samples fabricated from these fibers were also higher at all frequencies (low-, mid-, and high-frequency range) compared with samples made of untreated fibers in a way that the Sound Absorption Average value of the former increased by 17.97%. Moreover, it was found that inverted Dunn and Davern model via Nelder-Mead simplex method well follows the sound absorption pattern from the experimental results in the overall frequency range. The use of multifaceted improvement approaches for natural materials such as kenaf fibers increases the usability of these materials as sustainable and eco-friendly alternatives in the engineering process of manufacturing sound-absorbing materials.
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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 | 0.001 |
| 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 | 0.001 | 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".