Experiment-driven simplification of Johnson-Champoux-Allard-Lafarge model for fibrous materialsa)

Area of Science:

• Acoustics
• Materials Science
• Computational Physics

Background:

• Existing acoustic models like Johnson-Champoux-Allard-Lafarge (JCAL) are complex, relying on numerous unmeasurable parameters, limiting practical use and reproducibility.
• The need for simplified, computationally efficient models for predicting sound absorption in porous materials is critical for material design and optimization.

Purpose of the Study:

• To develop an experiment-driven method to simplify the Johnson-Champoux-Allard-Lafarge (JCAL) model for acoustic prediction.
• To validate the simplified JCAL model against the original model and experimental data for various fiber types.

Main Methods:

• Utilized a two-microphone impedance tube setup for experimental data acquisition.
• Applied Bayesian inference to determine non-acoustical parameters (tortuosity, airflow resistivity, characteristic lengths, thermal permeability).
• Employed regression analysis to establish a correlation between porosity and non-acoustical parameters, simplifying the JCAL model.

Main Results:

• The simplified JCAL model demonstrated good agreement with the original model for acrylic and wool fibers.
• Both models showed reliable performance for porosities between 92% and 98%, with discrepancies at 99% porosity possibly due to frame vibrations.
• The original and simplified JCAL models failed for silk fiber due to poroelastic effects.

Conclusions:

• The experiment-driven simplification successfully reduced the JCAL model to a single parameter (porosity), significantly improving computational efficiency and prediction speed.
• The simplified model enhances applicability for optimization tasks, enabling the design of materials with optimal sound absorption characteristics while considering thickness and mass constraints.