Characterization and analysis of granular activated carbon stacks subject to the edge-constraint effect

• Published in: Powder technology Vol. 444; p. 120051
• Main Authors: Song, Guochenhao, Mo, Zhuang, Shi, Tongyang, Bolton, J. Stuart
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.08.2024
• Subjects: acoustics; Activated carbon; Granular materials; Hierarchical porosity; Impedance tube; mathematical models; Poro-elastic material; Sound absorption; technology; Hierarchical porosity; Impedance tube; Granular materials; Sound absorption; Activated carbon; Poro-elastic material
• ISSN: 0032-5910; 1873-328X
• DOI: 10.1016/j.powtec.2024.120051

The objective of the current work was to identify the conditions under which the acoustical properties of unconsolidated stacks of granular activated carbon (GAC) can be accurately characterized based on impedance tube measurements. GAC particles are of interest since they have proven beneficial in various low-frequency applications: e.g., micro-speaker backing cavities. However, since the particles tend to stick to the sample holder wall, a time-consuming 2-D numerical model is often required to capture their acoustical physics. Here, by identifying a configuration in which the edge-constraint effect was minimized while still maintaining distinct frame resonance features, the GAC stack properties could be successfully characterized by using a more efficient 1-D model. The inferred parameters were validated by comparing predictions with measurements of stacks with different depths, diameters, and excitation levels. The proposed framework can be followed to accurately characterize GAC stack properties, thus allowing optimizations of sound packages including similar materials. [Display omitted] •Experimentally examined the edge-constraint effect: how particle-edge friction affects impedance tube measurements.•Identified conditions to minimize the edge-constraint effect.•Modified the depth-dependent modulus of granular activated carbon (GAC) stacks.•Proposed an accurate GAC stack inverse characterization framework.