Acoustical characteristics of oil palm mesocarp fibres - Abd Latif, Hanif (2016)
Natural fibres are availability biomass, plentiful in agricultural waste and ecofriendly materials. Currently, it is become the major reasons for emerging renewable materials in sustainable technology. Therefore, this study discusses the acoustical characteristics of agro-waste biomass fibres from oil palm Mesocarp as fibrous acoustic material. Incorporated polyurethane (PU) had improved the sound absorption coefficient (SAC), especially at low frequency range. The oil palm Mesocarp fibres were mixed with four different percentages of PU, namely 10%, 20%, 30% and 40%. The measurement of SAC was done using an impedance tube method (ITM). Moreover, there were two analytical models which are DelanyBazley model and Johnson-Champoux-Allard model to validate the experimental outcomes. There were several internal characteristics of the prepared samples investigated namely flow resistivity, porosity, tortuosity, characteristic lengths, fibre diameter, air gaps and thickness of sample. The flow resistivity, tortuosity, fibre diameter, density of samples and characteristic lengths tend to increase with percentage of PU binder. But, porosity was slightly decrease by adding more binder into the sample. This study confirms that by increasing the thickness of samples, the SAC were improved. Air gaps had great influence in adjusting amount of low frequency range and moved the peaks toward lower frequency. The internal characteristics have a positive and significant effect on Noise Reduction Coefficient (NRC). The sample with 10% PU binder showed the greatest sound absorption performance in most of low to mid frequency range and demonstrated highest value of NRC of 0.66. Besides, sample with 20% PU binder demonstrated optimum SAC which is very close to 1 at 1000 Hz. There is still space to improve the performance of the Delany-Bazley Model, especially for natural fibre. Besides, the JohnsonChampoux-Allard model gives similar pattern of graph to ITM results and the model predicts very well with better fit to acoustic behaviour.