Browse by author
Lookup NU author(s): Professor Cheng Chin
Full text for this publication is not currently held within this repository. Alternative links are provided below where available.
In this paper, an approach to model the acoustic environment of different types of machinery room in an off shore platform is presented. In a machinery room where airborne noise predominates structure-borne noise, five acoustic models to compute the sound pressure level namely: Thompson, Kuttruff, SNAME method, Heerema and Hodgson and Sergeyev are compared with statistical energy analysis (SEA) with direct field (SEA-DF) and measurement results. Unlike the simple acoustic models, the SEA-DF model includes structure-borne transmission. The results show that the noise prediction using SEA-DF model with absorption coefficients estimated from T 60 measurement exhibits a smaller error of 1.4 dBA (in an engine room) and 0.7 dBA (in a pump room) on the spatial averaging noise level as compared to the simple acoustic models. The Heerema and Hodgson and Kuttruff acoustic model have a closer result to the SEA-DF. The sensitivity of SEA-DF approach when subjected to variation in absorption coefficient (not exceeding 0.2) at 1000 Hz has shown a 15 dBA impact on sound pressure level as compared to higher absorption coefficient at about 4 dB difference. The result shows the impact of noise attenuation at different absorption coefficient and the importance of having information on the mean absorption coefficient for machinery room design on off shore platform. This enable acoustics professionals to implement better hearing protection programs for off shore crews working on the off shore platform.
Author(s): Ji X, Chin CS
Publication type: Article
Publication status: Published
Journal: Acta Acustica united with Acustica
Year: 2015
Volume: 101
Issue: 6
Pages: 1234-1244
Print publication date: 01/11/2015
Online publication date: 01/11/2015
Acceptance date: 24/08/2015
ISSN (print): 1610-1928
ISSN (electronic): 1861-9959
Publisher: S. Hirzel Verlag
URL: http://dx.doi.org/10.3813/AAA.918916
DOI: 10.3813/AAA.918916
Altmetrics provided by Altmetric