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In-Situ Acoustic Absorption Of A Living Green Wall

In-Situ Acoustic Absorption Of A Living Green Wall

Romanova, Anna and Horoshenkov, Kirill (2018) In-Situ Acoustic Absorption Of A Living Green Wall. In: Inter-Noise 2018: 47th International Congress and Exposition on Noise Control Engineering, 26-29 August 2018, Chicago, Illinois.

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21906 ROMANOVA_In-Situ_Acoustic_Absorption_of_a_Living_Green_Wall_2018.pdf - Accepted Version

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Data on the ability of a living green wall to absorb sound in-situ is scarce. In this work a directional parametric transducer was used to project sound on the centre of a living green wall to minimise the ground reflection and scattering from it edges. The sound pressure and particle velocity in the incident and reflected sound waves were measured with an intensity probe and used to estimate the acoustic absorption coefficient. These data were also used to estimate the ability of a living wall to scatter the incident sound. It was found that a living wall system that consists of several rectangular cells with plants can support acoustic resonances at frequencies which are controlled by the cell dimension and wall thickness. Some of these resonances are reduced or disappear when the wall is treated with a plant with a relatively high leaf area density. There is evidence that in some cases plants can scatter sound coherently resulting in an apparent decrease in the absorption coefficient. These effects need to be accounted for by a refined numerical model which is yet to be developed.

Item Type: Conference or Conference Paper (Speech)
Uncontrolled Keywords: green wall, noise absorption, acoustics
Subjects: T Technology > TD Environmental technology. Sanitary engineering
Faculty / Department / Research Group: Faculty of Engineering & Science
Faculty of Engineering & Science > Department of Applied Engineering & Management
Faculty of Engineering & Science > Designing for Sustainability Research Theme
Last Modified: 20 Oct 2018 22:33
Selected for GREAT 2016: None
Selected for GREAT 2017: None
Selected for GREAT 2018: None
Selected for GREAT 2019: None

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