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Acoustic sensing and noise identification of a heating, ventilation and air conditioning unit: industrial case study

Grigg, Stephen, Al-Shibaany, Zeyad Yousif Abdoon, Pearson, Matthew Robert ORCID:, Pullin, Rhys ORCID: and Calderbank, Paul 2021. Acoustic sensing and noise identification of a heating, ventilation and air conditioning unit: industrial case study. Applied Sciences 11 (21) , 9811. 10.3390/app11219811

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Reducing the noise and improving the sound quality of vehicles’ interior space is one of the challenges to enhance passengers’ experience. This is an ever-growing issue as entirely electric cars are becoming commonplace, making previously unnoticed noise a significant problem. Heating, Ventilation and Air Conditioning (HVAC) units are a major noise source in a vehicle’s interior space, yet automotive manufacturers only give a maximum dB specification to HVAC unit manufactures. Problematic noise is only typically identified once the unit is within the vehicle at the late stages of a project. Psychoacoustics is the study of human perception to sound, allowing unpleasant noise to be identified within recorded data. Within this study, an industrial prototype HVAC unit was analysed using a 96-channel acoustic camera capable of isolating and locating noise sources from the unit using beamforming. In addition to identifying the location of noise sources, several psychoacoustic metrics were used, such as sharpness and loudness, to identify undesirable noise within an extensive data set due to the vast range of test configurations. Testing was conducted to analyse the unit. Within the initial testing, an ‘annoying’ sound was identified at a particular motor RPM, and this was located using the camera to an area which indicated that it was a result of structural resonance. In addition, present was a high-frequency source which could not be located accurately. The results of this testing enable modifications to the unit to be made early in its’ development, either structurally to alter the resonance of the unit or within the settings to ensure certain RPMs are avoided.

Item Type: Article
Date Type: Publication
Status: Published
Schools: Engineering
Additional Information: This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// 4.0/
Publisher: MDPI
ISSN: 2076-3417
Date of First Compliant Deposit: 10 November 2021
Date of Acceptance: 12 October 2021
Last Modified: 12 May 2023 17:24

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