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Prediction of Air Induced Noise Levels for Automotive HVAC Systems

With the advent of electric vehicles and advance trends in power train there is an increasing demand in improving the overall cabin comfort level. This is especially true when it comes to electric vehicles as the major source of sound other than the external flows would be from the HVAC systems for...

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Bibliographic Details
Main Authors: Raut, Prashant Mohan, Chakraborty, Ardhendu, Yadav, Rahul Kumar, Arora, Maneesh
Format: Report
Language:English
Online Access:Request full text
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Summary:With the advent of electric vehicles and advance trends in power train there is an increasing demand in improving the overall cabin comfort level. This is especially true when it comes to electric vehicles as the major source of sound other than the external flows would be from the HVAC systems for the vehicle. The noise source can be further divided on structural noise and flow induced noise; the paper would be focusing on prediction of flow induced noise levels. Flow induced noise by the HVAC’s would be critical since there are significant advancements in the cabin insulations/materials from externally generated noise sources. Automotive HVAC system consist of complex flow paths, blower, flaps, ducts, and vents which are the main source of noise generation in HVAC systems. With packaging space beneath the IP been premium, changes proposed to improve the noise levels are expensive and are understood at later level/phase of product development cycle. This paper puts forward an approach to evaluate the air induced noise levels for HVAC systems/components which can be used in early phase of development cycle. Computational Aeroacoustics (CAA) is proposed to be used to predict the sound generation and propagation level for the given microphone positions. The noise source can be identified and improved in early phase of development cycle which if done in later phase would require an expensive change. The process for prediction of noise has been validated and tested with good correlations between the two approaches.
ISSN:0148-7191
2688-3627
DOI:10.4271/2024-28-0061