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Design of Super Silent Enclosure for Diesel Genset Using Statistical Energy Analysis (SEA) Technique
Technical Paper
2019-26-0185
ISSN: 0148-7191, e-ISSN: 2688-3627
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English
Abstract
Diesel engine generators are commonly used as a power source for various industrial and residential applications. While designing diesel generator (DG) enclosures requirements of noise control, ventilation and physical protection needs to be addressed. Indian legislation requirement demands DG enclosure insertion loss (IL) to be minimum 25 dB. However for certain critical applications like hospitals, residential apartments customer demands quiet DG sets than the statutory limits. IL targets for such application ranges between 35-40 dB. The objective of this paper is to develop methodology to design ‘Super Silent’ enclosure with IL of 35 dB by Statistical Energy Analysis (SEA) approach for small capacity DG set. Major challenge was to achieve IL of 35 dB with single enclosure and making use of SEA technique for small size enclosure wherein modal densities is very less. Major airborne noise sources like engine, radiator fan and exhaust were modelled by capturing noise source test data. Structure-borne sources like panel vibrations were also modelled via vibration measurements. Noise control treatments (NCT) were sculpted using BIOT parameters. The predicted sound levels for an existing DG enclosure model were validated with its test data to ensure correctness of SEA model. Further critical noise paths identification and ranking was done through simulation. Based on results of above step, modifications were suggested on NCT, louvers, baffles, ducts and muffler to arrive at optimized enclosure. Proto for optimized enclosure was tested. Test showed IL of 35.5 dB against predicted IL of 35 dB showing very good correlation.
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Bankar, H., Yadav, P., Kandalkar, M., and Karanth, N., "Design of Super Silent Enclosure for Diesel Genset Using Statistical Energy Analysis (SEA) Technique," SAE Technical Paper 2019-26-0185, 2019, https://doi.org/10.4271/2019-26-0185.Data Sets - Support Documents
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