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FrancoAngeli Journals

Articoli Scientifici

V. 48 N. 1 (2024)

Algoritmo di Beamforming coerente per la valutazione del comfort acustico interno di un veicolo

DOI
https://doi.org/10.3280/ria1-2024oa17464
Inviata
13 marzo 2024
Pubblicato
22-07-2024

Abstract

La crescente importanza dei veicoli elettrici nel mercato globale rende la riduzione del rumore aerodinamico un argomento cruciale su cui investire denaro e risorse. Per migliorare il comfort acustico di un’automobile è ora più che mai necessario identificare correttamente le sorgenti sonore aeroacustiche attorno al veicolo, molte delle quali in precedenza mascherate dal rumore del motore. Le tecniche di Beamforming sono generalmente utilizzate per localizzare le sorgenti di rumore su un piano virtuale in prossimità di un oggetto, elaborando i segnali acquisiti da uno o più array di microfoni. Non tutte le sorgenti rilevate, però, sono in grado di raggiungere l'abitacolo dell'auto e di influenzarne il comfort acustico. Sulla base di queste considerazioni, la galleria del vento Pininfarina ha sviluppato un algoritmo in grado di correlare le varie sorgenti aeroacustiche identificate dalla tecnica di Beamforming Convenzionale con il rumore misurato all’interno dell’auto. A differenza degli approcci convenzionali, il cosiddetto algoritmo di Beamforming Coerente ha rimosso con successo il contributo del rumore aerodinamico non coerente, isolando le principali sorgenti in grado di influire negativamente sul comfort dei passeggeri.

The growing importance of electric vehicles in the global market makes the reduction of wind noise a crucial point for investing money and resources. To improve vehicle acoustic comfort, it is increasingly important to identify aeroacoustic noise sources around the vehicle, many of which were previously masked by engine noise. The Beamforming techniques are widely used methods that, thanks to one or more arrays of microphones, localize the noise sources on a virtual plane close to the object. However, not all the sources highlighted infiltrate the vehicle cabin and affect its acoustic comfort. Based on these considerations, the Pininfarina Wind Tunnel developed an algorithm capable of correlating the various sound sources detected by the Conventional Beamforming technique to the noise measured inside the vehicle cabin. Unlike conventional approaches, this so-called Coherence Beamforming algorithm was successfully able to remove the uncorrelated aerodynamic noise contributions, isolating the major noise sources responsible for passenger discomfort.

 

Riferimenti bibliografici (comprensivi di DOI)

  1. K.-H. Chen, J. Johnson, U. Dietschi, B. Khalighi, Automotive Mirror Wind Noise Simulations and Wind Tunnel Measurements, in: 14th AIAA/CEAS Aeroacoustics Conference (29th AIAA Aeroacoustics Conference), American Institute of Aeronautics and Astronautics, Vancouver, British Columbia, Canada, 2008. https://doi.org/10.2514/6.2008-2906.
  2. L. Li, J. Li, B. Lu, Y. Liu, Z. Zhang, H. Cheng, Y. Zhang, H. Hou, Application of Beamforming to Side Mirror Aeroacoustic Noise Optimization, in: 2016: pp. 2016-01-0475. https://doi.org/10.4271/2016-01-0475.
  3. D. Lepley, S. Senthooran, D. Hendriana, T. Frazer, Numerical Simulations and Measurements of Mirror-Induced Wind Noise, SAE Int. J. Passeng. Cars – Mech. Syst. 2 (2009) 1550-1562. https://doi.org/10.4271/2009-01-2236.
  4. C.S. Allen, W.K. Blake, R.P. Dougherty, D. Lynch, P.T. Soderman, J.R. Underbrink, Aeroacoustic Measurements, Springer Berlin Heidelberg, Berlin, Heidelberg, 2002. https://doi.org/10.1007/978-3-662-05058-3.
  5. L. de Santana, Fundamentals of Acoustic Beamforming, (n.d.).
  6. Z. Chu, Y. Yang, Comparison of deconvolution methods for the visualization of acoustic sources based on cross-spectral imaging function beamforming, Mechanical Systems and Signal Processing 48 (2014) 404-422. https://doi.org/10.1016/j.ymssp.2014.03.012.
  7. J. Hald, Spherical Beamforming with Enhanced Dynamic Range, SAE Int. J. Passeng. Cars – Mech. Syst. 6 (2013) 1334-1341. https://doi.org/10.4271/2013-01-1977.
  8. Y. He, B. Wang, Z. Shen, Z. Yang, G. Heilmann, T. Zhang, G. Dong, Correlation Analysis of Interior and Exterior Wind Noise Sources of a Production Car Using Beamforming Techniques, in: 2017: pp. 2017-01–0449. https://doi.org/10.4271/2017-01-0449.
  9. J. Hald, H. Kuroda, T. Makihara, Y. Ishii, Mapping of contributions from car-exterior aerodynamic sources to an in-cabin reference signal using Clean-SC, (2016).
  10. J.-L. Adam, D. Ricot, C. Lambourg, A. Menoret, Correlated Beamforming Method for Relevant Aeroacoustic Sources Identification, in: 2009: pp. 2009-01-2234. https://doi.org/10.4271/2009-01-2234.
  11. S. Guidati, R. Sottek, Advanced Source Localization Techniques Using Microphone Arrays, SAE Int. J. Passeng. Cars – Mech. Syst. 4 (2011) 1241-1249. https://doi.org/10.4271/2011-01-1657.
  12. F. Uffreduzzi, A. Aquili, E. De Paola, L.G. Stoica, A. Di Marco, Beamforming Algorithm for Vehicle Cabin Acoustic Comfort Application, in: Proceedings of the 10th Convention of the European Acoustics Association Forum Acusticum 2023, European Acoustics Association, Turin, Italy, 2022: pp. 4559-4566. https://doi.org/10.61782/fa.2023.1091.
  13. J.J. Christensen, J. Hald, Beamforming, Beamforming No. 1 (2004). https://www.bksv.com/media/doc/bv0056.pdf.
  14. M. Garcia-Pedroche, G. Bennett, Aeroacoustic Noise Source Identifi cation Using Irregularly Sampled LDV Measurements Coupled with Beamforming., in: 2011. https://doi.org/10.2514/6.2011-2719.

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