Lumped parameter thermal model for fast voice coil temperature prediction

Villa L, Corsini C, Spatafora G, Capucci E, Mele D, Toppi R. Lumped parameter thermal model for fast voice coil temperature prediction. In: AES Conference: 2020 AES International Conference on Audio for Virtual and Augmented Reality (August 2020). Audio Engineering Society; 2020. Paper 10458. Available from: https://aes.org/publications/elibrary-page/?id=21135

@inproceedings{Villa2020_21135,
  author = {Villa, Luca and Corsini, Chiara and Spatafora, Grazia and Capucci, Emiliano and Mele, Davide and Toppi, Romolo},
  title = {{Lumped parameter thermal model for fast voice coil temperature prediction}},
  booktitle = {AES Conference: 2020 AES International Conference on Audio for Virtual and Augmented Reality (August 2020)},
  note = {Paper 10458},
  year = {2020},
  month = aug,
  publisher = {Audio Engineering Society},
  url = {https://aes.org/publications/elibrary-page/?id=21135}
}

TY  - CPAPER
TI  - Lumped parameter thermal model for fast voice coil temperature prediction
AU  - Villa, Luca
AU  - Corsini, Chiara
AU  - Spatafora, Grazia
AU  - Capucci, Emiliano
AU  - Mele, Davide
AU  - Toppi, Romolo
T2  - AES Conference: 2020 AES International Conference on Audio for Virtual and Augmented Reality (August 2020)
M1  - Paper 10458
PY  - 2020
DA  - 2020/08/06
UR  - https://aes.org/publications/elibrary-page/?id=21135
PB  - Audio Engineering Society
LA  - en
AB  - Voice coil (VC) temperature prediction is extremely important to make choices on the magnetic circuit during the pre-design and design phases. The aim of this study is to develop a model able to provide a quick response on the steady-state VC temperature. A lumped parameter thermal model describing the relevant heat transfer phenomena occurring in a working loudspeaker was developed. It was validated against measurements of VC, polar plate and magnet temperatures reached after 2 hours of sinusoidal or pink noise excitation. Differences between predicted and measured temperatures were not significant. By providing a fast and relatively accurate response, this model can be used for VC temperature prediction in the (pre-)design phase.
ER  -