A better compression driver? CutFEM 3D shape optimization taking viscothermal losses into account
<|reference_start|>A better compression driver? CutFEM 3D shape optimization taking viscothermal losses into account: The compression driver, the standard sound source for midrange acoustic horns, contains a cylindrical compression chamber connected to the horn throat through a system of channels known as a phase plug. The main challenge in the design of the phase plug is to avoid resonance and interference phenomena. The complexity of these phenomena makes it difficult to carry out this design task manually, particularly when the phase-plug channels are radially oriented. Therefore, we employ an algorithmic technique that combines numerical solutions of the governing equations with a gradient-based optimization algorithm that can deform the walls of the phase plug. A particular modeling challenge here is that viscothermal losses cannot be ignored, due to narrow chambers and slits in the device. Fortunately, a recently developed, accurate, but computationally inexpensive boundary-layer model is applicable. We use this model, a level-set geometry description, and the Cut Finite Element technique to avoid mesh changes when the geometry is modified by the optimization algorithm. Moreover, the shape calculus needed to compute derivatives for the optimization algorithm is carried out in the fully discrete case. Applying these techniques, the algorithm was able to successfully design the shape of a set of radially-directed phase plugs so that the final frequency response surprisingly closely matches the ideal response, derived by a lumped circuit model where wave interference effects are not accounted for. This result may serve to resuscitate the radial phase plug design, rarely used in today's commercial compression drivers.<|reference_end|>
@article{berggren2024a,
    title={A better compression driver? CutFEM 3D shape optimization taking
  viscothermal losses into account},
    author={Martin Berggren, Anders Bernland, Andr'e Massing, Daniel Noreland,
  and Eddie Wadbro},
    journal={arXiv preprint arXiv:2403.17963},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.17963},
    primaryClass={math.NA cs.NA math.OC}
}