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Published on: June 16, 2023
Calculation of acoustic radiation modes by using spherical waves and generalized singular value decomposition.
Jiawei Liu1, Yangfan Liu2, J Stuart Bolton2
1Cummins Inc., Research and Technology, 1900 McKinley Avenue, Columbus, Indiana 47201, USA.
Evaluating acoustic radiation modes (ARMs) for complex structures is computationally intensive. This study introduces a new method using spherical harmonics and generalized singular value decomposition to significantly reduce computational effort.
Area of Science:
- Acoustics
- Computational Mechanics
- Noise Control Engineering
Background:
- Acoustic radiation modes (ARMs) are crucial for noise control due to their sound-power properties.
- Evaluating ARMs for non-regular structures is computationally demanding, typically requiring boundary integral equation (BIE) solutions via the boundary element method (BEM).
Purpose of the Study:
- To develop a computationally efficient method for evaluating acoustic radiation modes (ARMs) for non-regular structures.
- To eliminate the need for solving boundary integral equations (BIEs) in ARM evaluation.
Main Methods:
- The proposed method involves projecting spherical harmonics onto an enclosing surface.
- Generalized singular value decomposition (GSVD) is applied to the projected spherical harmonics.
- This approach bypasses the traditional BIE solution and singular value decomposition (SVD) of the radiation resistance matrix.
Main Results:
- The new procedure successfully eliminates the necessity of solving the boundary integral equation (BIE).
- Computational effort for evaluating ARMs of non-regular structures is potentially reduced significantly.
- The method provides an alternative pathway to obtain radiation modes without direct BIE computation.
Conclusions:
- The proposed method offers a computationally advantageous alternative for determining acoustic radiation modes (ARMs).
- This technique simplifies the evaluation process for complex geometries in noise control engineering.
- Further research can explore the practical implementation and validation of this GSVD-based approach.
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