Conical Statistical Optimal Near-Field Acoustic Holography with Combined Regularization

Area of Science:

• Acoustics
• Signal Processing
• Mechanical Engineering

Background:

• Current statistical optimal near-field acoustic holography (SONAH) methods lack applicability and accuracy for large conical surfaces.
• Accurate sound field reconstruction is crucial for noise monitoring and control in mechanical systems.

Purpose of the Study:

• To develop an improved SONAH method for accurate sound field reconstruction on large conical surfaces.
• To enhance the applicability and precision of near-field acoustic holography for conical geometries.

Main Methods:

• Developed conical SONAH by transforming elementary cylindrical waves to a conical spatial domain using cylinder-cone coordinates.
• Solved for superposition coefficients based on wave field superposition principles.
• Implemented a combined regularization method using truncated singular value decomposition (TSVD) and Tikhonov regularization to address ill-posed problems.

Main Results:

• The proposed conical SONAH method demonstrated improved sound field reconstruction accuracy for conical surfaces.
• The combined TSVD and Tikhonov regularization effectively handled ill-posed problems and high-frequency noise.
• Numerical and experimental studies validated the effectiveness of the developed conical SONAH approach.

Conclusions:

• The novel conical SONAH method significantly enhances sound field reconstruction for large conical surfaces.
• The combined regularization technique provides a robust solution for ill-posed inverse problems in acoustic holography.
• This research offers a reliable tool for noise monitoring and control in mechanical systems with conical components.