Noise identification of confined orifice flow from sparse experimental data using a pressure decomposition framework

Area of Science:

• Acoustics
• Fluid Dynamics
• Signal Processing

Background:

• Flow-induced noise in lithography poses challenges for precision manufacturing.
• Sparse acoustic data often complicates the identification of noise sources.
• Decoupling hydrodynamic and acoustic components is crucial for noise mitigation.

Purpose of the Study:

• To propose a pressure decomposition framework for separating hydrodynamic and acoustic signals.
• To identify flow-induced noise in confined orifices relevant to lithography.
• To analyze the production mechanisms of pressure pulsations.

Main Methods:

• Developed a three-step framework: peak detection, mode decomposition, and component identification.
• Utilized spectral analysis and spectral proper orthogonal decomposition (SPOD).
• Employed wavenumber-frequency spectrum analysis and solved eigenvalue problems of linearized Navier-Stokes equations.

Main Results:

• Successfully reconstructed coupled hydrodynamic and acoustic pressures into modal representations.
• Identified four characteristic zones in pressure pulsations, with acoustic components dominant at low-mid frequencies.
• Revealed that decomposed sound pressure has a low attenuation factor, enabling long-distance propagation.

Conclusions:

• The proposed framework effectively decouples hydrodynamic and acoustic components from sparse data.
• Acoustic components are linked to large-scale vortex structures and propagate with minimal loss.
• The study provides insights into noise generation mechanisms in lithography systems.