What is it about?

A noise reduction method for pressure-sensitive paint (PSP) data is proposed. The method is based on modal expansion, the coefficients of which are determined from time-series data at optimally placed points and sparse modeling. PSP is an optical method for measuring pressure distribution on a model surface in air flows.

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Why is it important?

The method can extract small signals from low signal-to-noise ratio image. While existing methods uses the data from other sensors, the proposed method is a self-contained method. The coefficients of each mode can be statistically determined based on sparse modeling.


This modal-based approach will be applicable not only to PSP data but other types of experimental data.

Yu Matsuda
Waseda Daigaku

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This page is a summary of: Data-driven approach for noise reduction in pressure-sensitive paint data based on modal expansion and time-series data at optimally placed points, Physics of Fluids, July 2021, American Institute of Physics,
DOI: 10.1063/5.0049071.
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