Triple decomposition and sparse representation for noisy pressure-sensitive paint data

Abstract

Triple decomposition is a useful analytical method for extracting the mean value, organized coherent motion, and stochastic part from a fluctuating quantity. Although the pressure-sensitive paint (PSP) method is widely used to measure the pressure distribution on a surface, the PSP data measuring near atmospheric pressure contain significant noise. Here, we perform triple decomposition of noisy PSP data. To construct phase-averaged data representing an organized coherent motion, we propose a relatively simple method based on a multi-dimensional scaling plot of the cosine similarity between each PSP datum. Then, the stochastic part is extracted by selecting phase-averaged data with an appropriate phase angle based on the similarity between the measurement and phase-averaged data. As a data-driven approach, we also reconstruct the pressure distribution based on the triple decomposition and the pressure data at sparse optimal sensor positions determined from the proper orthogonal decomposition modes of the stochastic part. The optimal sensor positions are determined as a combinatorial optimization problem and are estimated using Fujitsu computing as a service digital annealer. Based on the results obtained, the root mean square error between the pressure measured by a pressure transducer and the reconstructed pressure obtained by the proposed method is small, even when the number of modes and sensor points is small. The application of PSP measurement is expected to expand further, and the framework for calculating triple decomposition and sparse representation based on the decomposition will be useful for flow analysis.

18 pages

References in corpus (8)

• Data-Driven Sparse Sensor Placement for Reconstruction
• Physics-Inspired Optimization for Quadratic Unconstrained Problems Using a Digital Annealer
• Determinant-based Fast Greedy Sensor Selection Algorithm
• Extended-Kalman-filter-based dynamic mode decomposition for simultaneous system identification and denoising
• Data-Driven Approach for Noise Reduction in Pressure-Sensitive Paint Data Based on Modal Expansion and Time-Series Data at Optimally Placed Points
• Data-Driven Optimal Sensor Placement for High-Dimensional System Using Annealing Machine
• Optimization of Sparse Sensor Placement for Estimation of Wind Direction and Surface Pressure Distribution Using Time-Averaged Pressure-Sensitive Paint Data on Automobile Model
• Clustering Method for Time-Series Images Using Quantum-Inspired Computing Technology