基于模态展开和最佳点处时序数据的压敏漆数据降噪方法
Data-Driven Approach for Noise Reduction in Pressure-Sensitive Paint Data Based on Modal Expansion and Time-Series Data at Optimally Placed Points
摘要：
我们提出了一种基于模态展开的非稳态压敏漆（PSP）数据降噪方法，模态展开的系数由时间序列数据在最佳位置点确定。在这项研究中，适当的正交分解（POD）模式从时间序列PSP数据计算作为一个模态基础。在POD模态的基础上，利用传感器优化技术对能有效反映压力分布特征的点进行优化配置。然后，通过最小化时间序列压力数据与最佳点处的重构压力之间的差来确定POD模式的时变系数向量。这里，假设系数向量是稀疏向量。所提出的方法的优点是一个独立的方法，而现有的方法使用其他数据，如压力抽头数据的噪声的减少。作为示范，我们应用所提出的方法对PSP数据测量卡门涡街后面的方柱。重建的压力数据与压力传感器独立测量的压力数据吻合得很好。
英文摘要：
We propose a noise reduction method for unsteady pressure-sensitive paint (PSP) data based on modal expansion, the coefficients of which are determined from time-series data at optimally placed points. In this study, the proper orthogonal decomposition (POD) mode calculated from the time-series PSP data is used as a modal basis. Based on the POD modes, the points that effectively represent the features of the pressure distribution are optimally placed by the sensor optimization technique. Then, the time-dependent coefficient vector of the POD modes is determined by minimizing the difference between the time-series pressure data and the reconstructed pressure at the optimal points. Here, the coefficient vector is assumed to be a sparse vector. The advantage of the proposed method is a self-contained method, while existing methods use other data such as pressure tap data for the reduction of the noise. As a demonstration, we applied the proposed method to the PSP data measuring the Kármán vortex street behind a square cylinder. The reconstructed pressure data is agreed very well with the pressures independently measured by pressure transducers.
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