基于CFD数据库及NMF的流场重构成像方法

Flow Field Reconstruction Tomography Method Based on CFD Database and NMF

摘要: 文章提出了一种基于计算流体动力学(CFD)和非负矩阵因子分解(NMF)的新型过程层析成像(PT)方法，用于重构流场。通过CFD模拟建立CFD样本数据库，并利用NMF从样本数据库提取样本基矩阵。然后，利用基矩阵可通过少量传感器测量数据逆解得到样本矩阵，从而减少重构所需的采样点，实现流场的快速降维重构。由重构结果可知，该方法可以精确地重构流场，尤其是不存在较大数量级差异的过程参数；与其他重构方法相比，本方法避免了多相流检测过程中复杂敏感场的计算，而且相较于纯CFD模拟方法和插值方法，重构所需的时间大幅缩减。

Abstract: This paper proposes a new process tomography (PT) method based on computational fluid dynamics (CFD) and non-negative matrix factorization (NMF) for reconstructing flow fields. The CFD sample database is established by CFD simulation, and the sample base matrix is extracted from the sample database by using NMF. Then, based on the basis matrix, the sample matrix can be obtained by inversely solving from a small amount of sensor measurement data, thereby reducing the sampling points required for reconstruction and realizing rapid dimensional reconstruction of the flow field. It can be seen from the reconstruction results that the method can accurately reconstruct the flow field, especially the process parameters without large order of magnitude difference; compared with other multi-phase flow detection methods, the method avoids the complex calculations of sensitive field in the multi-phase flow detection process. Meanwhile, compared to simple CFD simulation methods and interpolation methods, the time required for reconstruction is greatly reduced.