基于Daubechies小波尺度函数的炉膛空气动力场声学层析重建方法

Research on Reconstruction of Aerodynamic Field in Furnace by Acoustic Tomography Based on Scaling Function of Daubechies Wavelet

声学层析测量是一种先进、有前景的炉内空气动力场非接触重建方法,其中可靠、优化的重建模型算法是关键。小波尺度函数包含所确定尺度以下所有信号频率信息,便于有效重构包含复杂频率成分的场对象,鉴于此,该文提出以小波尺度函数为基对待测空间场层析重建的方法。其采用具有紧支撑、正交特点的Daubechies小波以获得更好的场重建辨识度;对几何参数进行归一化处理以适应不同炉膛断面尺寸的场重建;采用梯度下降法求解线性方程组,得到稳定可靠的结果。通过构建不同典型仿真断面流场并重建,该方法均得到与原设定场良好的一致性,从而证明所提出场重建方法的有效性与可靠性。

Acoustic tomography is an advanced and promising approach to reconstruct the aerodynamic field in furnace non-intrusively.A reliable and optimal reconstruction algorithm is critical.In this paper,a novel acoustic tomographic algorithm for 2-D aerodynamic field in furnace is proposed,which originally takes a set of wavelet scaling functions as basis to reconstruct the spatial field to be measured.A scaling function has intriguing characteristics to contain information of all signal frequencies beneath the defined scale,so that it is expected to effectively reconstruct an objective field with complex spatial frequencies.Nevertheless,Daubechies wavelet is applied to construct the 2-D scaling function basis,for the advantage of compact support,orthogonality etc.,so as to achieve better identification of reconstruction.On the other hand,the gradient descent method is adopted to solve the linear equation sets in the model.The geometric parameters related to the model are normalized to fit for different size or aspect ratio of flow field to be measured.By reconstructing different typical numerical phantoms of cross-sectional flow fields in a furnace,the proposed acoustic reconstruction model is essentially proved to be valid and robust.