光学学报, 2019, 39 (2): 0212009, 网络出版: 2019-05-10
颗粒粒径分布测量反演算法的改进 下载: 882次
An Improved Inversion Algorithm to Measure Particle Size Distribution
测量 粒径分布 改进算法 近场散射 病态问题 非负约束 measurement particle size distribution improved algorithm near-field scattering ill-posed problem non-negative constraint
摘要
综合奇异值截断法、奇异值修正法、Tikhonov正则化思想及Chahine迭代算法,提出一种改进的病态问题求解算法来测量颗粒系的粒径分布。结合Backus-Gilbert折中准则与奇异值最小原则确定了奇异截断值,采用L曲线法确定了最优正则化参数,并利用联合迭代反演法(SIRT)实现解的非负约束。模拟及实验结果表明,该算法对单、双峰分布的测量误差均小于3%,其抗噪性能、测量准确性、时效性及粒径测量范围相较其他反演算法都有明显优势。
Abstract
An improved solution algorithm of the ill-posed problem is proposed to measure the particle size distribution, which is combined with the truncated singular value decomposition(TSVD) method, the modified singular value decomposition method, the Tikhonov regularization method, and the Chahine iteration method. The singular cutoff value is determined by the Backus-Gilbert tradeoff criteria and the minimum principle of singular value. The optimal regularization parameters are determined by the L-curve method, and the simultaneous iterative reconstruction technique (SIRT) is adopted to realize the non-negative constraint of the solution. The simulation and experimental results show that the measurement errors of the single-peak and bimodal distributions are both less than 3% by the proposed algorithm. In addition, the proposed algorithm has obvious advantages superior to the other inversion algorithms in the anti-noise performance, measurement accuracy, timeliness, and measurement range of the particle size.
王晨, 张彪, 曹丽霞, 姚鸿熙, 许传龙. 颗粒粒径分布测量反演算法的改进[J]. 光学学报, 2019, 39(2): 0212009. Chen Wang, Biao Zhang, Lixia Cao, Hongxi Yao, Chuanlong Xu. An Improved Inversion Algorithm to Measure Particle Size Distribution[J]. Acta Optica Sinica, 2019, 39(2): 0212009.