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Generalized eikonal approximation for fast retrieval of particle size distribution in spectral extinction technique

机译:光谱消光技术中用于粒度分布快速恢复的广义电子近似

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摘要

In retrieving particle size distribution from spectral extinction data, a critical issue is the calculation of extinction efficiency, which affects the accuracy and rapidity of the whole retrieval. The generalized eikonal approximation (GEA) method, used as an alternative to the rigorous Mie theory, is introduced for retrieval of the unparameterized shape-independent particle size distribution (PSD). To compute the extinction efficiency more efficiently, the combination of GEA method and Mie theory is adopted in this paper, which not only extends the applicable range of the approximation method but also improves the speed of the whole retrieval. Within the framework of the combined approximation method, the accuracy and limitations of the retrieval are investigated. Moreover, the retrieval time and memory requirement are also discussed. Both simulations and experimental results show that the combined approximation method can be successfully applied to retrieval of PSD when the refractive index is within the validity range. The retrieval results we present demonstrate the high reliability and stability of the method. By using this method, we find the complexity and computation time of the retrieval are significantly reduced and the memory resources can also be saved effectively, thus making this method more suitable for online particle sizing.
机译:从光谱消光数据中检索粒度分布时,一个关键问题是消光效率的计算,这会影响整个检索的准确性和速度。引入了广义的Eikonal近似(GEA)方法,作为严格的Mie理论的替代方法,用于检索非参数化形状无关的粒度分布(PSD)。为了更有效地计算消光效率,本文采用GEA方法和Mie理论相结合,不仅扩大了近似方法的适用范围,而且提高了整个检索速度。在组合近似方法的框架内,研究了检索的准确性和局限性。此外,还讨论了检索时间和存储要求。仿真和实验结果均表明,当折射率在有效范围内时,组合逼近方法可以成功地应用于PSD的检索。我们目前的检索结果证明了该方法的高可靠性和稳定性。通过使用该方法,我们发现检索的复杂性和计算时间显着降低,并且还可以有效地节省存储资源,从而使该方法更适合于在线粒子大小确定。

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