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In multiangle elastic light scattering (MAELS) experiments, the morphology of aerosolized particles is inferred by shining collimated radiation through the aerosol and then measuring the scattered light intensity over a set of angles. In the case of soot-laden-aerosols MAELS can, in principle, be used to recover the size distribution of soot aggregates, although this involves solving an ill-posed inverse problem. This paper presents a design-of-experiment methodology for identifying the set of angles that maximizes the information content of the angular scattering measurements, thereby minimizing the ill-posedness of the underlying inverse problem. While the optimized angles highlight the physical significance of the scattering regimes, they do not improve the accuracy of size distributions reconstructed from simulated experimental data.

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In multiangle elastic light scattering (MAELS) experiments, the morphology of aerosolized particles is inferred by shining collimated radiation through the aerosol and then measuring the scattered light intensity over a set of angles. In the case of soot-laden aerosols MAELS can be used to recover, among other things, the size distribution of soot aggregates. This involves solving an ill-posed set of equations, however. While previous work focused on regularizing this inverse problem using Bayesian priors, this paper presents a design-ofexperiment methodology for identifying the set of measurement angles that minimizes its ill-posedness. The inverse problem produced by the optimal angle set requires less regularization and is less sensitive to noise, compared with two other measurement angle sets commonly used to carry out MAELS experiments.

ABSTRACT. Recovering the size distribution of aerosolized soot aggregates from multiangle elastic light scattering data requires solving an inverse problem. This paper presents a methodology that uses maximum à posteriori (MAP) inference to stabilize the inversion by introducing prior information about the size distribution of the soot aggregates. INTRODUCTIONIn most combustion processes unburned pyrolized fuel forms nanospheres called primary particles, which in turn agglomerate into polydisperse fractal soot aggregates. The impact of these aggregates on human health [1] and the environment [2] is a function of their transport properties and absorption and scattering cross-sections. Given the dependence of these attributes on aggregate morphology, especially the number of primary particles per aggregate, there is a need for instruments that quickly and accurately characterize the size distribution of aerosolized soot aggregates.