Modeling the Fragmentation of Dust–Ice Clusters at the Snow Line in Protoplanetary Disksстатья
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Аннотация:On Springer Nature: https://rdcu.be/co5G7 The results of mathematical (simulation) modeling of the structure and properties of heterogeneous fractal dust–ice clusters ~0.1–10 cm in size and their disintegration into fragments at the water snow line in protoplanetary disks are presented. The main goal of this paper is to clarify the conditions under which the decay of such a cluster as a result of ice sublimation leads to the formation of large fragments comparable in mass and size with the initial cluster or, on the contrary, small and low-mass fragments. The predominance by mass of large or small fragments on the inner side of the snow line, taking into account the differences in their interaction with the gas in the disk, can have a significant effect on the localization and specificity of the formation of planetesimals. It is assumed that the clusters consist of dust aggregates of two types: one is predominantly of water ice with a small admixture of a more refractory material, the other is predominantly of a material less volatile than ice; at the same time, the sizes of aggregates of both types are tens to hundreds of times smaller than the size of the cluster. The ratios of sizes, densities and mass fractions of aggregates of both types are variable parameters. When choosing the values of these parameters, cometary data are used. We showed that large massive fragments on the inner side of the snow line can form if ice aggregates are, on average, much larger than refractory ones. In this case, massive refractory fragments are formed even at a relatively high mass fraction of water ice (≿0.3), consistent with the protosolar mass ratio of refractory and ice components ≤2, provided that the ratio of densities of refractory and ice aggregates is ≲3. If the density ratio is of the order of 10, then massive fragments are formed with a much lower ice fraction (~0.1), which, however, does not contradict cometary data.