Low Velocity Collisions and the Growth of Planetesimals View Full Text


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Chapter Info

DATE

2000

AUTHORS

Willy Benz

ABSTRACT

The physics of low velocity collisions (5 m/s to 40 m/s) between basalt bodies ranging in size from 1 m to 10 km is studied in an effort to investigate the early phases of planetesimal accretions. To assess the importance of the internal structure of planetesimals on the outcome of the collisions, we model them either as solid spheres or as rubble piles with a filling factor of 0.5.The collisions are simulated using a three dimensional Smooth Particle Hydrodynamics (SPH) code that incorporates the combined effects of material strength and a brittle fragmentation model. This approach allows the determination not only of the mass of the largest fragments surviving the collisions but also their dynamical characteristics.We find that low velocity collisions are for equal incoming kinetic energy per gram of target material considerably more efficient in destroying and dispersing bodies than their high velocity counterparts. Furthermore, planetesimals modeled as rubble piles are found to be characterized by a disruption threshold about 5 times smaller than solid bodies. Both results are a consequence of a more efficient momentum transfer between projectile and fragments in collisions involving bodies of comparable sizes.Size and shape dependent gas drag is shown to provide relative collision velocities between similar meter-sized objects well in excess of the critical disruption threshold of either rubble piles or solid bodies. Unless accretion can proceed avoiding collisions between bodies of similar masses, the relative weakness of bodies in this size range creates a serious bottleneck for planetesimal growth. More... »

PAGES

279-294

Book

TITLE

From Dust to Terrestrial Planets

ISBN

978-94-010-5807-0
978-94-011-4146-8

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/978-94-011-4146-8_18

DOI

http://dx.doi.org/10.1007/978-94-011-4146-8_18

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1045266270


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