Title

An Automated Tracking Method To Study Particle Motion In Microgravity

Abstract

We have developed an automated method to track particles in a quasi-2D tray. Here we apply this method to the data analysis of the NanoRocks experiment currently flying on the NanoRacks platform on the International Space Station. In the NanoRocks particle trays, we observe many low-velocity collisions between mm-scale particles of varying sizes and compositions. This experiment replicates the conditions in a protoplanetary disk, since many low-velocity collisions occur during the early stages of planet formation. We have developed a method to automatically detect and track these particles. We used the programming language IDL (Interactive Data Language) and the open source software package Fiji (based on ImageJ) in this tracking method. The data derived from this method match the data derived from other methods of analysis including manual tracking of individual particles and a statistical analysis. Using the particle tracks acquired by our method, we found that the peak speed of the particles is lower for particles coated in a layer of dust. We developed two methods to measure coefficient of restitution, finding that the mean coefficient of restitution, calculated using the particles' mean free paths and average velocities, was lower in trays containing JSC-1, a lunar soil simulant. These results are significant for the early clustering of planetesimals and bodies in planetary rings. This paper will describe the development of this tracking method, its application to the NanoRocks experiment, and the results gained from this method.

Publication Date

1-1-2016

Publication Title

Earth and Space 2016: Engineering for Extreme Environments - Proceedings of the 15th Biennial International Conference on Engineering, Science, Construction, and Operations in Challenging Environments

Number of Pages

48-57

Document Type

Article; Proceedings Paper

Personal Identifier

scopus

DOI Link

https://doi.org/10.1061/9780784479971.006

Socpus ID

85025695990 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/85025695990

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