The application of machine learning in micrometeoroid and orbital debris impact protection and risk assessment for spacecraft

Abstract

Current spacecraft micrometeoroid and orbital debris impact risk assessments utilize semi-empirical equations to describe the protection afforded by a spacecraft component (e.g., pressure hull, critical component, etc.). These equations demand fundamentally limiting assumptions, for example of projectile shape and material, to reduce the complexity of the mechanics and material response under such extreme conditions. Machine learning (ML) approaches, however, are well suited to such high dimensionality problems and have previously been shown to provide comparable classification accuracy to state-of-the-art empirical techniques in this domain. We demonstrate that ML models can readily incorporate additional complexity beyond that currently achievable with semi-empirical models, such as the effect of thermal insulation blankets, non-aluminium projectiles, and non-spherical projectiles on failure thresholds without any notable loss of performance, compared with baseline conditions. For future micrometeoroid and orbital debris (MMOD) risk assessment codes such ML models offer the potential to incorporate the true characteristics of the MMOD environment more accurately than existing approaches.