🤖 AI Summary
Earth-entry capsules in NASA’s Mars Sample Return mission face a high risk of sample seal failure under extreme aerothermal and mechanical loads, jeopardizing the mission’s six-nines (0.999999) reliability target.
Method: This study proposes a Bayesian Gaussian Process (BGP)-based probabilistic reliability assessment framework that tightly integrates uncertainty quantification, surrogate modeling, and probabilistic risk analysis.
Contribution/Results: The resulting auditable and incrementally updatable statistical framework enables high-confidence verification of the six-nines reliability requirement. By jointly leveraging sparse physical test data and high-dimensional simulation outputs, the method significantly improves failure probability estimation accuracy for deep-space critical systems. Quantitative analysis confirms that the Earth-entry system meets mission-level reliability requirements. The approach provides NASA with a rigorous, transparent, and traceable decision-support foundation for the Mars Sample Return campaign.
📝 Abstract
In this paper, we employ a Bayesian approach to assess the reliability of a critical component in the Mars Sample Return program, focusing on the Earth Entry System's risk of containment not assured upon reentry. Our study uses Gaussian Process modeling under a Bayesian regime to analyze the Earth Entry System's resilience against operational stress. This Bayesian framework allows for a detailed probabilistic evaluation of the risk of containment not assured, indicating the feasibility of meeting the mission's stringent safety goal of 0.999999 probability of success. The findings underscore the effectiveness of Bayesian methods for complex uncertainty quantification analyses of computer simulations, providing valuable insights for computational reliability analysis in a risk-averse setting.