极端风险约束下航天任务可靠性量化的案例研究
A Case Study on Quantifying Reliability under Extreme Risk Constraints in Space Missions
AI总结：
本文采用贝叶斯框架下的高斯过程建模，量化地球再入返回舱在极端载荷下的可靠性，验证成功概率达0.999999的稀有事件安全目标，展示了贝叶斯方法在风险规避环境中进行复杂不确定性量化分析的有效性。
摘要：
本文采用贝叶斯方法对用于评估稀有事件概率的计算机模拟进行不确定性量化。作为案例研究，我们评估了用于样本返回任务的地球再入返回舱的可靠性，该返回舱必须能够承受再入载荷才能完整着陆。我们的研究在贝叶斯框架下使用高斯过程建模来分析再入飞行器对运行应力的韧性。该贝叶斯框架允许对系统可靠性进行详细的概率评估，表明我们能够验证成功概率为0.999999的稀有事件的严格安全目标。研究结果强调了贝叶斯方法在计算机模拟复杂不确定性量化分析中的有效性，为风险规避环境下的计算可靠性分析提供了有价值的见解。
英文摘要：
In this paper, we employ a Bayesian approach to uncertainty quantification of computer simulations used to assess the probability of rare events. As a case study, we assess the reliability of an Earth reentry capsule for sample return missions that must be able to withstand the reentry loads in order to land intact. Our study uses Gaussian Process modeling under a Bayesian regime to analyze the reentry vehicle's resilience against operational stress. This Bayesian framework allows for a detailed probabilistic evaluation of the system's reliability, indicating our ability to verify stringent safety goals of rare events with a 0.999999 of 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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
