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首页> 外文期刊>Journal of Spacecraft and Rockets >Spacecraft Reliability-Based Design Optimization Under Uncertainty Including Discrete Variables
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Spacecraft Reliability-Based Design Optimization Under Uncertainty Including Discrete Variables

机译：不确定性下基于航天器可靠性的离散变量优化设计

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摘要

High reliability is a primary design goal in commercial communication satellite systems because they require large capital investment and are inaccessible after launch. Spacecraft system reliability is typically computed using standard parallel-series combination techniques based upon component and subsystem failure rates provided by suppliers. Component failure rates are empirically determined and, as such, are nondeterministic parameters. Treating these failure rates as uncertain parameters in spacecraft design may avoid unnecessary high redundancy. However, probabilistic methods in reliability evaluation are often ignored because handling uncertain parameters associated with discrete or categorical design variables, such as technology choices and redundancy levels, requires computationally expensive sampling techniques. The computational cost of optimization approaches becomes prohibitive when considering discrete technology and redundancy choices as variables. This work presents a genetic algorithm with Monte Carlo sampling for probabilistic reliability-based design optimization of satellite systems. In this approach, confidence-level constraints ensure that system reliability requirements are met with high probability. The genetic algorithm-Monte Carlo sampling approach is compared to a deterministic margin-based approach that enforces margins or safety factors on the reliability of individual components. The comparison shows that the genetic algorithm-Monte Carlo sampling approach produces satellite designs that have low launch mass (a surrogate for cost) while achieving reliability requirements at specified high confidence levels, while the genetic algorithm-deterministic margin-based approach produces heavy satellite designs with excessive redundancy. Based on this work, extensions of a genetic algorithm-based approach for discrete optimization under uncertainty that may require less computational effort appear possible.
机译：高可靠性是商业通信卫星系统的主要设计目标，因为它们需要大量的资金投入并且在发射后无法使用。航天器系统的可靠性通常是根据供应商提供的组件和子系统故障率使用标准的并行串联组合技术来计算的。组件故障率是凭经验确定的，因此是不确定的参数。在航天器设计中将这些故障率视为不确定参数可以避免不必要的高冗余度。但是，可靠性评估中的概率方法常常被忽略，因为处理与离散或分类设计变量相关的不确定参数（例如技术选择和冗余级别）需要计算上昂贵的采样技术。当将离散技术和冗余选择视为变量时，优化方法的计算成本变得过高。这项工作提出了一种基于蒙特卡洛采样的遗传算法，用于基于概率可靠性的卫星系统设计优化。在这种方法中，置信度级别约束可确保以高概率满足系统可靠性要求。将遗传算法-蒙特卡洛采样方法与基于确定性余量的方法进行了比较，该方法对单个组件的可靠性施加了余量或安全系数。比较表明，遗传算法-蒙特卡洛采样方法产生的卫星设计发射质量低（成本的替代物），同时在指定的高置信度下达到可靠性要求，而遗传算法基于确定性余量的方法产生的卫星设计繁重具有过多的冗余。基于这项工作，可以在不确定性下扩展基于遗传算法的离散优化方法，这种方法可能需要较少的计算工作。

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