基于GO-FLOW的可重复使用火箭发动机可靠性评估

    Reliability Assessment of Reusable Rocket Engines Based on the GO-FLOW Method

    • 摘要: 为解决可重复使用火箭发动机在多阶段任务中可靠性评估的难题,提升任务安全性和经济性,针对发动机在点火发射、最大动压、级间分离、二级入轨和着陆捕获等关键阶段的动态特性,采用GO-FLOW方法构建系统可靠性模型。通过抽象物理部件为操作符并结合信号传递机制,分析推进剂供应、燃烧和控制三大子系统的多阶段演化路径。研究提出性能一致性指标,计算可知系统最终性能一致性达80.6%,表明其具有较高可靠性;利用MATLAB进行灵敏度分析,将关键操作符参数从0.95增至1.00,结果显示,喷嘴对系统性能一致性的影响最高达14.9%,推进剂贮箱、燃料管道、控制阀、燃烧室分别贡献11.7%、8.1%、7.1%、14.6%,为系统薄弱环节提供量化依据;在5个时间点起关键作用的部件分别为点火器、控制阀、燃烧室、万向传动装置与涡轮泵。相比传统可靠性分析方法,GO-FLOW方法在动态建模和计算效率上具有显著优势。本研究为可重复使用火箭发动机的设计优化和任务规划提供了全新的思路。

       

      Abstract: To enhance mission safety and cost-effectiveness, this study addresses the challenge of reliability assessment for reusable rocket engines across multiple mission phases. Key operational stages—ignition and launch, maximum dynamic pressure, stage separation, secondary orbit insertion, and landing capture—are analyzed using the GO-FLOW methodology to construct a comprehensive system reliability model. Physical components are abstracted into operators, with signal transmission mechanisms employed to evaluate the dynamic evolution of propellant supply, combustion, and control subsystems across these phases. A performance consistency metric is proposed, with calculations demonstrating a system-wide performance consistency of 80.6%, indicative of high reliability. Sensitivity analysis, performed using MATLAB, involved adjusting the key operator parameter from 0.95 to 1.00. The results identified the nozzle as the most influential component, contributing up to 14.9% to system performance consistency, followed by the propellant tank (11.7%), fuel pipeline (8.1%), control valve (7.1%), and combustion chamber (14.6%). These findings provide a quantitative foundation for pinpointing critical vulnerabilities in the system. Key components impacting reliability at the five mission phases include igniters, control valves, combustion chambers, gimbal mechanisms, and turbopumps. Compared to conventional reliability analysis methods, the GO-FLOW approach demonstrates superior dynamic modeling capabilities and computational efficiency. This study offers novel insights for the design and operational planning of reusable rocket engines, advancing their reliability and performance in multi-phase missions.

       

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