Complexity Management of Rocket Engines: Discrete Event Simulation of Clustered Engine Reuse

Over the past decade, the rise of reusable launch vehicles has fundamentally reshaped the economics of spaceflight. Beyond the reuse capability, the economic viability of these vehicles hinges on the efficiency of the associated refurbishment processes. Central to this challenge is the management of rocket engines. Indeed, engine lifetime is subject to several sources of randomness, ranging from manufacturing deviations to operational fluctuations. Without individual engine health monitoring, engine lifetimes would be mispredicted in 50-70% of cases, depending on the magnitude of the underlying stochastic factors. Adaptive health monitoring strategies are hence essential to guarantee system reliability while preserving the profitability benefits of reusability. To identify efficient management and health monitoring strategies for engine reuse, this work implements a Monte Carlo simulation of doghouse deformation in combustion chamber walls. As this failure mode governs the functional end-of-life of rocket engines, it provides a useful metric for tracking system health over successive flights. The study introduces a discrete event simulation of the lifecycle of a liquid-propellant rocket engine, modeling health evolution from manufacturing through flight operations and refurbishment. The paper first evaluates various health monitoring architectures for individual engines with the dual objective of maximizing prediction accuracy and minimizing operational demands. Results demonstrate that hybrid monitoring strategies, combining inline health monitoring systems and periodic manual inspections, can achieve a prediction accuracy within 1% of inspection-only approaches while reducing the number of inspections by a factor of seven. The analysis further reveals that prediction accuracy is more sensitive to operational variations than to manufacturing deviations, underscoring the need for precise operational characterization. Subsequently, the study compares three engine cluster management strategies under varied operational profiles, highlighting the trade-off between operational efficiency and resource utilization. For example, coordinating the replacement of engines approaching end-of-life, rather than servicing them one by one, incurs a 2% loss in useful life but cuts cluster-level refurbishment operations by 15%. By accounting for the distinct objectives of engine development phases, this study highlights complexity management strategies for next-generation rocket engines.