Monte-Carlo Simulation for Analyzing the Performance Variation of a Liquid Rocket Engine Using Gas-Generator Cycle

The performance of liquid rocket engines is influenced by randomness arising from various sources, making deterministic analyses imprecise. Recognizing this issue, we perform a Monte-Carlo simulation in this study to analyze the performance variation of a liquid rocket engine that uses a gas-generat...

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Bibliographic Details
Published inInternational journal of aeronautical and space sciences Vol. 26; no. 2; pp. 688 - 697
Main Authors Park, Sinyoung, Lee, Eunkwang, Lee, Minwoo
Format Journal Article
LanguageEnglish
Published Seoul The Korean Society for Aeronautical & Space Sciences (KSAS) 01.03.2025
한국항공우주학회
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ISSN2093-274X
2093-2480
DOI10.1007/s42405-024-00760-2

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Summary:The performance of liquid rocket engines is influenced by randomness arising from various sources, making deterministic analyses imprecise. Recognizing this issue, we perform a Monte-Carlo simulation in this study to analyze the performance variation of a liquid rocket engine that uses a gas-generator cycle, incorporating operational variance of the engine parameters. We take into account the randomness of total dynamic heads of fuel and oxidizer pumps, the efficiency of pumps and turbines, and the geometric aberrances of the turbine nozzle and thrust nozzle. We conduct transient analysis using a simplified one-dimensional numerical model of the engine and compute the effect of stochastic deviation of input parameters on the engine performance, specifically in terms of chamber pressure, mixture ratio, turbine inlet temperature, nozzle thrust, etc. We assess the correlation between the input parameters and the output parameters, establishing the framework for Monte-Carlo analysis of a gas-generator cycle rocket engine. The results of this study suggest that the present method can serve as an efficient framework for analyzing the performance variance of the rocket engine compared to the established technique, such as the root-sum-square variance analysis method.
ISSN:2093-274X
2093-2480
DOI:10.1007/s42405-024-00760-2