液体火箭发动机燃烧不稳定研究:数据驱动方法  

Combustion instability of liquid rocket engines:Data-driven approach

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作  者:关昱 安强 徐冠宇 汪广旭 GUAN Yu;AN Qiang;XU Guanyu;WANG Guangxu(Department of Aeronautical and Aviation Engineering,The Hong Kong Polytechnic University,Hong Kong 999077,China;National Key Laboratory of Science and Technology on Aero-Engine Aero-Thermodynamics,Research Institute of Aero-Engine,Beihang University,Beijing 102206,China;School of Aerospace Engineering,Tsinghua University,Beijing 100084,China;Xi'an Aerospace Propulsion Institute,Xi'an 710100,China)

机构地区:[1]香港理工大学航空及民航工程学系,中国香港999077 [2]北京航空航天大学航空发动机研究院,北京102206 [3]清华大学航天航空学院,北京100084 [4]西安航天动力研究所,陕西西安710100

出  处:《火箭推进》2025年第1期34-49,共16页Journal of Rocket Propulsion

基  金:国家自然科学基金(52306166)。

摘  要:燃烧不稳定依然是研发新一代液体火箭发动机亟待解决的关键问题。如何能够对其准确建模、早期预警和有效抑制,具有很高的理论研究和工程应用价值。近些年来,随着数据驱动方法在燃烧不稳定研究中的广泛应用,以往未被详细探究的复杂动力学现象和未被识别发掘的模式及特征(如复杂网络的相关属性),均被有效地提取和解读。流动-声-燃烧这三者复杂的相互作用关系在由数据驱动方法搭建的特征空间中被重新梳理,并借此提炼出相应的燃烧不稳定预示因子,为系统的设计参数和实时控制提供有效信息,有效抑制甚至完全避免燃烧不稳定问题。此外,过去几年伴随着机器学习方法的快速发展和广泛应用,相关方法被用于燃烧不稳定的早期预警研究,取得了不错的成果。针对液体火箭发动机燃烧不稳定问题,归纳总结了近期基于数据驱动方法的燃烧不稳定预测研究,尤其关注动力系统理论、复杂网络和机器学习在燃烧不稳定研究中的相关应用进展。在未来,结合日益多元化和高精度测量手段所产生的海量数据,数据驱动方法将进一步发挥其潜在价值,帮助科研和工程技术人员更深入全面地认识和理解燃烧不稳定问题,助力新型液体火箭发动机的研发。Combustion instability remains a critical technical problem in the path to developing next-generation liquid rocket engines.Improved modeling,predicting,and controlling of combustion instabilities in liquid rocket engines are of great theoretical and practical value.With the rapid and wide applications of data-driven methods in investigating combustion instabilities in recent decades,complex nonlinear dynamics,which were not addressed properly,and unidentified and revealed patterns,such as those in complex networks,can now be extracted,analyzed,and interpreted appropriately.The complicated flow-acoustics-combustion interactions are revisited in the feature space mapped from data using data-driven methods.Novel instability indicators are being developed,demonstrating practical efficacy in optimizing system design parameters and facilitating real-time control measures,thus benefiting the effective weakening and eventually the complete suppression of combustion instabilities.Through the integration of machine learning tools,notable progress has been made in the predictive capabilities for early warning of impending combustion instabilities.This review focuses on the combustion instability in liquid rocket engines,offering a temporary summary of recent advancements in early warning combustion instability using data-driven methods.It highlights the applications of dynamical system theories,complex networks,and machine learning in combustion instability research.Looking ahead,the synergistic fusion of vast datasets generated by advanced diagnostic techniques with sophisticated data-driven methods promises to unlock even greater potential.This convergence will empower researchers to deepen their understanding of combustion instabilities,transcending conventional limitations and propelling the advancement of liquid rocket engine technology to unprecedented heights.

关 键 词:燃烧不稳定 液体火箭发动机 数据驱动方法 非线性动力学 不稳定预示因子 

分 类 号:V19[航空宇航科学与技术—人机与环境工程]

 

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