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人工智能赋能固体发动机设计技术的五个方向展望OA

Artificial intelligence empowerment in solid rocket motor design:Five development prospects

中文摘要英文摘要

数智时代背景下,基于经验设计、试验迭代的固体发动机(SRM)传统设计模式,正在向基于数据、模型双驱动的数智化模式演变.首先分析了数智技术应用于航天领域的研究现状及未来发展趋势,并提出了人工智能赋能固体发动机技术(AISRM)的五个未来重点发展方向,即流程智能化、符号回归、非线性映射、智能优化、生成式智能,预估了未来五个方向发展需要解决的数据小样本特征、强几何泛化性、多参数优化、智能模型精度瓶颈等四项技术难题,最后展望了AISRM 发展的三个预期效果.未来 AISRM 将引领和象征着数智时代 SRM 先进技术的发展,极大促进 SRM 研发模式的转变、SRM 设计水平与设计效率的提升、SRM 技术创新能力进步.

Against the backdrop of the digital-intelligent era,the traditional design mode of solid rocket motors(SRM)based on empirical design and trial iterations is evolving toward a digital-intelligent paradigm driven by data and model.First,the current research status and future development trends of digital-intelligent technologies applied in the aerospace field were analyzed.Building on these analysis results,five key future development directions for artificial intelligence for solid rocket motor(AISRM)technology were put forward,such as intelligent processes,symbolic regression,nonlinear mapping,intelligent optimization,and generative intelligence.Four major technical challenges to be addressed in the future development of these five directions were predicted,including small-sample data characteristics,strong geometric generalization,multi-parameter optimization,and accuracy bottlenecks in intelligent models.Finally,three expected outcomes of AISRM development were outlined.In the future,AISRM will lead and symbolize the advancement of SRM technology in the digital-intelligent era,significantly promoting the transformation of SRM development models,enhancing SRM design standards and efficiency,and boosting SRM technological innovation capabilities.

褚佑彪;王琪虎;蒙波;任重光

西安航天动力技术研究所,西安 710025西安航天动力技术研究所,西安 710025航天动力技术研究院,西安 710025||西北工业大学 航天学院,西安 710072陕西理工大学,汉中 723000

航空航天

固体火箭发动机人工智能流程智能符号回归非线性映射智能优化生成式智能

solid rocket motorartificial intelligenceprocess intelligencesymbolic regressionnonlinear mappingintelligent optimizationgenerative intelligence

《固体火箭技术》 2026 (2)

173-182,10

国家级项目.

10.7673/j.issn.1006-2793.2026.02.002

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