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尾旋——一种高速自旋跌落的极端飞行情景,曾让全球超过90% 的战斗机大迎角事故酿成悲剧。如今,西北工业大学航空学院张伟伟课题组联合沈阳飞机设计研究所相关研究团队,用人工智能技术为这一世界级难题提供了中国解决方案。通过创新构建的增量叠加神经网络模型,战斗机尾旋气动力预测精度实现跨越式提升,关键参数预测误差降幅达77%,为破解“死亡螺旋”密码提供了新的解决思路。

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    尾旋——一种高速自旋跌落的极端飞行情景,曾让全球超过90% 的战斗机大迎角事故酿成悲剧。如今,西北工业大学航空学院张伟伟课题组联合沈阳飞机设计研究所相关研究团队,用人工智能技术为这一世界级难题提供了中国解决方案。通过创新构建的增量叠加神经网络模型,战斗机尾旋气动力预测精度实现跨越式提升,关键参数预测误差降幅达77%,为破解“死亡螺旋”密码提供了新的解决思路。

    . AI神经网络为战斗机飞行员提供保障. 科技导报, 2025 , 43 (11) : 15 -12 .
    . Science & Technology Review, 2025 , 43 (11) : 15 -12 .
    尾旋——一种高速自旋跌落的极端飞行情景,曾让全球超过90% 的战斗机大迎角事故酿成悲剧。如今,西北工业大学航空学院张伟伟课题组联合沈阳飞机设计研究所相关研究团队,用人工智能技术为这一世界级难题提供了中国解决方案。通过创新构建的增量叠加神经网络模型,战斗机尾旋气动力预测精度实现跨越式提升,关键参数预测误差降幅达77%,为破解“死亡螺旋”密码提供了新的解决思路。
    当飞机失速进入尾旋时,机体在紊乱的旋转流场中急剧俯仰、滚转、偏航,气动力呈强耦合非线性——传统基于“小扰动假设”的工程模型基本失灵。即便进行昂贵的风洞和飞行试验,也只能获得零散数据,难以拼成完整的动力学拼图。团队开发的神经网络模型,将传统气动数据库里关于舵面偏转的增量信息,与深度神经网络(DNN)强大的函数拟合能力“拼接”在一起,对战斗机尾旋过程中的非定常气动力矩进行实时预报。结果显示,三轴气动力矩平均预测误差从传统方法的水平骤降77%,稳定尾旋周期预测误差从9.8% 降至6.5%,意味着仿真系统能更精准预测战机尾旋状态。
    该研究成果为未来飞行器在复杂状态下的气动力模型构建与高精度飞行仿真提供了可行的技术路线,助力飞行器设计智能化。
    (来源于中国科技期刊卓越行动计划入选期刊:《哈尔滨工业大学学报》,2025,57(4))

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    2种不同金属材料的力学参数

    Family
    属数
    Number of
    genus
    种数
    Number of
    species
    占总种数比例
    Percentage of
    total species (%)

    Genus
    种数
    Number of
    species
    占总种数比例
    Percentage of total
    species (%)
    鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
    小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
    多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
    红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
    小菇属 Mycena 11 5.26
    光柄菇属 Pluteus 5 2.39
    红菇属 Russula 17 8.13
    栓菌属 Trametes 5 2.39
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