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Prediction of the Motion Statistical Characteristics of Amphibious Aircraft in Waves
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Gao-xiang SUN1, Chan-ying QI2, Peng-tao HU2, Guo-da CHENG3, *, Jue GONG3
Science Technology and Engineering | 2025, 25(1) : 404 - 409
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Science Technology and Engineering | 2025, 25(1): 404-409
• Papers·Aeronautics and Astronautics •
Prediction of the Motion Statistical Characteristics of Amphibious Aircraft in Waves
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Gao-xiang SUN1, Chan-ying QI2, Peng-tao HU2, Guo-da CHENG3, *, Jue GONG3
Affiliations
  • 1. Qingdao Innovation and Development Base, Harbin Engineering University, Qingdao 266000, China
  • 2. The Fifth Military Representative Office Stationed in Xi’an with Empty Equipment, Xi’an 710000, China
  • 3. Xi’an Flight Automatic Control Research Institute, AVIC, Xi’an 710076, China
Published: 2025-01-08 doi: 10.12404/j.issn.1671-1815.2401685
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Aiming at the problem of short effective prediction time for the movement history of amphibious aircraft in waves, the statistical values of amphibious aircraft movement over a period of time were proposed to predict, and a prediction model for the statistical characteristics of amphibious aircraft movement was constructed based on long short-term memory neural networks(LSTM). Taking the NACA TN 2929 amphibious aircraft as an example, based on its numerical simulation data, the statistical values of the three degrees of freedom motion of heave, roll, and pitch of amphibious aircraft under sea conditions of level 3, 4, and 5 were predicted, and their prediction effects were analyzed in detail. The results show that the LSTM neural network-based model for predicting the statistical characteristics of amphibious aircraft motion has good prediction accuracy. In practical engineering applications, this model can accurately predict the statistical values of amphibious aircraft motion in the future, providing auxiliary decision-making information for offshore operations.

amphibious aircraft  /  long short-term memory neural network  /  motion statistical characteristics  /  motion prediction
Gao-xiang SUN, Chan-ying QI, Peng-tao HU, Guo-da CHENG, Jue GONG. Prediction of the Motion Statistical Characteristics of Amphibious Aircraft in Waves[J]. Science Technology and Engineering, 2025 , 25 (1) : 404 -409 . DOI: 10.12404/j.issn.1671-1815.2401685
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Article Info
doi: 10.12404/j.issn.1671-1815.2401685
  • Receive Date：2024-03-11
  • Online Date：2025-07-29
  • Published：2025-01-08
Article Data
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History
  • Received：2024-03-11
  • Revised：2024-10-11
Affiliations
    1. Qingdao Innovation and Development Base, Harbin Engineering University, Qingdao 266000, China
    2. The Fifth Military Representative Office Stationed in Xi’an with Empty Equipment, Xi’an 710000, China
    3. Xi’an Flight Automatic Control Research Institute, AVIC, Xi’an 710076, China
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表12种不同金属材料的力学参数
科
Family		 属数
Number of
genus		 种数
Number of
species		 占总种数比例
Percentage of
total species (%)		 属
Genus		 种数
Number of
species		 占总种数比例
Percentage of total
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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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