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Prediction of Airtightness Performance of Stratospheric Ships Based on Multivariate Environmental Time-Series Data

Author

Listed:
  • Yitong Bi

    (Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
    School of Aeronautics and Astronautics, University of Chinese Academy of Sciences, Beijing 100049, China)

  • Wenkuan Xu

    (Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
    School of Aeronautics and Astronautics, University of Chinese Academy of Sciences, Beijing 100049, China)

  • Lin Song

    (Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
    School of Aeronautics and Astronautics, University of Chinese Academy of Sciences, Beijing 100049, China)

  • Molan Yang

    (Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
    School of Aeronautics and Astronautics, University of Chinese Academy of Sciences, Beijing 100049, China)

  • Xiangqiang Zhang

    (Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
    School of Aeronautics and Astronautics, University of Chinese Academy of Sciences, Beijing 100049, China)

Abstract

This study addresses the challenge of predicting the airtightness of stratospheric airship envelopes, a critical factor influencing flight performance. Traditional ground-based airtightness tests often rely on limited resources and empirical formulas. To overcome these limitations, this paper explores the use of predictive models to integrate multi-source test data, enhancing the accuracy of airtightness assessments. A performance comparison of various prediction models was conducted using ground-based test data from a specific stratospheric airship. Among the models evaluated, the NeuralProphet model demonstrated superior accuracy in long-term airtightness predictions, effectively capturing time-series dependencies and spatial interactions with environmental conditions. This work introduces an innovative approach to modeling airtightness, providing both experimental and theoretical contributions to the field of stratospheric airship performance prediction.

Suggested Citation

  • Yitong Bi & Wenkuan Xu & Lin Song & Molan Yang & Xiangqiang Zhang, 2025. "Prediction of Airtightness Performance of Stratospheric Ships Based on Multivariate Environmental Time-Series Data," Forecasting, MDPI, vol. 7(2), pages 1-24, June.
    • Handle: RePEc:gam:jforec:v:7:y:2025:i:2:p:28-:d:1677654
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    References listed on IDEAS

    as
    1. Sean J. Taylor & Benjamin Letham, 2018. "Forecasting at Scale," The American Statistician, Taylor & Francis Journals, vol. 72(1), pages 37-45, January.
    2. Muhammad Wasim & Ahsan Ali & Mohammad Ahmad Choudhry & Faisal Saleem & Inam Ul Hasan Shaikh & Jamshed Iqbal, 2021. "Unscented Kalman filter for airship model uncertainties and wind disturbance estimation," PLOS ONE, Public Library of Science, vol. 16(11), pages 1-25, November.
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