To accurately perceive sudden airflow changes during flight and avoid aviation safety risks caused by sudden weather at high altitudes, it is necessary to invert and reconstruct the wind field during flight. Based on the random sample consensus estimation, the improved Least Squares algorithm is used to fit the Automatic Dependent Surveillance-Broadcast (ADS-B) data. Firstly, the vertical wind profile is constructed by inverting the wind vectors at different altitude levels at the fixed airport location and compared with the meteorological data of the European Center for Medium-Range Weather Forecasts (ECMWF) to verify the feasibility and accuracy of the algorithm. Then, the wind field under the complete track of a specific flight is reconstructed to obtain the wind field distribution along the route. Finally, regional wind fields at different altitudes are reconstructed using multi-aircraft data in a fixed range. The experimental results show that this method can better reflect the real complex wind field structure.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Wind Field Inversion and Reconstruction Based on ADS-B Data


    Beteiligte:
    Xiaoyun, Shen (Autor:in) / Zixuan, Zhao (Autor:in) / Siyuan, Zhang (Autor:in) / Weidong, Jiao (Autor:in) / Chong, Ma (Autor:in) / Tianying, Wang (Autor:in)


    Erscheinungsdatum :

    2021-10-20


    Format / Umfang :

    1849556 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch





    Sea surface wind field inversion method and device

    DANG CHAOQUN / WANG BIN / ZHANG WEIXING et al. | Europäisches Patentamt | 2022

    Freier Zugriff

    An Inversion Model for Subsonic Moving Sound Source Reconstruction

    Li, X.-D. / Zhou, S. / Confederation of European Aerospace Societies| AIAA | British Library Conference Proceedings | 1995


    An Open-Source Adjoint-based Field Inversion Tool for Data-driven RANS modelling

    Bidar, Omid / He, Ping / Anderson, Sean et al. | AIAA | 2022