AeroFusion is a NASA Langley initiative to incorporate advances in data science into the aerodynamic modeling process to improve efficiency. The effort can largely be categorized in three components: reduced-order modeling techniques, surrogate modeling techniques, and uncertainty quantification. By combining various methods from these categories, AeroFusion aims to reduce the development cost of aerodynamic models, both in terms of time and money.


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    Titel :

    AeroFusion: Data Fusion and Uncertainty Quantification for Entry Vehicles


    Beteiligte:
    S. Snyder (Autor:in) / T. J. Wignall (Autor:in) / J. Green (Autor:in) / S. Kumar (Autor:in) / M. Lee (Autor:in) / T. Nakamura-Zimmerer (Autor:in) / J. B. Scoggins (Autor:in) / A. Williams (Autor:in)

    Erscheinungsdatum :

    2022


    Format / Umfang :

    8 pages


    Medientyp :

    Report


    Format :

    Keine Angabe


    Sprache :

    Englisch


    Schlagwörter :



    AeroFusion: Data Fusion and Uncertainty Quantification for Entry Vehicles

    Snyder, Steven / Wignall, Thomas J. / Green, Justin S. et al. | AIAA | 2023


    AeroFusion: Data Fusion and Uncertainty Quantification for Entry Vehicles

    Snyder, Steven / Wignall, Thomas J. / Green, Justin S. et al. | TIBKAT | 2023


    AeroFusion: Data Fusion and Uncertainty Quantification for Entry Vehicles

    Steven Snyder / T J Wignall / Justin Green et al. | NTRS


    AeroFusion: Data Fusion and Uncertainty Quantification for Entry Vehicles

    Steven Snyder / T J Wignall / Justin S Green et al. | NTRS