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    Titel :

    Adjoint-Based Model Tuning and Machine Learning Strategy for Turbulence Model Improvement


    Beteiligte:
    Wu, Haibo (Autor:in) / Zhou, Hua (Autor:in) / Xu, Sichuan (Autor:in) / Ren, Chao (Autor:in) / Chen, Qian (Autor:in) / Jadhav, Tushar (Autor:in)

    Kongress:

    WCX SAE World Congress Experience



    Erscheinungsdatum :

    01.01.2022


    Format / Umfang :

    ALL-ALL



    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Print


    Sprache :

    Englisch



    Adjoint-Based Model Tuning and Machine Learning Strategy for Turbulence Model Improvement

    Wu, Haibo / Zhou, Hua / Xu, Sichuan et al. | British Library Conference Proceedings | 2022


    Adjoint-Based Model Tuning and Machine Learning Strategy for Turbulence Model Improvement

    Ren, Chao / Wu, Haibo / Zhou, Hua et al. | SAE Technical Papers | 2022


    Tuning of Turbulence Model Closure Coefficients Using an Explainability Based Machine Learning Algorithm

    Bounds, Charles Patrick / Uddin, Mesbah / Desai, Shishir | British Library Conference Proceedings | 2023


    A Machine Learning Strategy to Assist Turbulence Model Development

    Tracey, Brendan D. / Duraisamy, Karthikeyan / Alonso, Juan J. | AIAA | 2015


    Tuning of Turbulence Model Closure Coefficients Using an Explainability Based Machine Learning Algorithm

    Bounds, Charles Patrick / Uddin, Mesbah / Desai, Shishir | SAE Technical Papers | 2023