In lidar-based gust load alleviation, the wind profile ahead of the aircraft cannot be measured directly but has to be reconstructed (estimated) based on the acquired line-of-sight measurements. Such wind reconstruction algorithms typically include regularization in order to adequately handle the noise within the data. This paper presents an empirical Bayesian approach to choosing optimal regularization parameters for any given set of measurements. Using simulations of flight through turbulence, the Bayesian approach is compared with a former approach (based on engineering guess) and an omniscient optimizer, which yields the best achievable results for a constant set of parameters by using the full knowledge of the wind field. The Bayesian approach is shown to outperform the engineering guess and performs close to the omniscient optimizer while purely relying on the lidar measurement data.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Adaptive Wind Field Estimation Using an Empirical Bayesian Approach


    Contributors:

    Published in:

    Publication date :

    2024-11-01




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English




    Aerodynamic Load Estimation in Wind Turbine Drivetrains Using a Bayesian Data Assimilation Approach

    Valikhani, Mohammad / Jahangiri, Vahid / Ebrahimian, Hamed et al. | TIBKAT | 2024


    Video Denoising via Empirical Bayesian Estimation of Space-Time Patches

    Arias, P. | British Library Online Contents | 2018


    Nonlinear Estimation and Bayesian Multi-sensor Fusion using Adaptive Quadrature

    Lee, Victor / Yoon, Jangho / Vedula, Prakash | AIAA | 2011


    Nonlinear Estimation and Bayesian Multi-sensor Fusion using Adaptive Quadrature

    Lee, V. / Yoon, J. / Vedula, P. et al. | British Library Conference Proceedings | 2011