Machine Learning based Prediction of Ditching Loads
<|reference_start|>Machine Learning based Prediction of Ditching Loads: We present approaches to predict dynamic ditching loads on aircraft fuselages using machine learning. The employed learning procedure is structured into two parts, the reconstruction of the spatial loads using a convolutional autoencoder (CAE) and the transient evolution of these loads in a subsequent part. Different CAE strategies are assessed and combined with either long short-term memory (LSTM) networks or Koopman-operator based methods to predict the transient behaviour. The training data is compiled by an extension of the momentum method of von-Karman and Wagner and the rationale of the training approach is briefly summarised. The application included refers to a full-scale fuselage of a DLR-D150 aircraft for a range of horizontal and vertical approach velocities at 6{\deg} incidence. Results indicate a satisfactory level of predictive agreement for all four investigated surrogate models examined, with the combination of an LSTM and a deep decoder CAE showing the best performance.<|reference_end|>
@article{schwarz2024machine,
    title={Machine Learning based Prediction of Ditching Loads},
    author={Henning Schwarz, Micha "Uberr"uck, Jens-Peter M. Zemke, Thomas Rung},
    journal={arXiv preprint arXiv:2402.10724},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.10724},
    primaryClass={cs.LG cs.CE}
}