Machine Learning Based Prediction of Ditching Loads

Approaches are presented 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 behavior. The training data are compiled by an extension of the momentum method of von Karman and Wagner, and the rationale of the training approach is briefly summarized. 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.

Subjects

MLE@TUHH

DDC Class

510: Mathematics

620: Engineering

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Machine Learning Based Prediction of Ditching Loads