Schwarz, HenningHenningSchwarzÜberrück, MichaMichaÜberrückZemke, JensJensZemkeRung, ThomasThomasRung2024-02-222024-02-222024-02-16arXiv: 2402.10724 (2024)https://hdl.handle.net/11420/45917We 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° 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.enhttps://creativecommons.org/licenses/by/4.0/Machine LearningAircraft DitchingMLE@TUHHMathematicsEngineering and Applied OperationsPreprint10.15480/882.924010.15480/882.92402402.10724Preprint