2024/02/16 by Henning Schwarz, Micha Überrück, Schwarz, Henning +5 · 1 citation
Engineering · #Advanced Manufacturing and Logistics Optimization #Belt Conveyor Systems Engineering #Computational Engineering #FOS: Computer and information sciences #Finance #Machine Learning (cs.LG) #and Science (cs.CE)
paper · pdf · doi:10.48550/arxiv.2402.10724
openalex publication_date 2024/02/16 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/01
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° 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.