Machine Learning based Prediction of Ditching Loads
Henning Schwarz Micha Überrück Jens-Peter M. Zemke Thomas Rung
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.