Machine Learning based Prediction of Ditching Loads
Abstract
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.
Jens-Peter M. Zemke
Oberingenieur & MLE-Koordinator
Numerische Lineare Algebra, Krylovraum-Verfahren, Eigenwertaufgaben
Thomas Rung
Professor
Fluid Dynamics, Simulation of complex engineering flows, Eulerian and Lagrangian two-phase flow modelling