Machine Learning based Prediction of Ditching Loads
This work addresses aircraft safety by improving load prediction, but it is incremental as it applies existing ML methods to a specific domain.
The paper tackled predicting dynamic ditching loads on aircraft fuselages using machine learning, achieving satisfactory predictive agreement across four surrogate models, with an LSTM and deep decoder CAE combination performing best.
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.