A rapidly trained DNN model for real-time flexible multibody dynamics simulations with a fixed-time increment

Abstract

This study presents an efficient fixed-time increment-based approach for a data-driven analysis of flexible multibody dynamics (FMBD) problems, combining deep neural network (DNN) modeling and principal component analysis (PCA). To construct a DNN-based surrogate model, we eliminated the time instant in the input features while applying PCA to reduce the dimensionality of the output results, which encompassed transient dynamics such as displacement, stress, and strain. This restructuring allowed us to maintain the temporal information in the output data set while still formatting it in a fixed-time increment format, streamlining the process of training an efficient DNN model. Despite using fewer samples, this approach significantly reduces training costs compared to DNN model without PCA. Benchmark problems, including a double compound pendulum, piston-cylinder system, and deployable parabolic antenna, demonstrate that the proposed scheme drastically reduces training time while maintaining accuracy and quick prediction time.