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.

本研究提出了一种有效的基于固定时间增量的方法，用于灵活多体动力学 (FMBD) 问题的数据驱动分析，结合深度神经网络 (DNN) 建模和主成分分析 (PCA)。为了构建基于 DNN 的代理模型，我们消除了输入特征中的时刻，同时应用 PCA 来降低输出结果的维度，其中包括位移、应力和应变等瞬态动力学。这种重组使我们能够维护输出数据集中的时间信息，同时仍将其格式化为固定时间增量格式，从而简化了训练高效 DNN 模型的过程。尽管使用的样本较少，但与没有 PCA 的 DNN 模型相比，这种方法显着降低了训练成本。基准问题，包括双复合摆、活塞缸系统和可展开抛物面天线，表明所提出的方案大大减少了训练时间，同时保持了准确性和快速预测时间。