Deep-Neural-Networks-Based Data-Driven Methods for Characterizing the Mechanical Behavior of Hydroxyl-Terminated Polyether Propellants.

ABSTRACT: Hydroxyl-terminated polyether (HTPE) propellants are attractive in the weapons materials and equipment industry for their insensitive properties. Storage, combustion, and explosion of solid propellants are affected by their mechanical properties, so accurate mechanical modeling is vital. In this study, deep neural networks are applied to model composite solid-propellant mechanical behavior for the first time. A data-driven framework incorporating a novel training-testing splitting strategy is proposed. By building Neural Networks (FFNNs), Kolmogorov-Arnold Networks (KANs) and Long Short-Term Memory (LSTM) networks and optimizing the model framework and parameters using a Bayesian optimization algorithm, the results show that the LSTM model predicts the stress-strain curve of HTPE propellan

SUBMITTER: Han R 

PROVIDER: S-EPMC11902718 | biostudies-literature | 2025 Feb

REPOSITORIES: biostudies-literature