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All sources NASA NTRS DOE OSTI

NASA NTRS2012

Simplified Models for the Study of Postbuckled Hat-Stiffened Composite Panels

The postbuckling response and failure of multistringer stiffened panels is analyzed using models with three levels of approximation. The first model uses a relatively coarse mesh to capture the global postbuckling response of a five-stringer panel. The second model can predict the nonlinear response as well as the debonding and crippling failure mechanisms in a single stringer compression specimen (SSCS). The third model consists of a simplified version of the SSCS that is designed to minimize the computational effort. The simplified model is well-suited to perform sensitivity analyses for studying the phenomena that lead to structural collapse. In particular, the simplified model is used to obtain a deeper understanding of the role played by geometric and material modeling parameters such as mesh size, inter-laminar strength, fracture toughness, and fracture mode mixity. Finally, a global/local damage analysis method is proposed in which a detailed local model is used to scan the global model to identify the locations that are most critical for damage tolerance.

Vescovini, Riccardo↗

DOE OSTI2022

Surrogate Modeling of Nonlinear Dynamic Systems: A Comparative Study

Surrogate models play a vital role in overcoming the computational challenge in designing and analyzing nonlinear dynamic systems, especially in the presence of uncertainty. This paper presents a comparative study of different surrogate modeling techniques for nonlinear dynamic systems. Four surrogate modeling methods, namely, Gaussian process (GP) regression, a long short-term memory (LSTM) network, a convolutional neural network (CNN) with LSTM (CNN-LSTM), and a CNN with bidirectional LSTM (CNN-BLSTM), are studied and compared. All these model types can predict the future behavior of dynamic systems over long periods based on training data from relatively short periods. The multi-dimensional inputs of surrogate models are organized in a nonlinear autoregressive exogenous model (NARX) scheme to enable recursive prediction over long periods, where current predictions replace inputs from the previous time window. Three numerical examples, including one mathematical example and two nonlinear engineering analysis models, are used to compare the performance of the four surrogate modeling techniques. The results show that the GP-NARX surrogate model tends to have more stable performance than the other three deep learning (DL)-based methods for the three particular examples studied. The tuning effort of GP-NARX is also much lower than its deep learning-based counterparts.

42 ENGINEERING↗