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Predicting Unreinforced Fabric Mechanical Behavior with Recurrent Neural NetworksUnreinforced woven fabrics are widely employed in various high-performance applications, including parachute deployment systems, airbags, and ballistic armor. The analysis of such materials is inherently complex due to the multiscale structure of these materials, and the dependence of macroscale behavior on changes that occur at lower scales. Previously, NASA’s Multiscale Analysis Tool (NASMAT) showed its capability in predicting unreinforced fabric behavior at the macroscale by capturing finite rotations that occur at the mesoscale. Though effective, the tool can face high computational cost for large, complex problems, motivating the need for the development of a surrogate model that can capture the same behavior. A recurrent neural network (RNN) was developed and trained on virtual NASMAT data to mimic the physics-based solutions while improving the computational runtime. The architecture of the RNN to best simulate the fabric behavior was carefully crafted based on heuristic knowledge of predicting physics-based temporal data, manual hyperparameter case studies, and Hyperband optimization.. The resultant model was able to predict a variety of stress-strain curves for fabrics with different mesoscale geometries, and was further validated by comparing to experimental data for the K706 style Kevlar plain-weave fabric, demonstrating the ability of the model to effectively capture the geometric changes in the fabric without explicitly calculating them, as is done in NASMAT. Furthermore, the tool showed its ability to improve on the runtime by a factor of 10 for fabric solutions compared to the multiscale tool, which would further enable the simulation of complex loading scenarios on unreinforced fabrics.
Document ID
20210023708
Acquisition Source
Glenn Research Center
Document Type
Technical Memorandum (TM)
Authors
Brandon Hearley
(HX5, LLC )
Joshua Stuckner
(Glenn Research Center Cleveland, Ohio, United States)
Evan Pineda
(Glenn Research Center Cleveland, Ohio, United States)
Scott Murman
(Ames Research Center Mountain View, California, United States)
Date Acquired
November 1, 2021
Publication Date
January 1, 2022
Subject Category
Mechanical Engineering
Report/Patent Number
E-20005
Funding Number(s)
WBS: 109492
Distribution Limits
Public
Copyright
Public Use Permitted.
Technical Review
Single Expert
Keywords
Fabric
multiscale
machine learning
recurrent neural network
Textile
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