کد مقاله کد نشریه سال انتشار مقاله انگلیسی ترجمه فارسی نسخه تمام متن
4963698 1364967 2018 20 صفحه PDF سفارش دهید دانلود کنید
عنوان انگلیسی مقاله
A neural network-based surrogate model for carbon nanotubes with geometric nonlinearities
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر نرم افزارهای علوم کامپیوتر
پیش نمایش صفحه اول مقاله
A neural network-based surrogate model for carbon nanotubes with geometric nonlinearities
چکیده انگلیسی

This paper presents a neural network (NN)-based surrogate modeling approach suitable for the geometrically nonlinear analysis of carbon nanotubes (CNTs). In this work we propose an NN-based equivalent beam element (NN-EBE) which is capable of accurately predicting the high-order phenomena caused by size-effects that characterize the behavior of CNTs at the nano-scale and can only be predicted by micro-mechanical models. The basic idea is to approximate the residual forces of the Newton-Raphson incremental-iterative formulation of the classical Euler or Timoshenko beams of the EBE model by an NN prediction, which is based on the response of the detailed MSM model of a CNT portion. Several numerical examples are presented for straight and wavy CNTs under bending and compression, which demonstrate that the proposed methodology is possible to efficiently predict the nonlinear response of large-scale CNT structures in a fraction computing time compared to the full-scale problem.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Computer Methods in Applied Mechanics and Engineering - Volume 328, 1 January 2018, Pages 411-430
نویسندگان
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