• Journal of the Korean Society of Manufacturing Process Engineers
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• v.19 no.7
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• pp.35-40
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• 2020

This work is concerned with various planar deformations of an isotropic sandwich beam, which generally consists of three layers: two stiff skin layers and one soft core layer. When one layer of the sandwich beam is modeled as a beam, the variational-asymptotic method is rigorously used to construct a zeroth-order beam model, which is similar to a generalized Timoshenko beam model capable of capturing the transverse shear deformations but still carries out the zeroth-order approximation. To analyze the planar sandwich beam, the sum of the energies of the two skin layers and one core layer is then formulated with different material and geometric properties and represented by a universal beam model in terms of the core-layer kinematics through interface displacement and stress continuity conditions. As a preliminary validation, two extreme examples are presented to demonstrate the capability and accuracy of this present approach.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.38 no.5
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• pp.575-584
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• 2014

In this study, the NREL 5 MW wind turbine tower model was optimized according to the multi-body dynamics and reliability-based design. The mathematical model was defined as a link-joint system including dynamic characteristics derived from Timoshenko's beam theory. For the optimization problem, the sensitivities to variations in the tower thicknesses and inner and outer diameters were acquired and arranged in terms of safety and efficiency according to bending stress and buckling standards. An optimal design was calculated with the advanced first-order second moment method and used to define a finite element model for validation. The finite element model was simulated by static analysis. The relationship between the multi-body dynamic and finite element method throughout the process was investigated, and the optimal model, which had high endurance despite its low mass, was determined.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.48 no.10
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• pp.773-782
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• 2020

The development of joint FE models for deep learning neural network (DLNN)-based hybrid FEA is presented. Material models of bolts and bearings in the front axle of tractor, showing complex behavior induced by various tightening conditions, were replaced with DLNN models. Bolts are modeled as one-dimensional Timoshenko beam elements with six degrees of freedom, and bearings as three-dimensional solid elements. Stress-strain data were extracted from all elements after finite element analysis subjected to various load conditions, and DLNN for bolts and bearing were trained with Tensorflow. The DLNN-based joint models were implemented in the ABAQUS user subroutines where stresses from the next increment are updated and the algorithmic tangent stiffness matrix is calculated. Generalization of the trained DLNN in the FE model was verified by subjecting it to a new loading condition. Finally, the DLNN-based FEA for the front axle of the tractor was conducted and the feasibility was verified by comparing with results of a static structural experiment of the actual tractor.