• Transactions of the Korean Society of Mechanical Engineers A
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• v.35 no.3
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• pp.235-240
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• 2011

A flexible hose attached to a mother ship experiences various motions that depend on the movement of the mother ship and that of underwater vehicle. Although the motion of the hose is a very important factor that determines how a mother ship should be steered in a real situation, it is difficult to experimentally obtain information about the hose motion. Therefore, we study the motion of the hose analytically. The ANCF(absolute nodal coordinate formulation) was used to model the hose, because this formulation can relax the Euler-Bernoulli theory and the Timoshenko beam theory and allow the deformation of the cross section. The mother ship is assumed to be a rigid body with 6 degrees of freedom. The motion of the hose is predominantly affected by the behavior of the mother ship and by the fluid flow.

• Tribology and Lubricants
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• v.40 no.1
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• pp.8-16
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• 2024

This study conducts numerical modeling and eigen-analysis of a rod-fastened rotor, which is mainly used in aircraft gas turbine engines in which multiple disks are in contact through curvic coupling. Nayak's theory is adopted to calculate surface parameters measured from the tooth profile of the curvic coupling gear. Surface parameters are important design parameters for predicting the stiffness between contact surfaces. Based on the calculated surface parameters, elastoplastic contact analysis is performed according to the interference between two surfaces based on the Greenwood-Williamson model. The equivalent bending stiffness is predicted based on the shape and elastoplastic contact stiffness of the curvic coupling. An equation of motion of the rod-fastened rotor, including the bending stiffness of the curvic coupling, is developed. Methods for applying the bending stiffness of a curvic coupling to the equation of motion and for modeling the equation of motion of a rotor that includes both inner and outer rotors are introduced. Rotordynamic analysis is performed through one-dimensional finite element analysis, and each element is modeled based on Timoshenko beam theory. Changes in bending stiffness and the resultant critical speed change in accordance with the rod fastening force are predicted, and the corresponding mode shapes are analyzed.

• 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.