Analysis of Stress Concentration Factor for Tensile Characteristics of Syntactic Foam Using Finite Element Method

The spent nuclear fuel of HTR-PM (High Temperature Reactor–Pebblebed Modules) will be dry stored in wells. In the mouth of each well, there is a cover weighing 11 tons. A lifting appliance with three hooks is used to open and close the covers. The hooks are L-shaped with fillet at the inside corner. The stress concentration at the corner has a significant impact on the strength and fatigue life of hooks. For optimizing the structure of the hook, the stress concentration factor related to the radius of fillet is calculated by both theoretical and numerical methods. The theoretical calculation is based on the Saint-Venant’s Principle and the analytical solution of a curved beam. The result is consistent with the numerical calculation performed by the finite element method.

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Research on the Maximum Bending Stress on Driving Tine of SCMW

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.184-185.445 ◽

2012 ◽

Vol 184-185 ◽

pp. 445-449 ◽

Cited By ~ 1

Author(s):

Yang Zhi Chen ◽

Shun Ke Liang

Keyword(s):

Finite Element Method ◽

Finite Element ◽

Stress Concentration ◽

Theoretical Result ◽

Stress Concentration Factor ◽

Concentration Factor ◽

Helix Angle ◽

Bending Stress ◽

Actual Result ◽

Two Factors

In this study, equations of the maximum bending stress (MBS) on the root of driving tine of the space-curve meshing-wheel (SCMW) are deduced. Four factors have an impact on stress concentration of the driving tines, the helix angle, the fillet, the diameter of driving tines and the radius of the spiral curve for driving tine. They have been studied by Finite Element Method (FEM). Results show the former two factors have great impact on stress concentration while the last two could be ignored. Then the method to gain the stress concentration factor is proposed. It makes the theoretical result of the MBS on the root of driving tine match the actual result.

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Surface Roughness Characterization for Stress Concentration Factor Predictions: A Bayesian Learning Approach

Journal of Engineering Research and Reports ◽

10.9734/jerr/2021/v21i717479 ◽

2021 ◽

pp. 59-70

Author(s):

Jingyi Zhang

Keyword(s):

Finite Element Method ◽

Surface Roughness ◽

Finite Element ◽

Fatigue Life ◽

Stress Concentration ◽

Stress Concentration Factor ◽

Concentration Factor ◽

Bayesian Learning ◽

Empirical Models ◽

Semi Empirical

The surface roughness has an important influence on the fatigue life of the structures. The fatigue life reduces due to the stress concentration caused by surface roughness. The stress concentration governs the fatigue crack initiation and propagation. The accurate acquisition of the stress concentration factor of rough surfaces is a key issue in determining fatigue life. Nevertheless, semi-empirical models may be biased for various machining processes. Besides, finite element method simulations cannot give explicit expression of the stress concentration factor. Bayesian learning can construct accurate prediction models which offering a number of additional advantages. In this paper, based on several data pairs constructed by finite element method, the correlation expression between the stress concentration factor and statistical roughness parameters of surfaces is established quickly through Bayesian learning. Compared with some other semi-empirical models, the accuracy and stability of the proposed method are certified. This paper provides a simple and effi-cient approach to determine the stress concentration factor for rough surfaces under different processing conditions.

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