Estimation the Natural Frequencies of a Cracked Shaft Based on Finite Element Modeling and Artificial Neural Network

2020 ◽  
Vol 10 (4) ◽  
pp. 1410
Author(s):  
Enass H. Flaieh ◽  
Farouk Omar Hamdoon ◽  
Alaa Abdulhady Jaber
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Finite Element ◽  
Finite Element Modeling ◽  
Natural Frequencies ◽  
Element Modeling ◽  
Artificial Neural ◽  
Cracked Shaft

Artificial neural network and finite element modeling of nanoindentation tests

2002 ◽  
Vol 33 (7) ◽  
pp. 1939-1947 ◽  
Author(s):  
Anastasia Muliana ◽  
Rami M. Haj-Ali ◽  
Rejanah Steward ◽  
Ashok Saxena
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Finite Element ◽  
Finite Element Modeling ◽  
Element Modeling ◽  
Artificial Neural

scholarly journals Prediction of Stress in Power Transformer Winding Conductors Using Artificial Neural Networks: Hyperparameter Analysis

Energies ◽  
2021 ◽  
Vol 14 (14) ◽  
pp. 4242
Author(s):  
Fausto Valencia ◽  
Hugo Arcos ◽  
Franklin Quilumba
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Finite Element ◽  
Power Transformer ◽  
Network Models ◽  
Activation Function ◽  
Neural Network Models ◽  
Transformer Winding ◽  
Artificial Neural ◽  
Femm Software

The purpose of this research is the evaluation of artificial neural network models in the prediction of stresses in a 400 MVA power transformer winding conductor caused by the circulation of fault currents. The models were compared considering the training, validation, and test data errors’ behavior. Different combinations of hyperparameters were analyzed based on the variation of architectures, optimizers, and activation functions. The data for the process was created from finite element simulations performed in the FEMM software. The design of the Artificial Neural Network was performed using the Keras framework. As a result, a model with one hidden layer was the best suited architecture for the problem at hand, with the optimizer Adam and the activation function ReLU. The final Artificial Neural Network model predictions were compared with the Finite Element Method results, showing good agreement but with a much shorter solution time.

Determining the Rayleigh damping parameters of flexible pavements for finite element modeling

2021 ◽  
pp. 107754632110267
Author(s):  
Jiandong Huang ◽  
Xin Li ◽  
Jia Zhang ◽  
Yuantian Sun ◽  
Jiaolong Ren
Keyword(s):  
Finite Element ◽  
Finite Element Modeling ◽  
Natural Frequencies ◽  
Least Squares Method ◽  
Element Model ◽  
Beam Model ◽  
Element Modeling ◽  
Rayleigh Damping ◽  
Damping Matrix

The dynamic analysis has been successfully used to predict the pavement response based on the finite element modeling, during which the stiffness and mass matrices have been established well, whereas the method to determine the damping matrix based on Rayleigh damping is still under development. This article presents a novel method to determine the two parameters of the Rayleigh damping for dynamic modeling in pavement engineering. Based on the idealized shear beam model, a more reasonable method to calculate natural frequencies of different layers is proposed, by which the global damping matrix of the road pavement can be assembled. The least squares method is simplified and used to calculate the frequency-independent damping. The best-fit Rayleigh damping is obtained by only determining the natural frequencies of the two modal. Finite element model and in-situ field test subjected by the same falling weight deflectometer pulse loads are performed to validate the accuracy of this method. Good agreements are noted between simulation and field in-situ results demonstrating that this method can provide a more accurate approach for future finite element modeling and back-calculation.

scholarly journals Artificial Neural Network-Based Method for Seismic Analysis of Concrete-Filled Steel Tube Arch Bridges

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Zhen Liu ◽  
Shibo Zhang
Keyword(s):  
Neural Network ◽  
Finite Element Method ◽  
Artificial Neural Network ◽  
Finite Element ◽  
Seismic Analysis ◽  
Steel Tube ◽  
Arch Bridge ◽  
Concrete Filled Steel Tube ◽  
Cfst Arch Bridge ◽  
Artificial Neural

Seismic analysis of concrete-filled steel tube (CFST) arch bridge based on finite element method is a time-consuming work. Especially when uncertainty of material and structural parameters are involved, the computational requirements may exceed the computational power of high performance computers. In this paper, a seismic analysis method of CFST arch bridge based on artificial neural network is presented. The ANN is trained by these seismic damage and corresponding sample parameters based on finite element analysis. In order to obtain more efficient training samples, a uniform design method is used to select sample parameters. By comparing the damage probabilities under different seismic intensities, it is found that the damage probabilities of the neural network method and the finite element method are basically the same. The method based on ANN can save a lot of computing time.

Development of Surrogate Models of Orthopedic Screws to Improve Biomechanical Performance

2012 ◽  
pp. 138-159
Author(s):  
Ching-Chi Hsu
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Finite Element ◽  
Objective Function ◽  
Bending Strength ◽  
Pullout Strength ◽  
Neural Network Method ◽  
Network Method ◽  
Artificial Neural Network Method ◽  
Artificial Neural

An optimization approach was applied to improve the design of the lag screws used in double screw nails. However, finite element analyses with an optimal algorithm may take a long time to find the best design. Thus, surrogate methods, either artificial neural networks or multiple linear regressions, were used to substitute for the finite element models. The results showed that an artificial neural network method can accurately develop the objective functions of the lag screws for both the bending strength and the pullout strength. A multiple linear regression method can successfully develop the objective function of the lag screws for the pullout strength, but it failed to construct the objective function for the bending strength. The optimal design of the lag screws could be obtained using the artificial neural network method and genetic algorithms.

Multi-physics Analysis of Multi-chip LED Light Source Based on Finite Element Method and Artificial Neural Network

2019 ◽  
Vol 40 (6) ◽  
pp. 795-802 ◽  
Author(s):  
刘宏伟 LIU Hong-wei ◽  
牛萍娟 YU Dan-dan ◽  
郭 凯 NIU Ping-juan ◽  
张建新 ZHANG Zan-yun ◽  
王 闯 GUO Kai ◽  
...  
Keyword(s):  
Neural Network ◽  
Finite Element Method ◽  
Artificial Neural Network ◽  
Finite Element ◽  
Light Source ◽  
Led Light ◽  
Artificial Neural ◽  
Element Method
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