Comparison Between Finite Element Model and an Artificial Neural Networks Procedure for Riser Analysis

2015 ◽  
Author(s):  
Cristiano S. de Aguiar ◽  
Thiago Angelo G. de Lacerda ◽  
Luis V. S. Sagrilo ◽  
Wallace B. Siqueira
Keyword(s):  
Neural Networks ◽  
Finite Element ◽  
Artificial Neural Networks ◽  
Finite Element Model ◽  
Dynamic Analysis ◽  
Computational Cost ◽  
Element Model ◽  
Statistical Parameters ◽  
Time Step ◽  
Artificial Neural

As exploitation activities moves into fields located in deep water, the industry has been addressing studies aiming at concepts of offshore systems that reduce the influence of environmental loads on risers. The Buoy Support Riser (BSR) system is one of these new proposed concepts. The BSR is composed by a subsurface tethered buoy, where flexible jumpers connect the Floating Production Unit (FPU) to the BSR and Steel Catenary Risers (SCRs). Due to its complexity and non-linearity, this offshore system requires a highly refined finite element model for dynamic analysis, which demands a high computational cost. In order to increase feasibility of the analysis it is proposed a low computational cost methodology based on Artificial Neural Networks (ANN). This work aims to develop a program to train an ANN to predict the jumpers’ dynamic tension from FPU motions without running the finite element model for every time step. In this way, the purpose is to find results as reliable as those achieved in a dynamic analysis with a finite element model. Statistical parameters will be used for this comparison.

Damage quantification in foam core sandwich composites via finite element model updating and artificial neural networks

2020 ◽  
Vol 234 (21) ◽  
pp. 4288-4304
Author(s):  
Ali Mardanshahi ◽  
Masoud Mardanshahi ◽  
Ahmad Izadi
Keyword(s):  
Neural Networks ◽  
Finite Element ◽  
Artificial Neural Networks ◽  
Finite Element Model ◽  
Model Updating ◽  
Sandwich Structures ◽  
Element Model ◽  
Foam Core ◽  
Damage Quantification ◽  
Artificial Neural

The main idea of this paper is to propose a nondestructive evaluation (NDE) system for two types of damages, core cracking and skin/core debonding, in fiberglass/foam core sandwich structures based on the inverse eigensensitivity-based finite element model updating using the modal test results, and the artificial neural networks. First, the modal testing was conducted on the fabricated fiberglass/foam core sandwich specimens, in the intact and damaged states, and the natural frequencies were extracted. Finite element modeling and inverse eigensensitivity-based model updating of the intact and damaged sandwich structures were conducted and the parameters of the models were identified. Afterward, the updated finite element models were employed to generate a large dataset of the first five harmonic frequencies of the damaged sandwich structures with different damage sizes and locations. This dataset was adopted to train the machine learning models for detection, localization, and size estimation of the core cracking and skin/core debonding damages. A multilayer perceptron neural network classification model was used for detection of types of damages and also a multilayer perceptron neural network regression model was fitted to the dataset for automatically estimation of the locations and dimensions of damages. This intelligent system of damage quantification was also used to make predictions on real damaged specimens not seen by the system. The results indicated that the extracted natural frequencies from the accurate finite element model, in coordination with the experimental data, and using the artificial neural networks can provide an effective system for nondestructive evaluation of foam core sandwich structures.

Model Updating of a Steel Truss Based on Artificial Neural Networks

2011 ◽  
Vol 121-126 ◽  
pp. 1363-1366
Author(s):  
Shi Lei Zhang ◽  
Shao Feng Chen ◽  
Huan Ding Wang ◽  
Wei Wang
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Finite Element ◽  
Artificial Neural Networks ◽  
Model Updating ◽  
Back Propagation ◽  
Element Model ◽  
Steel Truss ◽  
Artificial Neural ◽  
The Finite Element Model

Based on the artificial neural network, the parameters of a steel truss are identified. And the finite element model of truss is corrected. In order to improve the efficiency of model updating by artificial neural networks, the momentum is introduced into the back propagation algorithm. Based on the theory of probability and mathematical statistics, the expectation confidence interval of the measured deflections and strains is obtained. In this way, the samples to train the neural network are optimized. The numerical results show that the back propagation neural network proposed on this paper is able to correct the finite element model of the truss effectively.

A finite element model updated by artificial neural networks to explain the behaviour of the Z24 Swiss bridge in different temperature states.

2021 ◽  
Author(s):  
Iason Iakovidis ◽  
Konstantinos Morfidis‌‌‌‌‌‌‌‌‌‌‌‌‌‌‌‌‌‌‌‌‌‌‌‌‌‌‌‌
Keyword(s):  
Neural Networks ◽  
Finite Element ◽  
Artificial Neural Networks ◽  
Element Model ◽  
Winter Period ◽  
Fe Model ◽  
Model Update ◽  
Artificial Neural ◽  
Using Data ◽  
Physical Reasons

<p>A Finite Element (FE) model of bridge Z24 was developed to reflect its dynamic response and investigate the physical reasons behind the large variations observed on its natural modal properties during a 7-month continuous monitoring campaign conducted before its demolition in 1997. A significant increase in natural frequencies was observed especially during the winter period, something which was explained as a consequence of deck stiffness increase and boundary conditions change, due to the formation of ice layers on the deck and supports.</p><p>The paper concentrates on the procedure of developing a FE model update process, which employs Artificial Neural Networks (ANNs), which are trained using data generated through the Monte Carlo process and analysed within the FE model of the bridge. The aim of this procedure is to calibrate the FE update sensitivity parameters in such a way as to replicate the dynamic behaviour of the bridge based on real-time measured eigenvalues obtained during monitoring for five different temperature states at -10 oC, -5 oC, 0 oC, 5 oC and 10oC.</p>

scholarly journals USING ARTIFICIAL NEURAL NETWORKS TO ACCELERATE TRANSPORT SOLVES

2021 ◽  
Vol 247 ◽  
pp. 03027
Author(s):  
Mauricio E. Tano ◽  
Jean C. Ragusa
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Gaussian Elimination ◽  
Computing Time ◽  
Computational Cost ◽  
Large Set ◽  
Time Step ◽  
Local Systems ◽  
Artificial Neural ◽  
Transport Problems

Discontinuous Finite Element Methods (DFEM) have been widely used for solving SN radiation transport problems in participative and non-participative media. In this method, small matrix-vector systems are assembled and solved for each cell, angle, energy group, and time step while sweeping through the computational mesh. In practice, these systems are generally solved directly using Gaussian elimination, as computational acceleration for solving this small systems are often inadequate. Nonetheless, the computational cost of assembling and solving these local systems, repeated for each cell in the phase-space, can amount to a large fraction of the total computing time. In this paper, a Machine Learning algorithm is designed to accelerate the solution of local systems. This one is based on Artificial Neural Networks (ANNs). Its key idea is training an ANN with a large set of solutions to random one-cell transport problems and, then, replacing the assembling and solution of the local systems by the feedforward evaluation of the trained ANN. It is observed that the optimized ANNs are able to reduce the compute times by a factor of ~ 3:6 per source iteration, while introducing mean absolute errors between 0:5 – 2% in transport solutions.

scholarly journals A Machine Learning Approach as a Surrogate for a Finite Element Analysis: Status of Research and Application to One Dimensional Systems

Sensors ◽  
2021 ◽  
Vol 21 (5) ◽  
pp. 1654
Author(s):  
Poojitha Vurtur Badarinath ◽  
Maria Chierichetti ◽  
Fatemeh Davoudi Kakhki
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Finite Element Analysis ◽  
Finite Element ◽  
Artificial Neural Networks ◽  
Stress Distribution ◽  
Maintenance Scheduling ◽  
Element Analysis ◽  
One Dimensional ◽  
Artificial Neural

Current maintenance intervals of mechanical systems are scheduled a priori based on the life of the system, resulting in expensive maintenance scheduling, and often undermining the safety of passengers. Going forward, the actual usage of a vehicle will be used to predict stresses in its structure, and therefore, to define a specific maintenance scheduling. Machine learning (ML) algorithms can be used to map a reduced set of data coming from real-time measurements of a structure into a detailed/high-fidelity finite element analysis (FEA) model of the same system. As a result, the FEA-based ML approach will directly estimate the stress distribution over the entire system during operations, thus improving the ability to define ad-hoc, safe, and efficient maintenance procedures. The paper initially presents a review of the current state-of-the-art of ML methods applied to finite elements. A surrogate finite element approach based on ML algorithms is also proposed to estimate the time-varying response of a one-dimensional beam. Several ML regression models, such as decision trees and artificial neural networks, have been developed, and their performance is compared for direct estimation of the stress distribution over a beam structure. The surrogate finite element models based on ML algorithms are able to estimate the response of the beam accurately, with artificial neural networks providing more accurate results.

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