scholarly journals Damage Propagation Prediction of Adhesion Failure in Composite T-joint Structure and Improvement using PZT Patch

2021 ◽  
Vol 0 (0) ◽  
pp. 0-0
Author(s):  
S.K. Panda ◽  
P.K. Mishra ◽  
S.K. Panda
Keyword(s):  
Damage Propagation ◽  
Joint Structure ◽  
Adhesion Failure ◽  
Propagation Prediction

Blade Material Fatigue Assessment Using Elman Neural Networks

2007 ◽  
Author(s):  
Jihong Yan ◽  
Pengxiang Wang
Keyword(s):  
Neural Networks ◽  
Testing System ◽  
Steam Turbines ◽  
Material Degradation ◽  
Trend Prediction ◽  
Damage Propagation ◽  
Material Fatigue ◽  
Fatigue Severity ◽  
Propagation Prediction ◽  
The Cost

Material degradation evaluation and life prediction of major components such as blades, rotors, valves of steam turbines not only guarantees reliable, efficient and continuous operation of electric plants, but also offers the promise of substantially reducing the cost of repair and replacement of defective parts, and may even result in saving lives. In this paper, a recurrent neural network based strategy was developed for material degradation assessment and fatigue damage propagation prediction. Two Elman Neural Networks were developed for fatigue severity assessment and trend prediction correspondingly. The performance of the proposed prognostic methodology was evaluated by using blade material fatigue data collected from a material testing system. The prognostic method is found to be a reliable and robust material fatigue predictor.

Prognosis of Blade Material Fatigue Using Elman Neural Networks

2007 ◽  
Vol 10-12 ◽  
pp. 558-562
Author(s):  
Ji Hong Yan ◽  
P.X. Wang
Keyword(s):  
Neural Networks ◽  
Material Testing ◽  
Testing System ◽  
Material Degradation ◽  
Trend Prediction ◽  
Damage Propagation ◽  
Material Fatigue ◽  
Fatigue Data ◽  
Fatigue Severity ◽  
Propagation Prediction

Prognosis of major components such as blades, rotors, valves of steam turbine is crucial to reducing operating and maintenance costs. Prognostic strategies can assist to detect, classify and predict developing faults, guarantee reliable, efficient and continuous operation of electric plants, and may even result in saving lives. In this paper, a recurrent neural network based strategy was developed for blade material degradation assessment and fatigue damage propagation prediction. Two Elman Neural Networks were developed for fatigue severity assessment and trend prediction correspondingly. The performance of the proposed prognostic methodology was evaluated by using blade material fatigue data collected from a material testing system. The prognostic method is found to be a reliable and robust material fatigue predictor.

Representation of bolted joints in a structure using finite element modelling and model updating

2020 ◽  
Vol 14 (3) ◽  
pp. 7141-7151 ◽  
Author(s):  
R. Omar ◽  
M. N. Abdul Rani ◽  
M. A. Yunus
Keyword(s):  
Finite Element ◽  
Dynamic Behaviour ◽  
Model Updating ◽  
Complex Structure ◽  
Bolted Joints ◽  
Model Parameters ◽  
Fe Model ◽  
Bolted Joint ◽  
Fe Modelling ◽  
Joint Structure

Efficient and accurate finite element (FE) modelling of bolted joints is essential for increasing confidence in the investigation of structural vibrations. However, modelling of bolted joints for the investigation is often found to be very challenging. This paper proposes an appropriate FE representation of bolted joints for the prediction of the dynamic behaviour of a bolted joint structure. Two different FE models of the bolted joint structure with two different FE element connectors, which are CBEAM and CBUSH, representing the bolted joints are developed. Modal updating is used to correlate the two FE models with the experimental model. The dynamic behaviour of the two FE models is compared with experimental modal analysis to evaluate and determine the most appropriate FE model of the bolted joint structure. The comparison reveals that the CBUSH element connectors based FE model has a greater capability in representing the bolted joints with 86 percent accuracy and greater efficiency in updating the model parameters. The proposed modelling technique will be useful in the modelling of a complex structure with a large number of bolted joints.

Micromechanics of damage propagation in fluid-saturated cracked media

2007 ◽  
Vol 11 (7-8) ◽  
pp. 945-962 ◽  
Author(s):  
Luc Dormieux ◽  
Djimédo Kondo
Keyword(s):  
Damage Propagation

DEFORMATION AND CRACKING OF THE PLATFORM JOINT OF THE PREFABRICATED-MONOLITHICRC BUILDING FRAME

2020 ◽  
Vol 90 (4) ◽  
pp. 38-47
Author(s):  
VL.I. KOLCHUNOV ◽  
◽  
D.V. MARTYNENKO ◽  
Keyword(s):  
Reinforced Concrete ◽  
Plane Stress State ◽  
Hollow Core ◽  
Finite Element Scheme ◽  
Element Scheme ◽  
Joint Structure ◽  
Numerical Studies ◽  
Distributed Load ◽  
Different Types ◽  
Design Characteristics

A computational model and the results of numerical studies of the structure of a platform joint in a reinforced concrete precast-monolithic frame of a building from panel-frame elements of industrial production are presented. Modeling of the plane stress state of the joint structure is carried out by a finite element scheme, using finite elements of different types and a nonlinear law of deformation to determine the design characteristics of reinforced concrete. The parameters of deformation of the platform joint structure at different loading levels, including stage-by-stage cracking and destruction, have been determined. The schemes of distribution and stress concentration zones in the characteristic sections of the platform joint are established when the distributed load is transferred from the frame of the panel-frame to the hollow-core floor panels and concrete for embedding the joint in the presence of a cavity in the frame frame for centering elements.

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