On the use of neural networks for dynamic stress prediction in Francis turbines by means of stationary sensors

2021 ◽  
Vol 170 ◽  
pp. 652-660
Author(s):  
Alexandre Presas ◽  
David Valentin ◽  
Weiqiang Zhao ◽  
Mònica Egusquiza ◽  
Carme Valero ◽  
...  
Keyword(s):  
Neural Networks ◽  
Dynamic Stress ◽  
Stress Prediction

scholarly journals Nonlinear Data Transfonnation to Improve Flow Stress Prediction Using Neural Networks

2004 ◽  
Vol 37 (15) ◽  
pp. 371-376
Author(s):  
Y.Y. Yang ◽  
D.A. Linkens ◽  
M. Mahfouf
Keyword(s):  
Neural Networks ◽  
Flow Stress ◽  
Stress Prediction

scholarly journals Deep Neural Networks for Maximum Stress Prediction in Piping Design

2019 ◽  
Vol 19 (3) ◽  
pp. 140-146 ◽  
Author(s):  
Sang-jin Oh ◽  
Chae-og Lim ◽  
Byeong-choel Park ◽  
Jae-chul Lee ◽  
Sung-chul Shin
Keyword(s):  
Neural Networks ◽  
Deep Neural Networks ◽  
Maximum Stress ◽  
Stress Prediction

Superiority of neural networks for pillar stress prediction in bord and pillar method

2009 ◽  
Vol 4 (5-6) ◽  
pp. 845-853 ◽  
Author(s):  
M. Monjezi ◽  
Seyed Masoud Hesami ◽  
Manoj Khandelwal
Keyword(s):  
Neural Networks ◽  
Stress Prediction

Dynamic Stress Predictions of Acoustic-Induced Pipe Vibration Failures

2005 ◽  
Author(s):  
Denis G. Karczub ◽  
Allen C. Fagerlund
Keyword(s):  
Dynamic Stress ◽  
Broad Band ◽  
Fatigue Properties ◽  
Concentration Effects ◽  
Blow Down ◽  
Failure Data ◽  
Vibration Prediction ◽  
Piping Systems ◽  
Stress Prediction ◽  
Acoustic Fatigue

The dynamic stress prediction methodology developed by Norton [1] for broad-band acoustic-induced vibration of piping systems is applied here to the failure data of Carucci and Mueller [2]. Proprietary noise and vibration prediction technologies are used in order to improve the accuracy and robustness of the predictions. This results in generalized dynamic stress predictions that clearly delineate the cases of acoustic fatigue and satisfactory operation documented by Carucci and Mueller, unlike other failure prediction procedures traditionally used by industry. Other advantages of the dynamic stress approach include (i) a theoretically sound approach to account for pipe wall thickness, pipe diameter and internal density; (ii) direct consideration of material fatigue properties and stress concentration effects; and (iii) the potential to evaluate fatigue life for transient blow-down conditions.

Effects of Both Axial Gap and Blade Count Ratio on the Forced Response of a Steam Turbine Stage

2013 ◽  
Author(s):  
Tie Chen ◽  
John Rogerson ◽  
Kush Patel
Keyword(s):  
Steam Turbine ◽  
Dynamic Stress ◽  
Fluid Dynamic ◽  
Forced Response ◽  
Steam Turbines ◽  
Turbine Stage ◽  
Unsteady Forces ◽  
Count Ratio ◽  
Stress Prediction ◽  
Coupling Dynamic

Rotating blades of steam turbines are subjected to unsteady forces due to the presence of both wake and potential field from the adjacent stationary blade rows. The combined effects of both axial gap and blade count ratio on the unsteady forces have been demonstrated on a steam turbine stage. This study shows their effects on the forced response. Further investigations show their effects on stimulus, aero damping and frequency shift. This study uses a one-way coupling dynamic stress prediction tool, which integrates an in-house Computational Fluid Dynamic solver TF3D-VIB with a commercial Finite Element solver ABAQUS.

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