NDLI: Probability Analysis of Static Frequency and Dynamic Frequency of Steam Turbine Blade Based on RBF Neural Network and Monte Carlo Simulation

Content Provider	IEEE Xplore Digital Library
Author	Wei Duan Zhang-Qi Wang
Copyright Year	2007
Description	Author affiliation: North China Electr. Power Univ., Baoding (Wei Duan; Zhang-Qi Wang)
Abstract	Dynamic stress of steam turbine blade has great influence on its reliability and fatigue life. In order to decrease the magnitude of dynamic stress, frequency modulation method is often used to avoid resonance, which implies the frequency of active force must be kept away from the inherent vibration frequency of blade. At present, many models of calculating inherent vibration frequency of blade are deterministic, which didn't consider the randomness of many parameters (such as loading parameters, geometric parameters, material parameters) in practical operation. So, in this paper, an equal cross-section blade is investigated and a finite element model is built parametrically. Geometrical parameters (such as length, width and thickness), material parameters (such as young's modulus and density) and rotation speed of blade are treated as input random variables while the static frequencies and dynamic frequencies are treated as output random variables. The radial basis function (RBF) neural network is adopted to model and approximate the implicit function between the inherent frequencies and input random variables. The RBF technique uses a small set of the actual data which are obtained from calling the finite element solver several times and are used to develop a trained RBF algorithm. Then a large number of function values can be obtained from the established and successfully trained RBF network and are applied by Monte Carlo simulation to obtain the statistical characteristics and cumulative distribution functions of static frequencies and dynamic frequencies of the blade. The results demonstrate that the FEM-RBF-Monte Carlo method is an optional approach for the dynamic strength reliability design of the blade as RBF network has high regression precision and rapid calculation.
Starting Page	3512
Ending Page	3517
File Size	571786
Page Count	6
File Format	PDF
ISBN	9781424409723
DOI	10.1109/ICMLC.2007.4370755
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2007-08-19
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Turbines Blades Neural networks Random variables Stress Finite element methods Radial basis function networks Fatigue Frequency modulation Resonance Monte Carlo simulation Blade Static (dynamic) frequency Probability analysis Radial Basis Function neural network Finite element
Content Type	Text
Resource Type	Article

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in