Design Optimization for Fir-Tree Root of Turbine Blade Considering Manufacturing Variations

2013 ◽  
Vol 694-697 ◽  
pp. 2733-2737
Author(s):  
Qin Zhou ◽  
Ming Hui Zhang ◽  
Hui Yong Chen ◽  
Yong Hui Xie
Keyword(s):  
Particle Swarm Optimization ◽  
Turbine Blade ◽  
Equivalent Stress ◽  
Particle Swarm ◽  
Pattern Search ◽  
Design System ◽  
Contact Method ◽  
Swarm Optimization ◽  
Maximum Equivalent Stress ◽  
Manufacturing Variation

An optimization design system for fir-tree root of turbine blade has been developed in this paper. In the system, a parametric model of the blade and rim was established based on the parametric design language APDL, and nonlinear contact method was used for analysis by ANSYS, meanwhile some optimization algorithms, such as Pattern Search Algorithm, Genetic Algorithm, Simulated Annealing Algorithm and Particle Swarm Optimization, were adopted to control the optimizing process. Five cases of manufacturing variation in contact surfaces between root and rim were taken into account, and the design objective was to minimize the maximum equivalent stress of root-rim by optimizing eight critical geometrical dimensions of the root and rim. As a result, the maximum equivalent stress of root-rim decreases markedly after the optimization in all cases. In consideration of both precision and computing time, particle swarm optimization is assessed as the best algorithm to solve structure optimization problem in this work. Corresponding to five different cases of manufacturing variation, the maximum equivalent stress of root and rim reduces by 7%, 8%; 27%, 24%; 27%, 22%; 25%, 19%; 10%, 14% using the Particle Swarm Optimization.

Study on Identification of Damage to Wind Turbine Blade Based on Support Vector Machine and Particle Swarm Optimization

2015 ◽  
Vol 27 (3) ◽  
pp. 244-250 ◽  
Author(s):  
Guimei Gu ◽  
◽  
Rang Hu ◽  
Yuanyuan Li
Keyword(s):  
Support Vector Machine ◽  
Acoustic Emission ◽  
Particle Swarm Optimization ◽  
Wind Turbine ◽  
Turbine Blade ◽  
Damage Identification ◽  
Particle Swarm ◽  
Support Vector ◽  
Swarm Optimization ◽  
Upper Computer

<div class=""abs_img""> <img src=""[disp_template_path]/JRM/abst-image/00270003/03.jpg"" width=""340"" />Classification results of SVM-PSO</div> In order to identify two failures of crack damage and edge damage to wind turbine blade, a damage identification system was designed by acoustic emission technique. This system took advantage of wireless technique for signal collection and transmission and upper computer for receiving and processing data. This system adopted acoustic emission sensor, NRF905 wireless transmission, upper computer designed by VB language, and the serial communication function of VB for data receiving. Data was firstly normalized after being received. Then, the energy features of data were abstracted by db wavelet. With the abstracted features, support vector machine model was established and verified, and the machine parameters were optimized by particle swarm optimization. Results show that the system is reliable in data collection and transmission, and the correctness of damage identification obviously increases by optimizing the support vector machine with particle swarm. The design provides method to monitor the status of rotating object, so this system can provide model base for subsequent studies.

scholarly journals A COMPARISON OF SIMULATED ANNEALING, GENETIC ALGORITHM AND PARTICLE SWARM OPTIMIZATION IN OPTIMAL FIRST-ORDER DESIGN OF INDOOR TLS NETWORKS

2017 ◽  
Vol IV-2/W4 ◽  
pp. 75-82 ◽  
Author(s):  
F. Jia ◽  
D. Lichti
Keyword(s):  
Genetic Algorithm ◽  
Particle Swarm Optimization ◽  
Simulated Annealing ◽  
Particle Swarm ◽  
Optimal Solution ◽  
Optimization Methods ◽  
Network Design Problem ◽  
Design System ◽  
Swarm Optimization ◽  
First Order

The optimal network design problem has been well addressed in geodesy and photogrammetry but has not received the same attention for terrestrial laser scanner (TLS) networks. The goal of this research is to develop a complete design system that can automatically provide an optimal plan for high-accuracy, large-volume scanning networks. The aim in this paper is to use three heuristic optimization methods, simulated annealing (SA), genetic algorithm (GA) and particle swarm optimization (PSO), to solve the first-order design (FOD) problem for a small-volume indoor network and make a comparison of their performances. The room is simplified as discretized wall segments and possible viewpoints. Each possible viewpoint is evaluated with a score table representing the wall segments visible from each viewpoint based on scanning geometry constraints. The goal is to find a minimum number of viewpoints that can obtain complete coverage of all wall segments with a minimal sum of incidence angles. The different methods have been implemented and compared in terms of the quality of the solutions, runtime and repeatability. The experiment environment was simulated from a room located on University of Calgary campus where multiple scans are required due to occlusions from interior walls. The results obtained in this research show that PSO and GA provide similar solutions while SA doesn’t guarantee an optimal solution within limited iterations. Overall, GA is considered as the best choice for this problem based on its capability of providing an optimal solution and fewer parameters to tune.

scholarly journals 5GHz MMIC LNA Design Using Particle Swarm Optimization

2013 ◽  
Vol 5 (6) ◽  
pp. 257-262
Author(s):  
Low Wen Shin ◽  
Arjuna Marzuki .
Keyword(s):  
Particle Swarm Optimization ◽  
Integrated Circuit ◽  
Noise Figure ◽  
Particle Swarm ◽  
Low Noise ◽  
Design System ◽  
Stability Factor ◽  
Power Gain ◽  
Swarm Optimization ◽  
5 Ghz

This research presents an optimization study of a 5 GHz Monolithic Microwave Integrated Circuit (MMIC) design using Particle Swarm Optimization (PSO). MMIC Low Noise Amplifier (LNA) is a type of integrated circuit device used to capture signal operating in the microwave frequency. This project consists of two stages: implementation of PSO using MATLAB and simulation of MMIC design using Advanced Design System (ADS). PSO model that mimics the biological swarm behavior is developed to optimize the MMIC design variables in order to achieve the required circuit performance and specifications such as power gain, noise figure, drain current and circuit stability factor. Simulation results show that the proposed MMIC design fulfills the circuit stability factor and achieves a power gain of 19.73dB, a noise figure of 1.15 dB and a current of 0.0467A.

A Particle Swarm Optimization Algorithm Based on the Pattern Search Method

2012 ◽  
Vol 532-533 ◽  
pp. 1664-1669 ◽  
Author(s):  
Jun Li Zhang ◽  
Da Wei Dai
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Global Optimum ◽  
Pattern Search ◽  
Local Optimum ◽  
Effective Mechanism ◽  
Swarm Optimization

For the purpose of overcoming the premature property and low execution efficiency of the Particle Swarm Optimization (PSO) algorithm, this paper presents a particle swarm optimization algorithm based on the pattern search. In this algorithm， personal and global optimum particles are chosen in every iteration by a probability. Then, local optimization will be performed by the pattern search and then the original individuals will be replaced. The strong local search function of the pattern search provides an effective mechanism for the PSO algorithm to escape from the local optimum, which avoids prematurity of the algorithm. Simulation shows that this algorithm features a stronger function of global search than conventional PSO, so that the optimization process can be improved remarkably.

Clustering and pattern search for enhancing particle swarm optimization with Euclidean spatial neighborhood search

2016 ◽  
Vol 171 ◽  
pp. 966-981 ◽  
Author(s):  
Xinchao Zhao ◽  
Wenqiao Lin ◽  
Junling Hao ◽  
Xingquan Zuo ◽  
Jianhua Yuan
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Pattern Search ◽  
Neighborhood Search ◽  
Swarm Optimization
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