Development and Analysis of Variable Pitch Propeller with Aerodynamic Stable Blades

2020 ◽  
pp. 1-12
Author(s):  
Zhongyun Fan ◽  
Zhou Zhou ◽  
Xiaoping Zhu
Keyword(s):  
Author(s):  
L Slătineanu ◽  
O Dodun ◽  
M Coteață ◽  
I Coman ◽  
G Nagîț ◽  
...  

Materials ◽  
2018 ◽  
Vol 12 (1) ◽  
pp. 112 ◽  
Author(s):  
Alex Iglesias ◽  
Zoltan Dombovari ◽  
German Gonzalez ◽  
Jokin Munoa ◽  
Gabor Stepan

Cutting capacity can be seriously limited in heavy duty face milling processes due to self-excited structural vibrations. Special geometry tools and, specifically, variable pitch milling tools have been extensively used in aeronautic applications with the purpose of removing these detrimental chatter vibrations, where high frequency chatter related to slender tools or thin walls limits productivity. However, the application of this technique in heavy duty face milling operations has not been thoroughly explored. In this paper, a method for the definition of the optimum angles between inserts is presented, based on the optimum pitch angle and the stabilizability diagrams. These diagrams are obtained through the brute force (BF) iterative method, which basically consists of an iterative maximization of the stability by using the semidiscretization method. From the observed results, hints for the selection of the optimum pitch pattern and the optimum values of the angles between inserts are presented. A practical application is implemented and the cutting performance when using an optimized variable pitch tool is assessed. It is concluded that with an optimum selection of the pitch, the material removal rate can be improved up to three times. Finally, the existence of two more different stability lobe families related to the saddle-node and flip type stability losses is demonstrated.


2012 ◽  
Vol 562-564 ◽  
pp. 1012-1015
Author(s):  
S.X. Wang ◽  
Z.X. Li ◽  
D.X. Sun ◽  
X.X. Xie

In order to avoid the limitations of traditional mechanism modeling method, a neural network (NN) model of variable - pitch wind turbine is built by the NN modeling method based on field data. Then considering that from wind turbine’s startup to grid integration, the generator speed must be controlled to rise to the synchronous speed smoothly and precisely, a neural network model predictive control (NNMPC) strategy based on the small-world optimization algorithm (SWOA) is proposed. Simulation results show that the strategy can forecast the change of generator rotational speed based on the wind speed disturbance, making the controller act ahead to eliminate the impact of system delay. Furthermore, the system output can track the reference trajectory well, making sure that the system can connect the electricity grid steadily.


2008 ◽  
Vol 44 (19) ◽  
pp. 1103 ◽  
Author(s):  
F. Yang ◽  
P. Zhang ◽  
C.-J. Guo ◽  
J.-D. Xu

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