Numerical optimization for stator vane settings of multi-stage compressors based on neural networks and genetic algorithms

2016 ◽  
Vol 52 ◽  
pp. 81-94 ◽  
Author(s):  
Bo Li ◽  
Chun-wei Gu ◽  
Xiao-tang Li ◽  
Tai-qiu Liu
Keyword(s):  
Neural Networks ◽  
Genetic Algorithms ◽  
Numerical Optimization ◽  
Multi Stage ◽  
Stator Vane

Axial-Flow Compressor Model Based on a Cascade Stacking Technique and Neural Networks

2002 ◽  
Author(s):  
Ernesto Benini ◽  
Andrea Toffolo
Keyword(s):  
Neural Networks ◽  
Three Dimensional ◽  
Axial Flow ◽  
Design Tool ◽  
Preliminary Design ◽  
Cfd Simulations ◽  
Axial Flow Compressor ◽  
Multi Stage ◽  
Stator Vane ◽  
Flow Compressor

This paper introduces a cascade-stacking technique for the development of a gas turbine multi-stage axial-flow compressor model. A large database of stationary and rotating cascade performance is first obtained by quasi three-dimensional CFD simulations and used to train neural networks for the prediction of cascade performance under generalized conditions. Then the model directly calculates the operating point of a compressor having known geometry characteristics, including variable inlet guide/stator vane effects, as a function of mass flow rate and rotational speed. The model can also be used as a valuable preliminary design tool, obtaining geometry characteristics by imposing flow patterns.

Comparative analysis of intelligent planning methods

2012 ◽  
Vol 9 (2) ◽  
pp. 53-57 ◽  
Author(s):  
O.V. Darintsev ◽  
A.B. Migranov
Keyword(s):  
Neural Networks ◽  
Genetic Algorithms ◽  
Fuzzy Logic ◽  
Comparative Analysis ◽  
Mobile Robots ◽  
Sensory Information ◽  
Working Environment ◽  
Planning Methods ◽  
Intelligent Planning

The main stages of solving the problem of planning movements by mobile robots in a non-stationary working environment based on neural networks, genetic algorithms and fuzzy logic are considered. The features common to the considered intellectual algorithms are singled out and their comparative analysis is carried out. Recommendations are given on the use of this or that method depending on the type of problem being solved and the requirements for the speed of the algorithm, the quality of the trajectory, the availability (volume) of sensory information, etc.

scholarly journals Multi-Stage Feature Selection by Using Genetic Algorithms for Fault Diagnosis in Gearboxes Based on Vibration Signal

Sensors ◽  
2015 ◽  
Vol 15 (9) ◽  
pp. 23903-23926 ◽  
Author(s):  
Mariela Cerrada ◽  
René Sánchez ◽  
Diego Cabrera ◽  
Grover Zurita ◽  
Chuan Li
Keyword(s):  
Genetic Algorithms ◽  
Feature Selection ◽  
Fault Diagnosis ◽  
Vibration Signal ◽  
Multi Stage
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