Optimization of radial active magnetic bearings using the finite element technique and the differential evolution algorithm

2000 ◽  
Vol 36 (4) ◽  
pp. 1009-1013 ◽  
Author(s):  
K. Hameyer ◽  
U. Palmer ◽  
D. Dolinar ◽  
G. Stumberger
Keyword(s):  
Finite Element ◽  
Differential Evolution ◽  
Differential Evolution Algorithm ◽  
Magnetic Bearings ◽  
Active Magnetic Bearings ◽  
Finite Element Technique ◽  
Evolution Algorithm ◽  
Element Technique

Displacement Back Analysis Based on Return Mapping Algorithm and Differential Evolution Algorithm

2011 ◽  
Vol 301-303 ◽  
pp. 564-568
Author(s):  
Jun Xiang Wang ◽  
An Nan Jiang
Keyword(s):  
Finite Element ◽  
Differential Evolution ◽  
Differential Evolution Algorithm ◽  
Implicit Integration ◽  
Integration Algorithm ◽  
Mapping Algorithm ◽  
Return Mapping ◽  
Return Mapping Algorithm ◽  
Calculation Step ◽  
Evolution Algorithm

Differential evolution algorithm is a new global optimization algorithm. DE does not require an initial value, and it has rapid convergence, strong adaptability to a nonlinear function, the features of parallelcalculation, especially in adoption to the complex problem of multivariable optimization. The constitutive integration algorithm affecting the incremental calculation step, and convergence and accuracy of the results is a key of finite element analysis. It is usually divided into an explicit and implicit integration. Return mapping algorithm is an implicit integration to avoid solving the equivalent plastic strain directly so that we achieve a fast and accurate solution for the constitutive equations. Making use of DE and return mapping algorithm to program, the elasticplastic finite element simulation and parameter inversion, the inversion and simulation results are verificated, the results show that it is closed to the actual situation, indicating usefulness and correctness of the program.

Applying Bionic Intelligent Algorithm Combined with Finite Element Method to Optimize Cable

2013 ◽  
Vol 380-384 ◽  
pp. 1629-1632
Author(s):  
Zhi Peng Jiang ◽  
Xi Shan Wen ◽  
Xiao Qing Yuan
Keyword(s):  
Finite Element Method ◽  
Finite Element ◽  
Particle Swarm Optimization ◽  
Differential Evolution ◽  
Differential Evolution Algorithm ◽  
Particle Swarm ◽  
Intelligent Algorithm ◽  
Swarm Optimization ◽  
Evolution Algorithm ◽  
Element Method

The paper adopts bionic intelligent algorithm including particle swarm optimization and differential evolution algorithm combined with finite element method to optimize cable on the platform of ANSYS finite element soft. Parametric programming of a single-phase cable and a three-phase cable is accomplished to optimize the maximum electric field strength of cable insulation layer by using particle swarm optimization and differential evolution algorithm combined with finite element method, that provides enlightenment for optimizing high-voltage equipment in other aspects of electromagnetic field.

scholarly journals Optimization of surface-mounted permanent magnet brushless AC motor using analytical model and differential evolution algorithm

2019 ◽  
Vol 70 (3) ◽  
pp. 208-217 ◽  
Author(s):  
Mohd Rezal Mohamed ◽  
Dahaman Ishak
Keyword(s):  
Finite Element ◽  
Finite Element Model ◽  
Differential Evolution ◽  
Permanent Magnet ◽  
Differential Evolution Algorithm ◽  
Element Model ◽  
Torque Ripple ◽  
Domain Model ◽  
Computational Time ◽  
Evolution Algorithm

Abstract This paper discusses the optimization of surface-mounted permanent magnet brushless AC (PMBLAC) motor using Analytical Sub-domain model with Differential Evolution Algorithm (ASDEA). Only two regions were considered in this analytical sub-domain model, ie magnet and airgap regions, with assistance of Complex Relative Permeance Function (CRPF) to account for the stator slotting effect. Five machine parameters were chosen to be optimized, namely the magnet arc-pole-pitch ratio, slot opening width, magnet thickness, airgap length and stator inner radius. The optimization process has four objectives, ie minimum torque ripple, low cogging torque, high efficiency, and high output torque. The results from the optimized ASDEA were compared with the Analytical Sub-domain Genetic Algorithm (ASGA) and further validated against 2-D finite element model (FEM). Results show a good agreement between analytically optimized models and finite element model. The ASDEA has faster computational time compared to ASGA, and this provides benefit in terms of reducing the machine design parameterization time and less redundancy work required to achieve motor design specifications.

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