Optimization of bragg grating in optical fiber using modified fitness function and an accelerated genetic algorithm

2006 ◽  
Author(s):  
Marco Jos de Sousa ◽  
Claudomiro Souza Sales ◽  
Joo Chamma C. Carvalho ◽  
Joo Crisstomo Weyl Albuquerque Costa ◽  
C. R. L. Francs ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Optical Fiber ◽  
Fitness Function ◽  
Bragg Grating

Genetic Algorithm for the Reconstruction of Bragg Grating Sensor Strain Distribution

Aerospace ◽  
2003 ◽  
Author(s):  
Apninder Gill ◽  
Kara Peters ◽  
Michel Studer
Keyword(s):  
Genetic Algorithm ◽  
Optical Fiber ◽  
Strain Distribution ◽  
Axial Strain ◽  
Simulated Data ◽  
Strain Sensors ◽  
Bragg Grating ◽  
Inversion Method ◽  
Period Distribution ◽  
Bragg Grating Sensor

Optical fiber Bragg gratings are unique among embedded strain sensors due to their potential to measure strain distributions with a spatial resolution of a few nanometers over gage lengths of a few centimeters. This article presents a genetic algorithm for the interrogation of optical fiber Bragg grating strain sensors. The method calculates the period distribution along the Bragg grating which can then be directly related to the axial strain distribution. The period distribution is determined from the output intensity spectrum of the grating via a T-matrix approach. The genetic algorithm inversion method presented requires only intensity information and reconstructs non-linear and discontinuous distributions well, including regions with significant gradients. The method is demonstrated through example reconstructions of Bragg grating sensor simulated data. The development of this algorithm will permit the use of Bragg grating sensors for damage identification in regions close to localized damages where strong strain non-linearities occur.

scholarly journals Application of Genetic Algorithm Elements to Modelling of Rotation Processes in Motion Transmission Including a Long Shaft

Energies ◽  
2020 ◽  
Vol 14 (1) ◽  
pp. 115
Author(s):  
Andriy Chaban ◽  
Marek Lis ◽  
Andrzej Szafraniec ◽  
Radoslaw Jedynak
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Fitness Function ◽  
Least Square ◽  
Distributed Parameter ◽  
Parameter Method ◽  
Analytical Mechanics ◽  
Parameter System ◽  
Rotational Systems ◽  
Elastic Elements

Genetic algorithms are used to parameter identification of the model of oscillatory processes in complicated motion transmission of electric drives containing long elastic shafts as systems of distributed mechanical parameters. Shaft equations are generated on the basis of a modified Hamilton–Ostrogradski principle, which serves as the foundation to analyse the lumped parameter system and distributed parameter system. They serve to compute basic functions of analytical mechanics of velocity continuum and rotational angles of shaft elements. It is demonstrated that the application of the distributed parameter method to multi-mass rotational systems, that contain long elastic elements and complicated control systems, is not always possible. The genetic algorithm is applied to determine the coefficients of approximation the system of Rotational Transmission with Elastic Shaft by equivalent differential equations. The fitness function is determined as least-square error. The obtained results confirm that application of the genetic algorithms allow one to replace the use of a complicated distributed parameter model of mechanical system by a considerably simpler model, and to eliminate sophisticated calculation procedures and identification of boundary conditions for wave motion equations of long elastic elements.

scholarly journals Hybrid Optimization Based Mathematical Procedure for Dimensional Synthesis of Slider-Crank Linkage

Mathematics ◽  
2021 ◽  
Vol 9 (13) ◽  
pp. 1581
Author(s):  
Alfonso Hernández ◽  
Aitor Muñoyerro ◽  
Mónica Urízar ◽  
Enrique Amezua
Keyword(s):  
Genetic Algorithm ◽  
Fitness Function ◽  
Linear Equations ◽  
Optimization Procedure ◽  
Optimization Techniques ◽  
Dimensional Synthesis ◽  
Hybrid Strategy ◽  
Dimensional Parameters ◽  
Optimization Methodology ◽  
Crank Mechanism

In this paper, an optimization procedure for path generation synthesis of the slider-crank mechanism will be presented. The proposed approach is based on a hybrid strategy, mixing local and global optimization techniques. Regarding the local optimization scheme, based on the null gradient condition, a novel methodology to solve the resulting non-linear equations is developed. The solving procedure consists of decoupling two subsystems of equations which can be solved separately and following an iterative process. In relation to the global technique, a multi-start method based on a genetic algorithm is implemented. The fitness function incorporated in the genetic algorithm will take as arguments the set of dimensional parameters of the slider-crank mechanism. Several illustrative examples will prove the validity of the proposed optimization methodology, in some cases achieving an even better result compared to mechanisms with a higher number of dimensional parameters, such as the four-bar mechanism or the Watt’s mechanism.

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