scholarly journals Genetic Algorithm Based Critical Experiment Design for Uranium Cross Section Validation

2018 ◽  
Author(s):  
Dominik Arthur Fritz
Keyword(s):  
Genetic Algorithm ◽  
Cross Section ◽  
Experiment Design ◽  
Critical Experiment

scholarly journals Optimization of vane demister based on neural network and genetic algorithm

2019 ◽  
Vol 11 (3) ◽  
pp. 168781401983510 ◽  
Author(s):  
San He ◽  
Hang Liu ◽  
Yongli Zou ◽  
Qinqin Zhang
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Pressure Drop ◽  
Chemical Engineering ◽  
Separation Efficiency ◽  
Orthogonal Experiment ◽  
Structural Parameters ◽  
Experiment Design ◽  
Orthogonal Experiment Design ◽  
Operation Parameters

A vane demister is widely used for separating tiny droplets from gas streams in the petroleum industry, chemical engineering, and other industries. To obtain optimal structure and operation parameters, a method based on orthogonal experiment design is often adopted. However, in most cases, results from an orthogonal experiment design are suboptimal solutions when there are fewer experiments to optimize the vane demister performance. In this study, to obtain the maximum separation efficiency and minimum pressure drop, Fluent software was used to simulate the two-phase flow of gas and liquid in vane demister with different structural parameters and operation parameters, generating 473 solutions as the sample database. Based on this database, a back propagation neural network was used to establish the prediction model for the separation efficiency and pressure drop, and a genetic algorithm was used for multi-target optimization of this model. The optimization results were compared to Fluent simulation results and the orthogonal experiment design results. The results show that a genetic algorithm generates better results. The optimal separation efficiency of both methods is 100%. However, the optimal pressure drop of the genetic algorithm is 25.77% lower than that of the orthogonal experiment design.

scholarly journals Vibration-Based Damage Detection in Beams by Cooperative Coevolutionary Genetic Algorithm

2014 ◽  
Vol 6 ◽  
pp. 624949 ◽  
Author(s):  
Kittipong Boonlong
Keyword(s):  
Genetic Algorithm ◽  
Damage Detection ◽  
Cantilever Beam ◽  
Cross Section ◽  
Random Noise ◽  
Vibration Characteristics ◽  
Mode Shapes ◽  
Test Problems ◽  
Uniform Cross Section ◽  
Coevolutionary Genetic Algorithm

Vibration-based damage detection, a nondestructive method, is based on the fact that vibration characteristics such as natural frequencies and mode shapes of structures are changed when the damage happens. This paper presents cooperative coevolutionary genetic algorithm (CCGA), which is capable for an optimization problem with a large number of decision variables, as the optimizer for the vibration-based damage detection in beams. In the CCGA, a minimized objective function is a numerical indicator of differences between vibration characteristics of the actual damage and those of the anticipated damage. The damage detection in a uniform cross-section cantilever beam, a uniform strength cantilever beam, and a uniform cross-section simply supported beam is used as the test problems. Random noise in the vibration characteristics is also considered in the damage detection. In the simulation analysis, the CCGA provides the superior solutions to those that use standard genetic algorithms presented in previous works, although it uses less numbers of the generated solutions in solution search. The simulation results reveal that the CCGA can efficiently identify the occurred damage in beams for all test problems including the damage detection in a beam with a large number of divided elements such as 300 elements.

scholarly journals CRITICAL EXPERIMENT OF THORIUM LOADED THERMAL CORES AT KUCA (1) A NEW CRITICAL EXPERIMENT OF THORIUM LOADED CORE WITH HARDER NEUTRON SPECTRUM IN KUCA

2021 ◽  
Vol 247 ◽  
pp. 09013
Author(s):  
Tadafumi Sano ◽  
Jun-ichi Hori ◽  
Jeaong Lee ◽  
Yoshiyuki Takahashi ◽  
Kazuki Takahashi ◽  
...  
Keyword(s):  
Cross Section ◽  
Neutron Spectrum ◽  
Cross Sections ◽  
Capture Cross Section ◽  
The Other ◽  
Capture Cross ◽  
Delayed Neutron ◽  
Integral Evaluation ◽  
Critical Experiment ◽  
The Difference

In order to perform integral evaluation of 232Th capture cross section, a series of critical experiments for thorium-loaded and solid-moderated cores in KUCA had been carried out. In these experimental cores, H/235U nuclide ratio ranged about from 150 to 315, and 232Th/235U nuclide ratio ranged about from 13 to 19. In this study, a new critical experiment with Th loaded core in KUCA, which had about 70 of the H/235U ratio and 12.7 of 232Th/235U ratio, was carried out. As results, the excess reactivity was 0.086 ± 0.003 (% dk/k) and the keff was 1.0009 ± 0.0003, where the effective delayed neutron fraction was 7.656E-3. The keff was also calculated by MVP3.0 with different nuclear libraries. The respective calculations with JENDL-4.0, JENDL-3.3 and ENDF/B-VII.0 lead to 1.0056 ± 0.0086 (%), 1.0048 ± 0.0085 (%) and 1.0056 ± 0.0086 (%).On the other hand, the further MVP3.0 calculations, where only the 232Th cross sections were taken from JENDL-4.0, JENDL-3.3 or ENDF/B-VII.0 but all other nuclides were done from JENDL-4.0, were carried out to examine an impact of the difference of 232Th cross section among these nuclear libraries to the keff. The keff calculated with respective 232Th cross sections from JENDL-3.3 and ENDF/B-VII.0 was 1.0038 ± 0.0086 (%) and 1.0040 ± 0.0086 (%).

Shape and Cross-Section Optimization of Spatial Grid Structures Using Genetic Algorithm

2012 ◽  
Vol 479-481 ◽  
pp. 1463-1467
Author(s):  
Jian Chun Xiao ◽  
Jing Chen ◽  
Qi Li ◽  
Shao Quan Xia
Keyword(s):  
Genetic Algorithm ◽  
Cross Section ◽  
Physical Property ◽  
Structural Deformation ◽  
Improved Genetic Algorithm ◽  
Integer Variables ◽  
Optimization Function ◽  
Spatial Grid ◽  
The Stability ◽  
Mixed Codes

The optimization of the structures is difficult because the variables have different physical property or different quantitative attribute. The shape and cross-section optimization of spatial grid structures is performed by an improved genetic algorithm. The constraint conditions are composed of the structural deformation, the stability of the compressive members, the slender ratios, and etc. The treatment of the constraint conditions and the optimization function gives an unconstrained analytic function by adopting Lagrange multipliers. The method enhances the running efficiency of the genetic algorithm. The programme for structural optimization containing the mixed codes of continuous real variables, discontinuous real variables, and integer variables is coded by using MATLAB Toolbox functions for genetic algorithm. The analysis of examples shows that the programme is reliable, and the convergence of the algorithm is fast as well.

scholarly journals Evolutionary algorithm based approach to impedance design

2002 ◽  
Vol 15 (2) ◽  
pp. 175-184
Author(s):  
Dejana Herceg ◽  
Neda Pekaric-Nadj
Keyword(s):  
Genetic Algorithm ◽  
Evolutionary Algorithm ◽  
Cross Section ◽  
High Power ◽  
High Permeability ◽  
Power Switching ◽  
Complex Cross ◽  
Linear Rise ◽  
Suitable Combination ◽  
Micro Genetic Algorithm

Bus bars of complex cross section were designed in order to achieve desirable characteristics for use with high power switching devices. High permeability material was introduced between the copper bus bars, to increase non-linear rise of the bars' resistance with frequency. Micro genetic algorithm (?GA) was used to search for the most suitable combination of parameters. The main goal was to design bus bars with adequately adapted resistance and inductance at 10 kHz and 50 Hz simultaneously. The frequency dependant resistance and inductance were calculated utilizing 2D FEM software.

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