scholarly journals Cavitation optimization of single-orifice plate using CFD method and neighborhood cultivation genetic algorithm

2021 ◽  
Author(s):  
Yu Zhang ◽  
Jiang Lai ◽  
Chao He ◽  
Shihao Yang
Keyword(s):  
Genetic Algorithm ◽  
Orifice Plate ◽  
Cfd Method

Comparison between thermal resistances of optimized water-based and gallium-based heat sinks using the genetic algorithm

2019 ◽  
Vol 30 (3) ◽  
pp. 1388-1406
Author(s):  
Xiong Xiang ◽  
Yu Fan ◽  
Wei Liu ◽  
Aiwu Fan
Keyword(s):  
Genetic Algorithm ◽  
Laminar Flow ◽  
Thermal Resistance ◽  
Heat Sink ◽  
Goal Function ◽  
Heat Sinks ◽  
Channel Height ◽  
Content Type ◽  
Water Based ◽  
Cfd Method

Purpose The purpose of this paper is to compare the thermal resistances between optimized gallium- and water-based heat sinks to show which one is superior. Design/methodology/approach Taking the thermal resistances of heat sinks as the goal function, an optimization process is programmed based on the genetic algorithm. The optimal channel/fin widths and the corresponding thermal resistances of gallium- and water-based heat sinks are obtained and compared with/without a laminar flow constraint. The analytic model and CFD method are applied in different situations to ensure sufficient accuracy. Findings The results show that in the laminar regime, the thermal resistance of optimized gallium-based heat sink is lower than the water-based counterpart in most cases, but the latter becomes better if it is long enough or the channel is sufficient high. Without the laminar constraint, the thermal resistance of the optimized gallium-based heat sink can be decreased by 33-45 per cent compared with the water-based counterparts. It is interesting to find that when the heat sink is long or the channel height is short, the optimal geometry of gallium-based heat sink is a mini gap. Originality/value This paper demonstrates that the cooling performance of gallium-based heat sink can be significantly improved by optimization without the laminar flow constraint.

Application of Machine Learning Algorithm in Optimization of PSV for 110000Dwt Oil Tanker

2020 ◽  
Author(s):  
Liu Xiyang ◽  
Chen Jingpu ◽  
Sun Wenyu ◽  
Xu Wei
Keyword(s):  
Genetic Algorithm ◽  
Energy Saving ◽  
Design Parameters ◽  
Analysis Method ◽  
Training Set ◽  
Test Set ◽  
Agent Model ◽  
Cfd Method ◽  
Saving Effect ◽  
Oil Tanker

Abstract The pre-shrouded vane (PSV) in front of propeller is a kind of energy-saving device which can change the inflow to improve the received power of the propeller. The device needs to be optimized according to the flow field of the stern. Most of the existing design methods rely on the experience of the designer. In order to improve the design efficiency of PSV and obtain a design scheme with higher energy-saving effect, this paper presents an optimization and analysis method for PSV in front of propeller based on agent model. Aiming at an 110000dwt oil tanker, 11 design parameters such as stator angle and duct radius are determined by means of parameterization. The design parameters are sampled by Latin hypercube sampling method (LH), and the sample space with 300 samples is generated. The energy-saving effect of each sample is analyzed by CFD method. The data set is formed and next divided into training set and test set. Then, machine learning methods are used to build the agent model of sample space. The error of each model in the test set is analyzed. To obtain the best model, the performance of several models in the test set and training set is considered. The applicability of different models is also highly considered. On this basis, the sensitivity analysis method is used to analyze the sensitivity of each design parameter. Then, the main influencing parameters are found. Finally, particle swarm optimization and genetic algorithm are compared to optimize the design parameters of PSV for 110000dwt oil tanker. The optimization results are verified by CFD method. The results show that the artificial neural network model is better on this dataset, and the model error on test set is less than 1% compared with the CFD result. The optimal solution by genetic algorithm method is better than all the sample points, and a better design scheme of PSV is obtained.

The ground state energy of the ±J spin glass from the genetic algorithm

1994 ◽  
Vol 4 (9) ◽  
pp. 1281-1285 ◽  
Author(s):  
P. Sutton ◽  
D. L. Hunter ◽  
N. Jan
Keyword(s):  
Genetic Algorithm ◽  
Spin Glass ◽  
Ground State Energy ◽  
Ground State ◽  
State Energy

A Comparative Study of Ant and Genetic Algorithms in Digital Mammography

2018 ◽  
pp. 194-200
Author(s):  
J. Magelin Mary ◽  
Chitra K. ◽  
Y. Arockia Suganthi
Keyword(s):  
Genetic Algorithm ◽  
Image Processing ◽  
Medical Image ◽  
Medical Image Processing ◽  
Processing Technique ◽  
Input Image ◽  
Image Processing Technique ◽  
Computational Time ◽  
Image Mining ◽  
The Individual

Image processing technique in general, involves the application of signal processing on the input image for isolating the individual color plane of an image. It plays an important role in the image analysis and computer version. This paper compares the efficiency of two approaches in the area of finding breast cancer in medical image processing. The fundamental target is to apply an image mining in the area of medical image handling utilizing grouping guideline created by genetic algorithm. The parameter using extracted border, the border pixels are considered as population strings to genetic algorithm and Ant Colony Optimization, to find out the optimum value from the border pixels. We likewise look at cost of ACO and GA also, endeavors to discover which one gives the better solution to identify an affected area in medical image based on computational time.

Sign in / Sign up

Export Citation Format

Share Document