scholarly journals Efficient Multi-Objective CFD-Based Optimization Method for a Scroll Distributor

Energies ◽  
2021 ◽  
Vol 14 (2) ◽  
pp. 377
Author(s):  
Damian Obidowski ◽  
Mateusz Stajuda ◽  
Krzysztof Sobczak
Keyword(s):  
Energy Savings ◽  
Axisymmetric Flow ◽  
Geometry Optimization ◽  
Optimization Method ◽  
Problem Size ◽  
Computational Point ◽  
Multi Objective ◽  
Flow Problems ◽  
Multi Objective Genetic Algorithm ◽  
Separation Zones

An efficient approach to the geometry optimization problem of a non-axisymmetric flow channel is discussed. The method combines geometrical transformation with a computational fluid dynamics solver, a multi-objective genetic algorithm, and a response surface. This approach, through geometrical modifications and simplifications allows transforming a non-axisymmetric problem into the axisymmetric one in some specific devices i.e., a scroll distributor or a volute. It results in a significant decrease in the problem size, as only the flow in a quasi-2D section of the channel is solved. A significantly broader design space is covered in a much shorter time than in the standard method, and the optimization of large flow problems is feasible with desktop-class computers. One computational point is obtained approximately eight times faster than in full geometry computations. The method was applied to a scroll distributor. For the case under analysis, it was possible to increase flow uniformity, eradicate separation zones, and increase the overall efficiency, which was followed by energy savings of 16% for the scroll. The results indicate that this method can be successfully applied for the optimization of similar problems.

A Community Detection Method Based on Multi-Objective Optimization Method

2014 ◽  
Vol 571-572 ◽  
pp. 177-182 ◽  
Author(s):  
Lu Wang ◽  
Yong Quan Liang ◽  
Qi Jia Tian ◽  
Jie Yang ◽  
Chao Song ◽  
...  
Keyword(s):  
Community Detection ◽  
Detection Method ◽  
Optimization Method ◽  
Research Area ◽  
Multi Objective ◽  
Detection Algorithms ◽  
Multi Objective Genetic Algorithm ◽  
Community Fitness ◽  
Active Research ◽  
Active Research Area

Community detection in complex network has been an active research area in data mining and machine learning. This paper proposed a community detection method based on multi-objective evolutionary algorithm, named CDMOEA, which tries to find the Pareto front by maximize two objectives, community score and community fitness. Fast and Elitist Multi-objective Genetic Algorithm is used to attained a set of optimal solutions, and then use Modularity function to choose the best one from them. The locus based adjacency representation is used to realize genetic representation, which ensures the effective connections of the nodes in the network during the process of population Initialization and other genetic operator. Uniform crossover is introduced to ensure population’s diversity. We compared it with some popular community detection algorithms in computer generated network and real world networks. Experiment results show that it is more efficient in community detection.

scholarly journals A Smart-Distributed Pareto Front Using the ev-MOGA Evolutionary Algorithm

2014 ◽  
Vol 23 (02) ◽  
pp. 1450002 ◽  
Author(s):  
J. M. Herrero ◽  
G. Reynoso-Meza ◽  
M. Martínez ◽  
X. Blasco ◽  
J. Sanchis
Keyword(s):  
Pareto Front ◽  
Optimization Methods ◽  
Optimization Method ◽  
Initial Condition ◽  
Multi Objective Optimization ◽  
Computational Burden ◽  
Multi Objective ◽  
Multi Objective Genetic Algorithm ◽  
Normalized Normal Constraint ◽  
Normal Constraint

Obtaining multi-objective optimization solutions with a small number of points smartly distributed along the Pareto front is a challenge. Optimization methods, such as the normalized normal constraint (NNC), propose the use of a filter to achieve a smart Pareto front distribution. The NCC optimization method presents several disadvantages related with the procedure itself, initial condition dependency, and computational burden. In this article, the epsilon-variable multi-objective genetic algorithm (ev-MOGA) is presented. This algorithm characterizes the Pareto front in a smart way and removes the disadvantages of the NNC method. Finally, examples of a three-bar truss design and controller tuning optimizations are presented for comparison purposes.

A New Interval Multi-Objective Optimization Method for Uncertain Problems with Dependent Interval Variables

2020 ◽  
Vol 17 (10) ◽  
pp. 2050007
Author(s):  
Guiping Liu ◽  
Rui Luo ◽  
Sheng Liu
Keyword(s):  
Genetic Algorithm ◽  
Iterative Algorithm ◽  
Correlation Coefficients ◽  
Optimization Method ◽  
Multi Objective Optimization ◽  
Interval Model ◽  
Numerical Examples ◽  
Multi Objective ◽  
Multi Objective Genetic Algorithm ◽  
Interval Variables

In this paper, a new interval multi-objective optimization (MOO) method integrating with the multidimensional parallelepiped (MP) interval model has been proposed to handle the uncertain problems with dependent interval variables. The MP interval model is integrated to depict the uncertain domain of the problem, where the uncertainties are described by marginal intervals and the degree of the dependencies among the interval variables is described by correlation coefficients. Then an efficient multi-objective iterative algorithm combining the micro multi-objective genetic algorithm (MOGA) with an approximate optimization method is formulated. Three numerical examples are presented to demonstrate the efficiency of the proposed approach.

scholarly journals Multi-objective robust optimization method for the modified epoxy resin sheet molding compounds of the impeller

2016 ◽  
Vol 3 (3) ◽  
pp. 179-190 ◽  
Author(s):  
Xiaozhang Qu ◽  
Guiping Liu ◽  
Shuyong Duan ◽  
Jichu Yang
Keyword(s):  
Robust Optimization ◽  
Epoxy Resin ◽  
Ventilation System ◽  
Optimization Method ◽  
Approximate Model ◽  
Analysis Model ◽  
Molding Compounds ◽  
Multi Objective ◽  
Multi Objective Genetic Algorithm ◽  
Modified Epoxy

Abstract A kind of modified epoxy resin sheet molding compounds of the impeller has been designed. Through the test, the non-metal impeller has a better environmental aging performance, but must do the waterproof processing design. In order to improve the stability of the impeller vibration design, the influence of uncertainty factors is considered, and a multi-objective robust optimization method is proposed to reduce the weight of the impeller. Firstly, based on the fluid-structure interaction, the analysis model of the impeller vibration is constructed. Secondly, the optimal approximate model of the impeller is constructed by using the Latin hypercube and radial basis function, and the fitting and optimization accuracy of the approximate model is improved by increasing the sample points. Finally, the micro multi-objective genetic algorithm is applied to the robust optimization of approximate model, and the Monte Carlo simulation and Sobol sampling techniques are used for reliability analysis. By comparing the results of the deterministic, different sigma levels and different materials, the multi-objective optimization of the SMC molding impeller can meet the requirements of engineering stability and lightweight. And the effectiveness of the proposed multi-objective robust optimization method is verified by the error analysis. After the SMC molding and the robust optimization of the impeller, the optimized rate reached 42.5%, which greatly improved the economic benefit, and greatly reduce the vibration of the ventilation system.

Multi-Objective Optimization Using Asynchronous Distributed Applications

2004 ◽  
Vol 126 (5) ◽  
pp. 767-774 ◽  
Author(s):  
Alessandro Giassi ◽  
Fouad Bennis ◽  
Jean-Jacques Maisonneuve
Keyword(s):  
Concurrent Engineering ◽  
Optimization Method ◽  
Distributed Applications ◽  
Asynchronous Communication ◽  
Collaborative Optimization ◽  
Multi Objective Optimization ◽  
Communication Tool ◽  
Multi Objective ◽  
Multi Objective Genetic Algorithm ◽  
Optimization Loop

In the context of concurrent engineering, this paper presents a quite innovative approach to the collaborative optimization process, which couples a multi-objective genetic algorithm with an asynchronous communication tool. This optimization method allows the collaborative and multi-sites design to be performed without requiring significant investments or changes in the company organization. To illustrate this methodology, the collaboration of three European companies on the optimization of a ship hull is described. The hull shape is automatically optimised distributing the elements of the optimization loop among three distant sites. Our study demonstrates that when multi-objective optimization is carried out in a distributed manner it can provide a powerful tool for concurrent product design.

Hybrid Hydraulic Vehicle Parameter Optimization using Multi-Objective Genetic Algorithm

2019 ◽  
Vol 16 (3) ◽  
pp. 7007-7018 ◽  
Author(s):  
A. F. Hawary ◽  
M. I. Ramdan
Keyword(s):  
Genetic Algorithm ◽  
Fuel Economy ◽  
Combustion Engine ◽  
Search Algorithm ◽  
Optimization Method ◽  
Hydraulic Motor ◽  
Engine Torque ◽  
Multi Objective ◽  
Multi Objective Genetic Algorithm ◽  
Conflicting Objectives

Parameter optimizations of HHV torque distribution must deal with conflicting objectives between the engine torque and fuel economy without compromising the vehicle driving quality. The torque generation from an internal combustion engine (ICE)  is directly influenced by the amount of fuel burnt, hence cannot be solved using a classical single-objective optimization method. In this paper, multi-objective genetic algorithm (MOGA) is used to optimize the power split of a parallel hybrid hydraulic vehicle (HHV) that utilizes an ICE and a hydraulic motor. The simulation runs on three operating modes, engine only, power assist and regenerative modes to optimize two conflicting objectives, engine torque and fuel economy considering both highway and city drive cycles. Using a single unified formulation, a number of design objectives can be simultaneously optimized through a systematic search algorithm within a diverse parameter space. Simulation results have shown both objectives have good compromises that lie along the Pareto optimal front. In comparison, it is observed that there is a significant improvement on fuel economy for HHV as compared to a conventional ICE especially at low-torque operation when the hydraulic motor assists the vehicle for both highway and city drive cycles.    

Optimizing of Turning Parameters Using Multi-Objective Genetic Algorithm

2010 ◽  
Vol 118-120 ◽  
pp. 359-363 ◽  
Author(s):  
Ramezan Ali Mahdavinejad
Keyword(s):  
Genetic Algorithm ◽  
Removal Rate ◽  
Target Function ◽  
Optimization Methods ◽  
Optimization Method ◽  
Surface Finishing ◽  
Multi Objective ◽  
Multi Objective Genetic Algorithm ◽  
Engineering Problems ◽  
Stock Removal

In this research, the turning parameters of steel are optimized via multi-objective genetic algorithm and multi-objective harmony research algorithm. These two algorithms are known as strong and powerful tools in optimization of engineering problems. The stock removal rate and surface roughness, as two main of output parameters are the target function and have been considered to be optimized. Since, there are two functions here; we can not use the ordinary optimization method with single-objective algorithm. In steel machining, the stock removal rate usually decreases with the surface finishing and visa versa. Therefore, it is necessary to define the weight of these parameters. In this paper the importance of each of these parameters are determined with weight sum method. In this research, the optimization methods to solve the problems via these two algorithms are discussed first. Then, the steel samples are machined and the output data are analyzed and optimized.

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