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.

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.

Multi-Objective Design Optimization of Cooling Turbine Blade Based on Kriging Model

2012 ◽  
Vol 184-185 ◽  
pp. 316-319
Author(s):  
Liang Bo Ao ◽  
Lei Li ◽  
Yuan Sheng Li ◽  
Zhi Xun Wen ◽  
Zhu Feng Yue
Keyword(s):  
Design Optimization ◽  
Turbine Blade ◽  
Optimization Model ◽  
Sampling Technique ◽  
Latin Hypercube Sampling ◽  
Kriging Model ◽  
Optimization Method ◽  
Approximate Model ◽  
Multi Objective ◽  
Pin Fin

The multi-objective design optimization of cooling turbine blade is studied using Kriging model. The optimization model is created, with the diameter of pin fin at the trailing edge of cooling turbine blade and the location, width, height of rib as design variable, the blade body temperature, flow resistance loss and aerodynamic efficiency as optimization object. The sample points are selected using Latin hypercube sampling technique, and the approximate model is created using Kriging method, the set of Pareto-optimal solutions of optimization objects is obtained by the multi-object optimization model using elitist non-dominated sorting genetic algorithm (NSGA-Ⅱ) based on the approximate model. The result shows that the conflict among all optimization objects is solved effectively and the feasibility of the optimization method is improved.

Robust Optimization of an Automotive Valvetrain Using a Multiobjective Genetic Algorithm

2003 ◽  
Author(s):  
Emre Kazancioglu ◽  
Guangquan Wu ◽  
Jeonghan Ko ◽  
Stanislav Bohac ◽  
Zoran Filipi ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Robust Optimization ◽  
Production Cost ◽  
Pareto Front ◽  
Total Production ◽  
Multi Objective ◽  
Engine Model ◽  
Multi Objective Genetic Algorithm ◽  
Current Design ◽  
Alternative Designs

A robust optimization of an automobile valvetrain is presented where the variation of engine performances due to the component dimensional variations is minimized subject to the constraints on mean engine performances. The dimensional variations of valvetrain components are statistically characterized based on the measurements of the actual components. Monte Carlo simulation is used on a neural network model built from an integrated high fidelity valvetrain-engine model, to obtain the mean and standard deviation of horsepower, torque and fuel consumption. Assuming the component production cost is inversely proportional to the coefficient of variation of its dimensions, a multi-objective optimization problem minimizing the variation in engine performances and the total production cost of components is solved by a multi-objective genetic algorithm (MOGA). The comparisons using the newly developed Pareto front quality index (PFQI) indicate that MOGA generates the Pareto fronts of substantially higher quality, than SQP with varying weights on the objectives. The current design of the valvetrain is compared with two alternative designs on the obtained Pareto front, which suggested potential improvements.

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 Modified Epoxy Resin Synthesis from Phosphorus—Containing Polyol and Physical Changes Studies in the Synthesized Products

Polymers ◽  
2019 ◽  
Vol 11 (12) ◽  
pp. 2116 ◽  
Author(s):  
Jeong Beom Jang ◽  
Tae Hee Kim ◽  
Taeyoon Kim ◽  
Hye Jin Kim ◽  
Bongkuk Seo ◽  
...  
Keyword(s):  
Mechanical Properties ◽  
Epoxy Resin ◽  
Epoxy Resins ◽  
Mechanical And Thermal Properties ◽  
Release Rates ◽  
Molding Compounds ◽  
Reinforced Plastics ◽  
Phosphorus Containing ◽  
Physical Changes ◽  
Modified Epoxy

Epoxy resins are commonly used to manufacture the molding compounds, reinforced plastics, coatings, or adhesives required in various industries. However, the demand for new epoxy resins has increased to satisfy diverse industrial requirements such as enhanced mechanical properties, thermal stability, or electrical properties. Therefore, in this study, we synthesized new epoxy resin (PPME) by modifying phosphorous-containing polyol. The prepared resin was analyzed and added to epoxy compositions in various quantities. The compositions were cured at high temperatures to obtain plastics to further test the mechanical and thermal properties of the epoxy resin. The measured tensile and flexural strength of epoxy compositions were similar to the composition without synthesized epoxy resin. However, the heat release rates of the compositions exhibited tendencies of a decrease proportional to the amount of PPME.

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