scholarly journals TỐI ƯU HÓA TIN CẬY VA ĐẬP CỦA ỐNG ĐA TẾ BÀO HÌNH VUÔNG CHỊU TẢI VA ĐẬP XIÊN

2021 ◽  
Vol 45 (03) ◽  
Author(s):  
TRẦN TRỌNG NHÂN
Keyword(s):  
Genetic Algorithm ◽  
Response Surface ◽  
Pareto Front ◽  
Nsga Ii ◽  
Knee Point

Tối ưu hóa tin cậy va đập của những ống hình vuông đa đa tế bào trong trường hợp va đập xiên được nghiên cứu trong bài báo này. Đối với các cấu trúc này, các chỉ số tin cậy va đập SEA và PCF được thu thập bằng cách sử dụng HYPERMESH / LS-DYNA. Pareto front thu được bẳng cách kết hợp response surface (RS) và Non-dominated Sorting Genetic Algorithm II (NSGA-II). Một “giải pháp tốt hơn” (hay còn gọi là knee point) được xác định từ Pareto front. Kết quả của nghiên cứu này là cơ sở tham khảo cho việc thiết kế các cấu trúc đa tế bào có khả năng tin cậy va đập tốt hơn.

Multi-Objective Geometric Optimization of Elliptical-Toothed Gerotor Pumps for Kinematics and Wear by Genetic Algorithm

2018 ◽  
Author(s):  
Andrew J. Robison ◽  
Andrea Vacca
Keyword(s):  
Genetic Algorithm ◽  
Pareto Front ◽  
Adhesive Wear ◽  
Geometric Optimization ◽  
Objective Functions ◽  
Computationally Efficient ◽  
Nsga Ii ◽  
Generation Algorithm ◽  
Flow Ripple ◽  
Gerotor Pumps

A computationally efficient gerotor gear generation algorithm has been developed that creates elliptical-toothed gerotor gear profiles, identifies conditions to guarantee a feasible geometry, evaluates several performance objectives, and is suitable to use for geometric optimization. Five objective functions are used in the optimization: minimize pump size, flow ripple, adhesive wear, subsurface fatigue (pitting), and tooth tip leakage. The gear generation algorithm is paired with the NSGA-II optimization algorithm to minimize each of the objective functions subject to the constraints to define a feasible geometry. The genetic algorithm is run with a population size of 1000 for a total of 500 generations, after which a clear Pareto front is established and displayed. A design has been selected from the Pareto front which is a good compromise between each of the design objectives and can be scaled to any desired displacement. The results of the optimization are also compared to two profile geometries found in literature. Two alternative geometries are proposed that offer much lower adhesive wear while respecting the size constraints of the published profiles and are thought to be an improvement in design.

scholarly journals Configuration and Optimization of a Minichannel Using Water–Alumina Nanofluid by Non-Dominated Sorting Genetic Algorithm and Response Surface Method

Nanomaterials ◽  
2020 ◽  
Vol 10 (5) ◽  
pp. 901 ◽  
Author(s):  
Ali Akbar Ahmadi ◽  
Masoud Arabbeiki ◽  
Hafiz Muhammad Ali ◽  
Marjan Goodarzi ◽  
Mohammad Reza Safaei
Keyword(s):  
Heat Transfer ◽  
Genetic Algorithm ◽  
Response Surface ◽  
Channel Width ◽  
Pumping Power ◽  
Nsga Ii ◽  
Square Channel ◽  
Cylinder Diameter ◽  
Input Variables ◽  
Volume Method

Nanofluids in minichannels with various configurations are applied as cooling and heating fluids. Therefore, it is essential to have an optimal design of minichannels. For this purpose, a square channel with a cylinder in the center connected to wavy fins at various concentrations of an Al2O3 nanofluid is simulated using the finite volume method (FVM). Moreover, central composite design (CCD) is used as a method of design of experiment (DOE) to study the effects of three input variables, namely the cylinder diameter, channel width, and fin radius on the convective heat transfer and pumping power. The impacts of the linear term, together with those of the square and interactive on the response variables are determined using Pareto and main effects plots by an ANOVA. The non-dominated sorting genetic algorithm-II (NSGA-II), along with the response surface methodology (RSM) is applied to achieve the optimal configuration and nanofluid concentration. The results indicate that the effect of the channel width and cylinder diameter enhances about 21% and 18% by increasing the concentration from 0% to 5%. On the other hand, the pumping power response is not sensitive to the nanofluid concentration. Besides, the channel width has the highest and lowest effect on the heat transfer coefficient (HTC) and pumping power, respectively. The optimization for a concentration of 3% indicates that in Re = 500 when the geometry is optimized, the HTC enhances by almost 9%, while the pumping power increases by about 18%. In contrast, by increasing the concentration from 1% to 3%, merely an 8% enhancement in HTC is obtained, while the pumping power intensifies around 60%.

scholarly journals Pareto Optimization of a Half Car Passive Suspension Model Using a Novel Multiobjective Heat Transfer Search Algorithm

2017 ◽  
Vol 2017 ◽  
pp. 1-17 ◽  
Author(s):  
Vimal Savsani ◽  
Vivek Patel ◽  
Bhargav Gadhvi ◽  
Mohamed Tawhid
Keyword(s):  
Heat Transfer ◽  
Genetic Algorithm ◽  
Multiobjective Optimization ◽  
Pareto Front ◽  
Search Algorithm ◽  
Extreme Points ◽  
Search Technique ◽  
Nsga Ii ◽  
Linear Ordinary Differential Equations ◽  
Nondominated Sorting

Most of the modern multiobjective optimization algorithms are based on the search technique of genetic algorithms; however the search techniques of other recently developed metaheuristics are emerging topics among researchers. This paper proposes a novel multiobjective optimization algorithm named multiobjective heat transfer search (MOHTS) algorithm, which is based on the search technique of heat transfer search (HTS) algorithm. MOHTS employs the elitist nondominated sorting and crowding distance approach of an elitist based nondominated sorting genetic algorithm-II (NSGA-II) for obtaining different nondomination levels and to preserve the diversity among the optimal set of solutions, respectively. The capability in yielding a Pareto front as close as possible to the true Pareto front of MOHTS has been tested on the multiobjective optimization problem of the vehicle suspension design, which has a set of five second-order linear ordinary differential equations. Half car passive ride model with two different sets of five objectives is employed for optimizing the suspension parameters using MOHTS and NSGA-II. The optimization studies demonstrate that MOHTS achieves the better nondominated Pareto front with the widespread (diveresed) set of optimal solutions as compared to NSGA-II, and further the comparison of the extreme points of the obtained Pareto front reveals the dominance of MOHTS over NSGA-II, multiobjective uniform diversity genetic algorithm (MUGA), and combined PSO-GA based MOEA.

scholarly journals Coordinated Control of PV Generation and EVs Charging Based on Improved DECell Algorithm

2015 ◽  
Vol 2015 ◽  
pp. 1-13 ◽  
Author(s):  
Guo Zhao ◽  
Xueliang Huang ◽  
Hao Qiang
Keyword(s):  
Genetic Algorithm ◽  
Differential Evolution ◽  
Control Strategy ◽  
Optimization Model ◽  
Pareto Front ◽  
Optimal Solution ◽  
Coordinated Control ◽  
Nsga Ii ◽  
Nondominated Solutions ◽  
Pv Generation

Recently, the coordination of EVs’ charging and renewable energy has become a hot research all around the globe. Considering the requirements of EV owner and the influence of the PV output fluctuation on the power grid, a three-objective optimization model was established by controlling the EVs charging power during charging process. By integrating the meshing method into differential evolution cellular (DECell) genetic algorithm, an improved differential evolution cellular (IDECell) genetic algorithm was presented to solve the multiobjective optimization model. Compared to the NSGA-II and DECell, the IDECell algorithm showed better performance in the convergence and uniform distribution. Furthermore, the IDECell algorithm was applied to obtain the Pareto front of nondominated solutions. Followed by the normalized sorting of the nondominated solutions, the optimal solution was chosen to arrive at the optimized coordinated control strategy of PV generation and EVs charging. Compared to typical charging pattern, the optimized charging pattern could reduce the fluctuations of PV generation output power, satisfy the demand of EVs charging quantity, and save the total charging cost.

Multi-Objective Optimization of the Impingement-Film Cooling Structure of a HPT Endwall Using Conjugate Heat Transfer CFD

2016 ◽  
Author(s):  
Zhongran Chi ◽  
Haiqing Liu ◽  
Shusheng Zang
Keyword(s):  
Heat Transfer ◽  
Genetic Algorithm ◽  
Wall Temperature ◽  
Conjugate Heat Transfer ◽  
Pareto Front ◽  
Multi Objective Optimization ◽  
Nsga Ii ◽  
Multi Objective ◽  
Lift Off ◽  
Optimization Search

This paper discusses the approach of cooling design optimization of a HPT endwall with 3D Conjugate Heat Transfer (CHT) CFD applied. This study involved the optimization of the spacing of impingement jet array and the exit width of shaped holes, which were different for each cooling cavity. The optimization objectives were to reduce the wall temperature level and also to increase the aerodynamic performance of the gas turbine. The optimization methodology consisted of an in-house parametric design & CFD mesh generation tool, a CHT CFD solver, a database of wall temperature distributions, a metamodel, and a genetic algorithm (GA) for evolutionary multi-objective optimization. The CFD tool was validated against experimental data of an endwall at CHT conditions. The metamodel, which could efficiently predict the aerodynamic loss and the wall temperature distribution of a new individual based on the database, was developed and coupled with Non-dominated Sorting Genetic Algorithm II (NSGA-II) to accelerate the optimization process. Through optimization search, the Pareto front of the problem was found costing only tens of CFD runs. By comparing with additional CFD results, it was demonstrated that the design variables in the Pareto front successfully reached the optimal values. The optimal spacing of each impingement array was decided accommodating the local thermal load while avoiding jet lift-off of film coolant. It was also suggested that using cylindrical film holes near throat could benefit both aerodynamics and cooling.

scholarly journals Optimization of Wind Turbine Airfoil Using Nondominated Sorting Genetic Algorithm and Pareto Optimal Front

2012 ◽  
Vol 2012 ◽  
pp. 1-9 ◽  
Author(s):  
Ziaul Huque ◽  
Ghizlane Zemmouri ◽  
Donald Harby ◽  
Raghava Kommalapati
Keyword(s):  
Genetic Algorithm ◽  
Response Surface ◽  
Cfd Simulation ◽  
Lift Coefficient ◽  
Response Surfaces ◽  
Least Square ◽  
Pareto Optimal ◽  
Nsga Ii ◽  
Pareto Solution ◽  
Pareto Optimal Front

A Computational Fluid Dynamics (CFD) and response surface-based multiobjective design optimization were performed for six different 2D airfoil profiles, and the Pareto optimal front of each airfoil is presented. FLUENT, which is a commercial CFD simulation code, was used to determine the relevant aerodynamic loads. The Lift Coefficient (CL) and Drag Coefficient (CD) data at a range of 0°to 12°angles of attack (α) and at three different Reynolds numbers (Re=68,459, 479, 210, and 958, 422) for all the six airfoils were obtained. Realizablek-εturbulence model with a second-order upwind solution method was used in the simulations. The standard least square method was used to generate response surface by the statistical code JMP. Elitist Non-dominated Sorting Genetic Algorithm (NSGA-II) was used to determine the Pareto optimal set based on the response surfaces. Each Pareto optimal solution represents a different compromise between design objectives. This gives the designer a choice to select a design compromise that best suits the requirements from a set of optimal solutions. The Pareto solution set is presented in the form of a Pareto optimal front.

Improved Memetic NSGA-II Using a Deep Neighborhood Search

2021 ◽  
Vol 12 (4) ◽  
pp. 138-154
Author(s):  
Samir Mahdi ◽  
Brahim Nini
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Pareto Front ◽  
Neighborhood Search ◽  
Pareto Optimal ◽  
Multi Objective Optimization ◽  
Nsga Ii ◽  
Pareto Optimal Front ◽  
Slow Convergence ◽  
Multi Objective

Elitist non-sorted genetic algorithms as part of Pareto-based multi-objective evolutionary algorithms seems to be one of the most efficient algorithms for multi-objective optimization. However, it has some shortcomings, such as low convergence accuracy, uneven Pareto front distribution, and slow convergence. A number of review papers using memetic technique to improve NSGA-II have been published. Hence, it is imperative to improve memetic NSGA-II by increasing its solving accuracy. In this paper, an improved memetic NSGA-II, called deep memetic non-sorted genetic algorithm (DM-NSGA-II), is proposed, aiming to obtain more non-dominated solutions uniformly distributed and better converged near the true Pareto-optimal front. The proposed algorithm combines the advantages of both exact and heuristic approaches. The effectiveness of DM-NSGA-II is validated using well-known instances taken from the standard literature on multi-objective knapsack problem. As will be shown, the performance of the proposed algorithm is demonstrated by comparing it with M-NSGA-II using hypervolume metric.

scholarly journals Multi-objective Optimization to Increase Nusselt Number and Reduce Friction Coefficient of Water/Carbon Nanotubes via NSGA II using Response Surface Methodology

2020 ◽  
Vol 3 (1) ◽  
pp. 1-14
Author(s):  
Amin Moslemi Petrudi ◽  
Pourya Fathi ◽  
Masoud Rahmani
Keyword(s):  
Response Surface Methodology ◽  
Nusselt Number ◽  
Friction Coefficient ◽  
Response Surface ◽  
Pareto Front ◽  
Volume Fraction ◽  
Reynolds Numbers ◽  
Solid Particles ◽  
Nsga Ii ◽  
Engineering Sciences

Heat transfer science is one of the most important and most applied engineering sciences, with the importance of energy management and energy conservation being doubled. Because of their properties, nanofluids have been widely used in various industries, making them particularly important to study. In this paper, the Nusselt number and coefficient of friction with volume fraction ranging from 0 to 0.1 at approximately Reynolds numbers of 200 to 5000 are studied experimentally. Higher thermal conductivity, better stability, lower pressure drop was observed using nanoparticles of solid particles. NSGA II algorithm was used to maximize Nusselt number and minimum friction coefficient by changing temperature and volume fraction of nanoparticles. To obtain Nusselt number and friction coefficient based on the temperature and volume fraction of the nanoparticles, the experimental data response surface methodology was used and with increasing Reynolds number, the Nusselt number increased and the friction coefficient decreased. In order to evaluate the objective functions in the optimization, the response surface methodology is attached to the optimization algorithm. At the end, the Pareto Front and its corresponding optimal points are presented.

scholarly journals Study on Parameter Optimization Design of Drum Brake Based on Hybrid Cellular Multiobjective Genetic Algorithm

2012 ◽  
Vol 2012 ◽  
pp. 1-23 ◽  
Author(s):  
Yi Zhang ◽  
Hu Zhang ◽  
Chao Lu
Keyword(s):  
Genetic Algorithm ◽  
Differential Evolution ◽  
Parameter Optimization ◽  
Optimization Design ◽  
Pareto Front ◽  
Nsga Ii ◽  
Multiobjective Genetic Algorithm ◽  
Drum Brake ◽  
Multiobjective Problems ◽  
Parameter Optimization Design

In consideration of the significant role the brake plays in ensuring the fast and safe running of vehicles, and since the present parameter optimization design models of brake are far from the practical application, this paper proposes a multiobjective optimization model of drum brake, aiming at maximizing the braking efficiency and minimizing the volume and temperature rise of drum brake. As the commonly used optimization algorithms are of some deficiency, we present a differential evolution cellular multiobjective genetic algorithm (DECell) by introducing differential evolution strategy into the canonical cellular genetic algorithm for tackling this problem. For DECell, the gained Pareto front could be as close as possible to the exact Pareto front, and also the diversity of nondominated individuals could be better maintained. The experiments on the test functions reveal that DECell is of good performance in solving high-dimension nonlinear multiobjective problems. And the results of optimizing the new brake model indicate that DECell obviously outperforms the compared popular algorithm NSGA-II concerning the number of obtained brake design parameter sets, the speed, and stability for finding them.

scholarly journals Using Different Approaches to Approximate a Pareto Front for a Multiobjective Evolutionary Algorithm: Optimal Thinning Regimes forEucalyptus fastigata

2012 ◽  
Vol 2012 ◽  
pp. 1-27 ◽  
Author(s):  
Oliver Chikumbo
Keyword(s):  
Genetic Algorithm ◽  
Evolutionary Algorithm ◽  
Pareto Front ◽  
Nsga Ii ◽  
Multiobjective Evolutionary Algorithm ◽  
Fitness Sharing ◽  
Pareto Ranking ◽  
Ranking Scheme ◽  
Volume Production ◽  
Nondominated Sorting

A stand-level, multiobjective evolutionary algorithm (MOEA) for determining a set of efficient thinning regimes satisfying two objectives, that is, value production for sawlog harvesting and volume production for a pulpwood market, was successfully demonstrated for aEucalyptus fastigatatrial in Kaingaroa Forest, New Zealand. The MOEA approximated the set of efficient thinning regimes (with a discontinuous Pareto front) by employing a ranking scheme developed by Fonseca and Fleming (1993), which was a Pareto-based ranking (a.k.a Multiobjective Genetic Algorithm—MOGA). In this paper we solve the same problem using an improved version of a fitness sharing Pareto ranking algorithm (a.k.a Nondominated Sorting Genetic Algorithm—NSGA II) originally developed by Srinivas and Deb (1994) and examine the results. Our findings indicate that NSGA II approximates the entire Pareto front whereas MOGA only determines a subdomain of the Pareto points.

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