Finite Element Design and Multi-objective Optimization of Four Pole Reluctance Motor Based on NSGA-II Intelligent Algorithm

2021 ◽  
Author(s):  
Emmanuel C. Abunike ◽  
Ogbonnaya I. Okoro ◽  
Innocent E. Davidson
Keyword(s):  
Finite Element ◽  
Multi Objective Optimization ◽  
Intelligent Algorithm ◽  
Nsga Ii ◽  
Multi Objective ◽  
Reluctance Motor

scholarly journals Finite Element Based Overall Optimization of Switched Reluctance Motor Using Multi-Objective Genetic Algorithm (NSGA-II)

Mathematics ◽  
2021 ◽  
Vol 9 (5) ◽  
pp. 576
Author(s):  
Mohamed El-Nemr ◽  
Mohamed Afifi ◽  
Hegazy Rezk ◽  
Mohamed Ibrahim
Keyword(s):  
Genetic Algorithm ◽  
Finite Element ◽  
Optimal Design ◽  
Switched Reluctance Motor ◽  
Design Parameters ◽  
Objective Functions ◽  
Nsga Ii ◽  
Switched Reluctance ◽  
Multi Objective ◽  
Reluctance Motor

The design of switched reluctance motor (SRM) is considered a complex problem to be solved using conventional design techniques. This is due to the large number of design parameters that should be considered during the design process. Therefore, optimization techniques are necessary to obtain an optimal design of SRM. This paper presents an optimal design methodology for SRM using the non-dominated sorting genetic algorithm (NSGA-II) optimization technique. Several dimensions of SRM are considered in the proposed design procedure including stator diameter, bore diameter, axial length, pole arcs and pole lengths, back iron length, shaft diameter as well as the air gap length. The multi-objective design scheme includes three objective functions to be achieved, that is, maximum average torque, maximum efficiency and minimum iron weight of the machine. Meanwhile, finite element analysis (FEA) is used during the optimization process to calculate the values of the objective functions. In this paper, two designs for SRMs with 8/6 and 6/4 configurations are presented. Simulation results show that the obtained SRM design parameters allow better average torque and efficiency with lower iron weight. Eventually, the integration of NSGA-II and FEA provides an effective approach to obtain the optimal design of SRM.

scholarly journals Multi-Objective Lightweight Optimization of Parameterized Suspension Components Based on NSGA-II Algorithm Coupling with Surrogate Model

Machines ◽  
2021 ◽  
Vol 9 (6) ◽  
pp. 107
Author(s):  
Rongchao Jiang ◽  
Zhenchao Jin ◽  
Dawei Liu ◽  
Dengfeng Wang
Keyword(s):  
Ride Comfort ◽  
Lightweight Design ◽  
Dynamic Performance ◽  
Vehicle Dynamic ◽  
Multi Objective Optimization ◽  
Original Design ◽  
Dynamic Simulations ◽  
Nsga Ii ◽  
Torsion Beam ◽  
Multi Objective

In order to reduce the negative effect of lightweighting of suspension components on vehicle dynamic performance, the control arm and torsion beam widely used in front and rear suspensions were taken as research objects for studying the lightweight design method of suspension components. Mesh morphing technology was employed to define design variables. Meanwhile, the rigid–flexible coupling vehicle model with flexible control arm and torsion beam was built for vehicle dynamic simulations. The total weight of control arm and torsion beam was taken as optimization objective, as well as ride comfort and handling stability performance indexes. In addition, the fatigue life, stiffness, and modal frequency of control arm and torsion beam were taken as the constraints. Then, Kriging model and NSGA-II were adopted to perform the multi-objective optimization of control arm and torsion beam for determining the lightweight scheme. By comparing the optimized and original design, it indicates that the weight of the optimized control arm and torsion beam are reduced 0.505 kg and 1.189 kg, respectively, while structural performance and vehicle performance satisfy the design requirement. The proposed multi-objective optimization method achieves a remarkable mass reduction, and proves to be feasible and effective for lightweight design of suspension components.

scholarly journals Design and characterization of effective solar cells

2021 ◽  
Author(s):  
Varun Ojha ◽  
Giorgio Jansen ◽  
Andrea Patanè ◽  
Antonino La Magna ◽  
Vittorio Romano ◽  
...  
Keyword(s):  
Solar Cells ◽  
Quantum Efficiency ◽  
Cost Benefit Analysis ◽  
Cost Minimization ◽  
Cell Structure ◽  
Cost Benefit ◽  
Structure Design ◽  
Multi Objective Optimization ◽  
Nsga Ii ◽  
Multi Objective

AbstractWe propose a two-stage multi-objective optimization framework for full scheme solar cell structure design and characterization, cost minimization and quantum efficiency maximization. We evaluated structures of 15 different cell designs simulated by varying material types and photodiode doping strategies. At first, non-dominated sorting genetic algorithm II (NSGA-II) produced Pareto-optimal-solutions sets for respective cell designs. Then, on investigating quantum efficiencies of all cell designs produced by NSGA-II, we applied a new multi-objective optimization algorithm II (OptIA-II) to discover the Pareto fronts of select (three) best cell designs. Our designed OptIA-II algorithm improved the quantum efficiencies of all select cell designs and reduced their fabrication costs. We observed that the cell design comprising an optimally doped zinc-oxide-based transparent conductive oxide (TCO) layer and rough silver back reflector (BR) offered a quantum efficiency ($$Q_e$$ Q e ) of 0.6031. Overall, this paper provides a full characterization of cell structure designs. It derives relationship between quantum efficiency, $$Q_e$$ Q e of a cell with its TCO layer’s doping methods and TCO and BR layer’s material types. Our solar cells design characterization enables us to perform a cost-benefit analysis of solar cells usage in real-world applications.

Multi-objective optimization of squirrel cage fan for range hood based on Kriging model

2021 ◽  
pp. 095440622199586
Author(s):  
Qianhao Xiao ◽  
Jun Wang ◽  
Boyan Jiang ◽  
Weigang Yang ◽  
Xiaopei Yang
Keyword(s):  
Flow Rate ◽  
Optimization Design ◽  
Volume Flow Rate ◽  
Kriging Model ◽  
Volume Flow ◽  
Design Parameters ◽  
Multi Objective Optimization ◽  
Nsga Ii ◽  
Multi Objective ◽  
Squirrel Cage

In view of the multi-objective optimization design of the squirrel cage fan for the range hood, a blade parameterization method based on the quadratic non-uniform B-spline (NUBS) determined by four control points was proposed to control the outlet angle, chord length and maximum camber of the blade. Morris-Mitchell criteria were used to obtain the optimal Latin hypercube sample based on the evolutionary operation, and different subsets of sample numbers were created to study the influence of sample numbers on the multi-objective optimization results. The Kriging model, which can accurately reflect the response relationship between design variables and optimization objectives, was established. The second-generation Non-dominated Sorting Genetic algorithm (NSGA-II) was used to optimize the volume flow rate at the best efficiency point (BEP) and the maximum volume flow rate point (MVP). The results show that the design parameters corresponding to the optimization results under different sample numbers are not the same, and the fluctuation range of the optimal design parameters is related to the influence of the design parameters on the optimization objectives. Compared with the prototype, the optimized impeller increases the radial velocity of the impeller outlet, reduces the flow loss in the volute, and increases the diffusion capacity, which improves the volume flow rate, and efficiency of the range hood system under multiple working conditions.

Multi-objective optimization of weld geometry in hybrid fiber laser-arc butt welding using Kriging model and NSGA-II

2016 ◽  
Vol 122 (6) ◽  
Author(s):  
Zhongmei Gao ◽  
Xinyu Shao ◽  
Ping Jiang ◽  
Chunming Wang ◽  
Qi Zhou ◽  
...  
Keyword(s):  
Fiber Laser ◽  
Kriging Model ◽  
Multi Objective Optimization ◽  
Hybrid Fiber ◽  
Nsga Ii ◽  
Butt Welding ◽  
Multi Objective ◽  
Weld Geometry

scholarly journals Multi-Objective Optimization of Autonomous Microgrids with Reliability Consideration

Energies ◽  
2021 ◽  
Vol 14 (15) ◽  
pp. 4466
Author(s):  
Maël Riou ◽  
Florian Dupriez-Robin ◽  
Dominique Grondin ◽  
Christophe Le Loup ◽  
Michel Benne ◽  
...  
Keyword(s):  
Power Systems ◽  
Rural Areas ◽  
Energy System ◽  
Design Stage ◽  
Multi Objective Optimization ◽  
Renewable Energy Resources ◽  
Nsga Ii ◽  
Renewable Integration ◽  
Multi Objective ◽  
Trade Offs

Microgrids operating on renewable energy resources have potential for powering rural areas located far from existing grid infrastructures. These small power systems typically host a hybrid energy system of diverse architecture and size. An effective integration of renewable energies resources requires careful design. Sizing methodologies often lack the consideration for reliability and this aspect is limited to power adequacy. There exists an inherent trade-off between renewable integration, cost, and reliability. To bridge this gap, a sizing methodology has been developed to perform multi-objective optimization, considering the three design objectives mentioned above. This method is based on the non-dominated sorting genetic algorithm (NSGA-II) that returns the set of optimal solutions under all objectives. This method aims to identify the trade-offs between renewable integration, reliability, and cost allowing to choose the adequate architecture and sizing accordingly. As a case study, we consider an autonomous microgrid, currently being installed in a rural area in Mali. The results show that increasing system reliability can be done at the least cost if carried out in the initial design stage.

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