scholarly journals Multi-Objective Optimization Design of Standard Industrial Motor

2020 ◽  
Vol 30.8 (147) ◽  
pp. 27-33
Author(s):  
Tuan V Tran ◽  
◽  
Chi D Dang
Keyword(s):  
Optimization Design ◽  
Design Method ◽  
Electrical Machines ◽  
Multi Objective Optimization ◽  
Pareto Solution ◽  
Active Mass ◽  
Traditional Design ◽  
Conflicting Objectives ◽  
Optimization Methodology

This paper presents a method to optimally design electrical machines. Unlike the traditional design method “tries-and-errors iterative process”, the optimal design approach consists of combining optimization algorithms and multi-physics models to reach the optimum design. A case study of designing a standard industrial motor of 6 HP with multi-objectives and constraints is chosen in order to test this optimization methodology. The Pareto solution results of two conflicting objectives between the efficiency and the active mass of this machine are reached to help designers and customers selecting the best compromised design of motor of 6 HP in terms of cost and consuming energy.

scholarly journals Research on Blank Optimization Design Based on Low-Carbon and Low-Cost Blank Process Route Optimization Model

2021 ◽  
Vol 13 (4) ◽  
pp. 1929
Author(s):  
Yongmao Xiao ◽  
Wei Yan ◽  
Ruping Wang ◽  
Zhigang Jiang ◽  
Ying Liu
Keyword(s):  
Optimization Model ◽  
Optimization Design ◽  
Design Method ◽  
Low Cost ◽  
Route Optimization ◽  
Low Carbon ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Blank Design ◽  
Process Route

The optimization of blank design is the key to the implementation of a green innovation strategy. The process of blank design determines more than 80% of resource consumption and environmental emissions during the blank processing. Unfortunately, the traditional blank design method based on function and quality is not suitable for today’s sustainable development concept. In order to solve this problem, a research method of blank design optimization based on a low-carbon and low-cost process route optimization is proposed. Aiming at the processing characteristics of complex box type blank parts, the concept of the workstep element is proposed to represent the characteristics of machining parts, a low-carbon and low-cost multi-objective optimization model is established, and relevant constraints are set up. In addition, an intelligent generation algorithm of a working step chain is proposed, and combined with a particle swarm optimization algorithm to solve the optimization model. Finally, the feasibility and practicability of the method are verified by taking the processing of the blank of an emulsion box as an example. The data comparison shows that the comprehensive performance of the low-carbon and low-cost multi-objective optimization is the best, which meets the requirements of low-carbon processing, low-cost, and sustainable production.

scholarly journals Finocyl Grain Design Using the Genetic Algorithm in Combination with Adaptive Basis Function Construction

2019 ◽  
Vol 2019 ◽  
pp. 1-12
Author(s):  
Saeed Mesgari ◽  
Mehrdad Bazazzadeh ◽  
Alireza Mostofizadeh
Keyword(s):  
Genetic Algorithm ◽  
Level Set Method ◽  
Level Set ◽  
Computer Aided Design ◽  
Optimization Design ◽  
Design Method ◽  
Burning Surface ◽  
Aided Design ◽  
Function Construction

This study deals with the application of optimization in Finocyl grain design with ballistic objective functions using a genetic algorithm. The classical sampling method is used for space filling; a level-set method is used for simulating the evaluation of a burning surface of the propellant grain. An algorithm is developed beside the level-set code that prepares the initial grain configuration using a computer-aided design (CAD) to export generated models to the level-set code. The lumped method is used to perform internal ballistic analysis. A meta-model is used to surrogate the level-set method in an optimization design loop. Finally, a case study is done to verify the proposed algorithm. Observed results show that the grain design method reduced the design time significantly, and this algorithm can be used in designing any grain type.

Multi-Objective Optimal Design of a Double Circular Gear Based on the Differential Evolution Algorithm

2014 ◽  
Vol 977 ◽  
pp. 365-369
Author(s):  
Li Mei Zou ◽  
Bo Guo ◽  
Xue Yi Qian
Keyword(s):  
Differential Evolution ◽  
Optimization Design ◽  
Design Method ◽  
Optimization Technique ◽  
Differential Evolution Algorithm ◽  
Research Process ◽  
Multi Objective Optimization ◽  
Design Quality ◽  
Multi Objective ◽  
Technical And Economic Indicators

In order to improve the comprehensive technical and economic indicators of a double circular gear, based on the conjugate principle and design method of the double circular gear, by use of the modified differential evolution multi-objective optimization technique and MATLAB computer simulation technology, constrained multi-objective optimization design of a double circular gear was done. According to the research process and results, by use of the improved differential evolutionary multi-objective optimization technique, the design cycle of product can be shorten effectively, the design quality of product can be improved.

Design Method for Lattice-Skin Structure Fabricated by Additive Manufacturing

2014 ◽  
Author(s):  
Yunlong Tang ◽  
Yaoyao Fiona Zhao
Keyword(s):  
Complex Geometry ◽  
Lattice Structure ◽  
Design Method ◽  
Mapping Function ◽  
Second Step ◽  
Parameter Distribution ◽  
Skin Structure ◽  
Traditional Design ◽  
Novel Design

Parts with complex geometry structure can be produced by AM without significant increase of fabrication time and cost. One application of AM technology is to fabricate customized lattice-skin structure which can enhance performance of products with less material and less weight. However, most of traditional design methods only focus on design at macro-level with solid structure. Thus, a design method which can generate customized lattice-skin structure for performance improvement and functionality integration is urgently needed. In this paper, a novel design method for lattice-skin structure is proposed. In this design method, FSs and FVs are firstly generated according to FRs. Then, initial design space is created by filling FVs and FSs with selected lattice topology and skin, respectively. In parallel to the second step, initial parameters of lattice-skin structure are calculated based on FRs. Finally, TO method is used to optimize parameter distribution of lattice structure with the help of mapping function between TO’s result and lattice parameters. The design method proposed in this paper is proven to be efficient with case study and provides an important foundation for wide adoption of AM technologies in industry.

The Simulation and Optimization Design of Adams-Based Engine Suspension System

2014 ◽  
Vol 686 ◽  
pp. 529-534
Author(s):  
Jian Xin Xie ◽  
Xiao Le Wang ◽  
Chao Liu
Keyword(s):  
Initial Data ◽  
Optimization Design ◽  
Vibration Isolation ◽  
Design Method ◽  
Suspension System ◽  
Multi Objective Optimization ◽  
Intrinsic Frequency ◽  
Simulation And Optimization ◽  
Engine Vibration ◽  
Simulation Results

In this study, the engine suspension system was optimized for making the vibration between engine and car body minimized, and also the optimization was simulated using software Adams. The purpose of this study was to research the vibration isolation of the engine mounting system and implement multi-objective optimization for the intrinsic frequency. In this paper, the optimization was implemented in two ways: (1) the intrinsic frequency was optimized by reasonably allocating it: (2) the intrinsic frequency was optimized using energy decoupling. The optimized intrinsic frequencies were simulated using software Adams and then the simulation results were compared. The simulation results showed that the optimized energy distribution was almost up to 90% and the decoupling degree was greatly improved by comparing the initial data, proving the optimized data played a greater effect on engine vibration isolation and further verifying the feasibility of optimization design method.

Multi-Objective Optimization Design Method for Tandem Compressor Cascade at Design and Off Design Conditions

2010 ◽  
Author(s):  
Yaping Ju ◽  
Chuhua Zhang
Keyword(s):  
Optimization Design ◽  
Design Method ◽  
Pressure Ratio ◽  
Aerodynamic Performance ◽  
Multi Objective Optimization ◽  
Ann Model ◽  
Compressor Cascade ◽  
Control Cost ◽  
Multi Objective ◽  
Operation Conditions

Recently, there has been a renewed interest in the research of tandem compressor cascades due to the high stage pressure ratio and low control cost. Firstly, the computational fluid dynamics (CFD) method is employed to examine the particular aerodynamic performance of the tandem cascade. Then we propose an automatic multi-objective optimization design method of the tandem cascade for the superior aerodynamic performance under the multiple operation conditions. Particular efforts have been devoted to the gap geometry optimization in terms of the front and aft airfoil relative position, camber turning ratio as well as chord ratio. The multi-objective optimization algorithm comprises a refined multi-objective genetic algorithm (MOGA) and a developed artificial neural network (ANN) model which is used to fast approximate the aerodynamic performance of the tandem cascade. The results show that the tandem cascade outperforms the single cascade in terms of producing higher pressure ratio and lower losses while the operation range is rather narrow. The optimized all-better-than (ABT) tandem cascade has its design point performance significantly improved while the operation range slightly widened. We also find that a slight axial proximity and separation of the tandem airfoils are beneficial to widening the positive and negative operation range, respectively. This research is useful to the tandem compressor cascade design in minimizing the stage number of the engine compressors.

Multi-Objective Optimization in Multi-Attribute and Multi-Unit Combinatorial Reverse Auctions

2017 ◽  
Vol 26 (05) ◽  
pp. 1760016 ◽  
Author(s):  
Shubhashis Kumar Shil ◽  
Samira Sadaoui
Keyword(s):  
Quality Measures ◽  
Optimization Approach ◽  
Multi Objective Optimization ◽  
Solution Quality ◽  
Winner Determination ◽  
Multi Objective ◽  
Conflicting Objectives ◽  
Trading Constraints ◽  
Significant Superiority

This study introduces an advanced Combinatorial Reverse Auction (CRA), multi-units, multiattributes and multi-objective, which is subject to buyer and seller trading constraints. Conflicting objectives may occur since the buyer can maximize some attributes and minimize some others. To address the Winner Determination (WD) problem for this type of CRAs, we propose an optimization approach based on genetic algorithms that we integrate with our variants of diversity and elitism strategies to improve the solution quality. Moreover, by maximizing the buyer’s revenue, our approach is able to return the best solution for our complex WD problem. We conduct a case study as well as simulated testing to illustrate the importance of the diversity and elitism schemes. We also validate the proposed WD method through simulated experiments by generating large instances of our CRA problem. The experimental results demonstrate on one hand the performance of our WD method in terms of several quality measures, like solution quality, run-time complexity and trade-off between convergence and diversity, and on the other hand, it’s significant superiority to well-known heuristic and exact WD techniques that have been implemented for much simpler CRAs.

Multi-point and multi-objective optimization design method for industrial axial compressor cascades

2011 ◽  
Vol 225 (6) ◽  
pp. 1481-1493 ◽  
Author(s):  
Y P Ju ◽  
C H Zhang
Keyword(s):  
Optimization Design ◽  
Design Method ◽  
Axial Compressor ◽  
Multi Objective Optimization ◽  
Original Design ◽  
Design Point ◽  
Operating Range ◽  
Multi Objective ◽  
Pressure Loss Coefficient ◽  
Total Pressure Loss Coefficient

Modern aerodynamic optimization design methods for the industrial axial compressor cascade mainly aim at improving both design point and off-design point performance. In this study, a multi-point and multi-objective optimization design method is established for the cascade, particularly aiming at widening the operating range while maintaining good performance at the acceptable expense of computational load. The design objectives are to maximize the static pressure ratio and minimize the total pressure loss coefficient at the design point, and to maximize the operating range for the positive and negative incidences. To alleviate the computational load, a design of experiment (DOE)-based GA–BP-ANN model is constructed to rapidly approximate the cascade aerodynamic performance in the optimization process. The artificial neural network (ANN) is trained by the genetic algorithm (GA) technique and back propagation (BP) algorithm, where the training cascades are sampled by the DOE method and analysed by the computational fluid dynamics method. The multi-objective genetic algorithm is used to search for a series of Pareto-optimum solutions, from which an optimal cascade is found out whose objectives are all better than (ABT) those of the original design. The ABT cascade is characterized by the lower camber and higher turning angle, leading to better aerodynamic performance in a widened operating range. Compared with the original design, the ABT cascade decreases the total pressure loss coefficient by 1.54 per cent, 23.4 per cent, and 7.87 per cent at the incidences of 5°, −9°, and 13°, respectively. The established optimization design method can be extended to the three-dimensional aerodynamic design of axial compressor blade.

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