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Article
Publication date: 2 March 2022

Yuki Hidaka

The purpose of this paper is to develop a multi-material topology optimization method for permanent magnet-assisted synchronous reluctance motors.

Abstract

Purpose

The purpose of this paper is to develop a multi-material topology optimization method for permanent magnet-assisted synchronous reluctance motors.

Design/methodology/approach

In the proposed method, the optimization procedure consists of two steps. In the first step, the entire rotor area was selected for the design region and the distribution of the core and air materials was optimized. In the second step, the design region was limited to the air region of the former solution and the distribution of magnets and cores or magnets and air was optimized.

Findings

Because of the two-step process of the proposed method, the design parameters can be reduced compared to the conventional method. As a result, this study can prevent the solution space from becoming more complex and superior solutions can be founded effectively.

Research limitations/implications

Since limited case study is denoted in this paper, much more case studies, for example, three-dimensional optimization problems, are needed to be discussed.

Practical implications

The optimal solutions obtained by the proposed method have a smaller magnet volume and higher average torque than that of the conventional method.

Originality/value

In the proposed methods, optimization methodology, which consists of two-steps process, is differed from the conventional method.

Details

COMPEL - The international journal for computation and mathematics in electrical and electronic engineering , vol. 41 no. 5
Type: Research Article
ISSN: 0332-1649

Keywords

Article
Publication date: 3 November 2021

Hayaho Sato and Hajime Igarashi

This paper aims to present a deep learning–based surrogate model for fast multi-material topology optimization of an interior permanent magnet (IPM) motor. The…

Abstract

Purpose

This paper aims to present a deep learning–based surrogate model for fast multi-material topology optimization of an interior permanent magnet (IPM) motor. The multi-material topology optimization based on genetic algorithm needs large computational burden because of execution of finite element (FE) analysis for many times. To overcome this difficulty, a convolutional neural network (CNN) is adopted to predict the motor performance from the cross-sectional motor image and reduce the number of FE analysis.

Design/methodology/approach

To predict the average torque of an IPM motor, CNN is used as a surrogate model. From the input cross-sectional motor image, CNN infers dq-inductance and magnet flux to compute the average torque. It is shown that the average torque for any current phase angle can be predicted by this approach, which allows the maximization of the average torque by changing the current phase angle. The individuals in the multi-material topology optimization are evaluated by the trained CNN, and the limited individuals with higher potentials are evaluated by finite element method.

Findings

It is shown that the proposed method doubles the computing speed of the multi-material topology optimization without loss of search ability. In addition, the optimized motor obtained by the proposed method followed by simplification for manufacturing is shown to have higher average torque than a reference model.

Originality/value

This paper proposes a novel method based on deep learning for fast multi-material topology optimization considering the current phase angle.

Details

COMPEL - The international journal for computation and mathematics in electrical and electronic engineering , vol. 41 no. 3
Type: Research Article
ISSN: 0332-1649

Keywords

Article
Publication date: 12 August 2022

Kang Liu, Yingchun Bai, Shouwen Yao and Shenggang Luan

The purpose of this paper is to develop a topology optimization algorithm considering natural frequencies.

Abstract

Purpose

The purpose of this paper is to develop a topology optimization algorithm considering natural frequencies.

Design/methodology/approach

To incorporate natural frequency as design criteria of shell-infill structures, two types of design models are formulated: (1) type I model: frequency objective with mass constraint; (2) type II model: mass objective with frequency constraint. The interpolation functions are constructed by the two-step density filtering approach to describe the fundamental topology of shell-infill structure. Sensitivities of natural frequencies and mass with respect to the original element densities are derived, which will be used for both type I model and type II model. The method of moving asymptotes is used to solve both models in combination with derived sensitivities.

Findings

Mode switching is one of the challenges faced in eigenfrequency optimization problems, which can be overcome by the modal-assurance-criterion-based mode-tracking strategy. Furthermore, a shifting-frequency-constraint strategy is recommended for type II model to deal with the unsatisfactory topology obtained under direct frequency constraint. Numerical examples are systematically investigated to demonstrate the effectiveness of the proposed method.

Originality/value

In this paper, a topology optimization method considering natural frequencies is proposed by the author, which is useful for the design of shell-infill structures to avoid the occurrence of resonance in dynamic conditions.

Details

Engineering Computations, vol. 39 no. 8
Type: Research Article
ISSN: 0264-4401

Keywords

Article
Publication date: 1 September 2022

Xuwen Chi, Cao Tan, Bo Li, Jiayu Lu, Chaofan Gu and Changzhong Fu

The purpose of this paper is to solve the common problems that traditional optimization methods cannot fully improve the performance of electromagnetic linear actuators (EMLAs).

Abstract

Purpose

The purpose of this paper is to solve the common problems that traditional optimization methods cannot fully improve the performance of electromagnetic linear actuators (EMLAs).

Design/methodology/approach

In this paper, a multidisciplinary optimization (MDO) method based on the non-dominated sorting genetic algorithm-II (NSGA-II) algorithm was proposed. An electromagnetic-mechanical coupled actuator analysis model of EMLAs was established, and the coupling relationship between static/dynamic performance of the actuator was analyzed. Suitable optimization variables were designed based on fuzzy grayscale theory to address the incompleteness of the actuator data and the uncertainty of the coupling relationship. A multiobjective genetic algorithm was used to obtain the optimal solution set of Pareto with the maximum electromagnetic force, electromagnetic force fluctuation rate, time constant and efficiency as the optimization objectives, the final optimization results were then obtained through a multicriteria decision-making method.

Findings

The experimental results show that the maximum electromagnetic force, electromagnetic force fluctuation rate, time constants and efficiency are improved by 18.1%, 38.5%, 8.5% and 12%, respectively. Compared with single-discipline optimization, the effectiveness of the multidiscipline optimization method was verified.

Originality/value

This paper proposes a MDO method for EMLAs that takes into account static/dynamic performance, the proposed method is also applicable to the design and analysis of various electromagnetic actuators.

Details

COMPEL - The international journal for computation and mathematics in electrical and electronic engineering , vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 0332-1649

Keywords

Article
Publication date: 22 August 2022

Qingxia Li, Xiaohua Zeng and Wenhong Wei

Multi-objective is a complex problem that appears in real life while these objectives are conflicting. The swarm intelligence algorithm is often used to solve such…

Abstract

Purpose

Multi-objective is a complex problem that appears in real life while these objectives are conflicting. The swarm intelligence algorithm is often used to solve such multi-objective problems. Due to its strong search ability and convergence ability, particle swarm optimization algorithm is proposed, and the multi-objective particle swarm optimization algorithm is used to solve multi-objective optimization problems. However, the particles of particle swarm optimization algorithm are easy to fall into local optimization because of their fast convergence. Uneven distribution and poor diversity are the two key drawbacks of the Pareto front of multi-objective particle swarm optimization algorithm. Therefore, this paper aims to propose an improved multi-objective particle swarm optimization algorithm using adaptive Cauchy mutation and improved crowding distance.

Design/methodology/approach

In this paper, the proposed algorithm uses adaptive Cauchy mutation and improved crowding distance to perturb the particles in the population in a dynamic way in order to help the particles trapped in the local optimization jump out of it which improves the convergence performance consequently.

Findings

In order to solve the problems of uneven distribution and poor diversity in the Pareto front of multi-objective particle swarm optimization algorithm, this paper uses adaptive Cauchy mutation and improved crowding distance to help the particles trapped in the local optimization jump out of the local optimization. Experimental results show that the proposed algorithm has obvious advantages in convergence performance for nine benchmark functions compared with other multi-objective optimization algorithms.

Originality/value

In order to help the particles trapped in the local optimization jump out of the local optimization which improves the convergence performance consequently, this paper proposes an improved multi-objective particle swarm optimization algorithm using adaptive Cauchy mutation and improved crowding distance.

Details

International Journal of Intelligent Computing and Cybernetics, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 1756-378X

Keywords

Article
Publication date: 11 August 2022

Yanbo Feng, Xiande Wu, Weidong Chen, Yaen Xie, Taihang Yu and Yong Hao

On-orbit assembly technology is a promising research topic in spaceflight field. For purposes of studying the dynamic performance and reducing weight of an on-orbit…

Abstract

Purpose

On-orbit assembly technology is a promising research topic in spaceflight field. For purposes of studying the dynamic performance and reducing weight of an on-orbit assembly satellite structure frame, this paper aims to propose a structural optimization design method based on natural frequency.

Design/methodology/approach

The dynamic stability of the satellite under working condition depends on the mechanical properties of the structure matrix. A global structural optimization model is established, with the objective of mass minimization and the constraints of given natural frequencies and given structure requirements. The structural optimization and improvement design method is proposed using sequential quadratic programming calculation.

Findings

The optimal result of objective function is effectively obtained, and the best combination of structural geometric parameters is configurated. By analyzing the relationship between the structural variables and optimization parameters, the primary and secondary factors to the mass optimization process of the microsatellite satisfying the dynamic performance requirements are obtained, which improves the effectiveness and accuracy of the system optimization design.

Originality/value

This method can coordinate the relation between satellite vibration stability and weight reduction, which provides an effective way for the optimization design of on-orbit assembly microsatellite. It has reference significance for the similar spacecraft framework structure design.

Details

Aircraft Engineering and Aerospace Technology, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 1748-8842

Keywords

Article
Publication date: 8 February 2016

Jun Sun, Lei Shu, Xianhao Song, Guangsheng Liu, Feng Xu, Enming Miao, Zhihao Xu, Zheng Zhang and Junwei Zhao

This paper aims to use the crankshaft-bearing system of a four-cylinder internal combustion engine as the studying object, and develop a multi-objective optimization

Abstract

Purpose

This paper aims to use the crankshaft-bearing system of a four-cylinder internal combustion engine as the studying object, and develop a multi-objective optimization design of the crankshaft-bearing. In the current optimization design of engine crankshaft-bearing, only the crankshaft-bearing was considered as the studying object. However, the corresponding relations of major structure dimensions exist between the crankshaft and the crankshaft-bearing in internal combustion engine, and there are the interaction effects between the crankshaft and the crankshaft-bearing during the operation of internal combustion engine.

Design/methodology/approach

The crankshaft mass and the total frictional power loss of crankshaft-bearing s are selected as the objective functions in the optimization design of crankshaft-bearing. The Particle Swarm Optimization algorithm based on the idea of decreasing strategy of inertia weight with the exponential type is used in the optimization calculation.

Findings

The total frictional power loss of crankshaft-bearing and the crankshaft mass are decreased, respectively, by 26.2 and 5.3 per cent by the multi-objective optimization design of crankshaft-bearing, which are more reasonable than the ones of single-objective optimization design in which only the crankshaft-bearing is considered as the studying object.

Originality/value

The crankshaft-bearing system of a four-cylinder internal combustion engine is taken as the studying object, and the multi-objective optimization design of crankshaft-bearing based on the crankshaft-bearing system is developed. The results of this paper are helpful to the design of the crankshaft-bearing for engine. There is universal significance to research the multi-objective optimization design of crankshaft-bearing based on the crankshaft-bearing system. The research method of the multi-objective optimization design of crankshaft-bearing based on the crankshaft-bearing system can be used to the optimization design of the bearing in the shaft-bearing system of ordinary machinery.

Article
Publication date: 1 March 1997

Chongbin Zhao, G.P. Steven and Y.M. Xie

Extends the evolutionary structural optimization method to the solution for the natural frequency optimization of a two‐dimensional structure with additional…

Abstract

Extends the evolutionary structural optimization method to the solution for the natural frequency optimization of a two‐dimensional structure with additional non‐structural lumped masses. Owing to the significant difference between a static optimization problem and a structural natural frequency optimization problem, five basic criteria for the evolutionary natural frequency optimization have been established. The inclusion of these criteria into the evolutionary structural optimization method makes it possible to solve structural natural frequency optimization problems for two‐dimensional structures with additional non‐structural lumped masses. Gives two examples to demonstrate the feasibility of the extended evolutionary structural optimization method when it is used to solve structural natural frequency optimization problems.

Details

Engineering Computations, vol. 14 no. 2
Type: Research Article
ISSN: 0264-4401

Keywords

Article
Publication date: 3 July 2017

Jacek Mieloszyk

The paper aims to apply numerical optimization to the aircraft design procedures applied in the airspace industry.

Abstract

Purpose

The paper aims to apply numerical optimization to the aircraft design procedures applied in the airspace industry.

Design/methodology/approach

It is harder than ever to achieve competitive construction. This is why numerical optimization is becoming a standard tool during the design process. Although optimization procedures are becoming more mature, yet in the industry practice, fairly simple examples of optimization are present. The more complicated is the task to solve, the harder it is to implement automated optimization procedures. This paper presents practical examples of optimization in aerospace sciences. The methodology is discussed in the article in great detail.

Findings

Encountered problems related to the numerical optimization are presented. Different approaches to the solutions of the problems are shown, which have impact on the time of optimization computations and quality of the obtained optimum. Achieved results are discussed in detail with relation to the used settings.

Practical implications

Investigated different aspects of handling optimization problems, improving quality of the obtained optimum or speeding-up optimization by parallel computations can be directly applied in the industry optimization practice. Lessons learned from multidisciplinary optimization can bring industry products to higher level of performance and quality, i.e. more advanced, competitive and efficient aircraft design procedures, which could be applied in the industry practice. This can lead to the new approach of aircraft design process.

Originality/value

Introduction of numerical optimization methods in aircraft design process. Showing how to solve numerical optimization problems related to advanced cases of conceptual and preliminary aircraft design.

Details

Aircraft Engineering and Aerospace Technology, vol. 89 no. 4
Type: Research Article
ISSN: 1748-8842

Keywords

Article
Publication date: 1 August 2022

Rustanto Nanang, Connie Susilawati and Martin Skitmore

Governments in developing countries manage their considerable state assets for public service delivery directly. In Indonesia, the Directorate of State Asset Management…

Abstract

Purpose

Governments in developing countries manage their considerable state assets for public service delivery directly. In Indonesia, the Directorate of State Asset Management responsible for developing the national strategy for state asset optimization requires the determination of key elements and prioritization tools. The purpose of this paper is to show that a simple calculation using the combination of the balanced scorecard (BCS) and analytical hierarchy process (AHP) will help in the prioritization of strategy development.

Design/methodology/approach

A questionnaire survey of 131 multistakeholder respondents to identify the most important key elements and the best alternative for asset optimization was done in this study.

Findings

The respondents agree on the most important key elements, and that the best alternative for asset optimization is the efficient maintenance of assets. Competitive human resources comprise the recommended second key element, and that improvements in asset performance and value will improve public service as the second-highest alternative. This study also shows the importance of the integration of asset optimization in existing government strategic instruments supported by a comprehensive data set related to public assets and their performance.

Originality/value

This paper provides a new contribution to integrating asset optimization strategies as the core of the organization’s performance and prioritization strategies. Additional BSC perspectives are suggested, with the inclusion of AHP for prioritization. In addition, this study includes the opinions of all the stakeholders, from external users to the central management. The flexibility of the tools to adapt to the existing strategic framework will allow their application by different agencies and in different countries.

Details

Construction Innovation , vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 1471-4175

Keywords

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