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Article
Publication date: 8 August 2019

Vahid Ghorbanian, Mohammad Hossain Mohammadi and David Lowther

This paper aims to propose a data-driven approach to determine the design guidelines for low-frequency electromagnetic devices.

Abstract

Purpose

This paper aims to propose a data-driven approach to determine the design guidelines for low-frequency electromagnetic devices.

Design/methodology/approach

Two different devices, a core-type single-phase transformer and a motor-drive system, are used to show the usefulness and generalizability of the proposed approach. Using a finite element solver, a large database of design possibilities is created by varying design parameters, i.e. the geometrical and control parameters of the systems. Design rules are then extracted by performing a statistical analysis and exploring optimal and sub-optimal designs considering various targets such as efficiency, torque ripple and power factor.

Findings

It is demonstrated that the correlation of the design parameters influences the way the data-driven approach must be made. Also, guidelines for defining new design constraints, which can lead to a more efficient optimization routine, are introduced for both case studies.

Originality/value

Using the proposed approach, new design guidelines, which are generally not obtainable by the classical design methods, are introduced. Also, the proposed approach can potentially deal with different parameter–objective correlations, as well as different number of connected systems. This approach is applicable regardless of the device type.

Details

COMPEL - The international journal for computation and mathematics in electrical and electronic engineering , vol. 38 no. 5
Type: Research Article
ISSN: 0332-1649

Keywords

Article
Publication date: 7 January 2020

David Lowther, Vahid Ghorbanian, Mohammad Hossain Mohammadi and Issah Ibrahim

The design of electromagnetic systems for a variety of applications such as induction heating, electrical machines, actuators and transformers requires the solution of a…

Abstract

Purpose

The design of electromagnetic systems for a variety of applications such as induction heating, electrical machines, actuators and transformers requires the solution of a multi-physics problem often involving thermal, structural and mechanical coupling to the electromagnetic system. This results in a complex analysis system embedded within an optimization process. The appearance of high-performance computing systems over the past few years has made coupled simulations feasible for the design engineer. When coupled with surrogate modelling techniques, it is possible to significantly reduce the wall clock time for generating a complete design while including the impact of the multi-physics performance on the device.

Design/methodology/approach

An architecture is proposed for linking multiple singe physics analysis tools through the material models and a controller which schedules the execution of the various software tools. The combination of tools is implemented on a series of computational nodes operating in parallel and creating a “super node” cluster within a collection of interconnected processors.

Findings

The proposed architecture and job scheduling system can allow a parallel exploration of the design space for a device.

Originality/value

The originality of the work derives from the organization of the parallel computing system into a series of “super nodes” and the creation of a materials database suitable for multi-physics interactions.

Details

COMPEL - The international journal for computation and mathematics in electrical and electronic engineering , vol. 39 no. 1
Type: Research Article
ISSN: 0332-1649

Keywords

Article
Publication date: 29 March 2022

Issah Ibrahim, Mohammad Hossain Mohammadi, Vahid Ghorbanian and David Lowther

Acoustic noise is a crucial performance index in the design of electrical machines. Due to the challenges associated with modelling a complete motor, the stator is often…

Abstract

Purpose

Acoustic noise is a crucial performance index in the design of electrical machines. Due to the challenges associated with modelling a complete motor, the stator is often used to estimate the sound power in the prototyping stage. While this approach greatly reduces lengthy simulations, the actual sound power of the motor may not be known. But, from the acoustic noise standpoint, not much is known about the correlation between the stator and complete motor. This paper, therefore, aims to use the sound pressure levels of the stator and the full motor to investigate the existence of correlations in the interior permanent magnet synchronous motor.

Design/methodology/approach

A multiphysics simulation framework is proposed to evaluate the sound pressure levels of multiple motor geometries in a given design space. Then, a statistical analysis is performed on the calculated sound pressure levels of each geometry over a selected speed range to compare the correlation strength between the stator and the full model.

Findings

It was established that the stator and the complete motor model are moderately correlated. As such, a reliance on the stator sound power for design and optimization routines could yield inaccurate results.

Originality/value

The main contribution involves the use of statistical tools to study the relationship between sound pressure levels associated with the stator geometry and the complete electric motor by increasing the motor sample size to capture subtle acoustic correlation trends in the design space of the interior permanent magnet synchronous motor.

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: 26 May 2022

Liang Jin, Yuankai Liu, Qingxin Yang, Chuang Zhang and Suzhen Liu

Under the condition of small data set, a prediction model of motor magnetic field is established based on deep learning method. This paper aims to complete the magnetic…

Abstract

Purpose

Under the condition of small data set, a prediction model of motor magnetic field is established based on deep learning method. This paper aims to complete the magnetic field prediction quickly and accurately.

Design/methodology/approach

An improved Linknet model is proposed to predict the motor magnetic field. This is a digital twin technology, which can predict the function values of other points according to the function values of typical sampling points. The results of magnetic field distribution are represented by color images. By predicting the pixels of the image, the corresponding magnetic field distribution is obtained. The model not only considers the correlation between pixels but also retains the spatial information in the original input image and can well learn the mapping relationship between motor structure and magnetic field.

Findings

The model can speed up the calculation while ensuring the accuracy and has obvious advantages in large-scale calculation and real-time simulation.

Originality/value

Under the condition of small data set, the model can well learn the mapping relationship between motor structure and magnetic field, so as to complete the magnetic field prediction quickly and accurately. In the future, according to the characteristics of magnetic field distribution, it will lay a foundation for solving the problems of rapid optimization, real-time simulation and physical field control of electrical equipment.

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

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