• Journal of Computational Design and Engineering
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• 제1권2호
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• pp.88-95
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• 2014

Because the cost of performance testing using actual products is expensive, manufacturers use lower-cost computer-aided design simulations for this function. In this paper, we propose using hexahedral meshes, which are more accurate than tetrahedral meshes, for finite element analysis. We propose automatic hexahedral mesh generation with sharp features to precisely represent the corresponding features of a target shape. Our hexahedral mesh is generated using a voxel-based algorithm. In our previous works, we fit the surface of the voxels to the target surface using Laplacian energy minimization. We used normal vectors in the fitting to preserve sharp features. However, this method could not represent concave sharp features precisely. In this proposal, we improve our previous Laplacian energy minimization by adding a term that depends on multi-normal vectors instead of using normal vectors. Furthermore, we accentuate a convex/concave surface subset to represent concave sharp features.

• 대한전기학회논문지:전기기기및에너지변환시스템부문B
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• 제53권5호
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• pp.332-337
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• 2004

A number of techniques have been developed for estimation of speed or position in motor drives. The accuracy of these techniques is affected by the variation of motor parameters such as the stator resistance, stator inductance or torque constant. This paper is proposed a neural network based estimator for torque and stator resistance in IPMSM Drives. The neural weights are initially chosen randomly and a model reference algorithm adjusts those weights to give the optimum estimations. The neural network estimator is able to track the varying. parameters quite accurately at different speeds with consistent performance. The neural network parameter estimator has been applied to slot and flux linkage torque ripple minimization of the IPMSM. The validity of the proposed parameter estimator is confirmed by the operating characteristics controlled by neural networks control.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2009년도 제40회 하계학술대회
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• pp.895_896
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• 2009

this paper presents a rotor shape optimization of interior type permanent magnet (IPM) motor for vibration minimization. the vibration of permanent magnet motor is generated by cogging torque, radial force and commutation torque ripple which are electromagnetic source of vibration. In order to minimize the vibration, the optimal notches are put on the rotor pole face and the arc type pole face is applied. The variations of cogging torque and radial force of each model vibration frequency are computation by finite element method (FEM) and the validity of the analysis and rotor shape design is confirmed by vibration experiments.

• Journal of Electrical Engineering and Technology
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• 제3권1호
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• pp.68-78
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• 2008

This paper introduces a complete package for SRM drive on Power System Computer-Aided Design/Electromagnetic Transients (PSCAD/EMTDC). A three-phase SRM drive is modeled and simulated on PSCAD. The motor is modeled using an accurate nonlinear analytical model that takes into consideration the machine nonlinearities. A current control algorithm is applied for torque ripple minimization to achieve a smooth output torque which is necessary for high performance applications. The motor drive is tested for four-quadrant operations. The modeled SRM is capable of operating as a motor or generator during clockwise and counterclockwise motions. The proposed package helps in understanding the operational principles of switched reluctance motors, investigating the dynamic characteristics of SRM drives, and achieving a high performance dynamic control task.

• 제어로봇시스템학회논문지
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• 제13권5호
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• pp.429-433
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• 2007

A number of techniques have been developed for estimation of speed or position in motor drives. The accuracy of these techniques is affected by the variation of motor parameters such as the stator resistance, stator inductance or torque constant. This paper is proposed a neural network based estimator for torque and ststor resistance in IPMSM Drives. The neural weights are initially chosen randomly and a model reference algorithm adjusts those weights to give the optimum estimations. The neural network estimator is able to track the varying parameters quite accurately at different speeds with consistent performance. The neural network parameter estimator has been applied to slot and flux linkage torque ripple minimization of the IPMSM. The validity of the proposed parameter estimator is confirmed by the operating characteristics controlled by neural networks control.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2006년도 심포지엄 논문집 정보 및 제어부문
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• pp.207-209
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• 2006

A number of techniques have been developed for estimation of speed or position in motor drives. The accuracy of these techniques is affected by the variation of motor parameters such as the stator resistance, stator inductance or torque constant. This paper is proposed a neural network based estimator for torque and ststor resistance in IPMSM Drives. The neural weights are initially chosen randomly and a model reference algorithm adjusts those weights to give the optimum estimations. The neural network estimator is able to track the varying parameters quite accurately at different speeds with consistent performance. The neural network parameter estimator has been applied to slot and flux linkage torque ripple minimization of the IPMSM. The validity of the proposed parameter estimator is confirmed by the operating characteristics controlled by neural networks control.

• 전력전자학회:학술대회논문집
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• 전력전자학회 1998년도 Proceedings ICPE 98 1998 International Conference on Power Electronics
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• pp.476-481
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• 1998

In this paper, a commutation torque minimization method using parameter observer for a brushless DC motor fed by a voltage source inverter is described. In order to investigate the nature of the commutation torque ripple in trapezoidal brushless DC motor, a new model of the motor is proposed. The optimal drive voltage to minimize the ripple torque is represented as a function of the motor parameters. Therefore, the important parameter is estimated by least-square algorithm.

• 전기학회논문지
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• 제60권2호
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• pp.293-300
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• 2011

A number of techniques have been developed for estimation of speed or position in motor drives. The accuracy of these techniques is affected by the variation of motor parameters such as the stator resistance, stator inductance or torque constant. This paper is proposed a neural network based estimator for torque and stator resistance and adaptive fuzzy learning contrroller(AFLC) for speed control in IPMSM Drives. AFLC is chaged fuzzy rule base by rule base modifier for robust control of IPMSM. The neural weights are initially chosen randomly and a model reference algorithm adjusts those weights to give the optimum estimations. The neural network estimator is able to track the varying parameters quite accurately at different speeds with consistent performance. The neural network parameter estimator has been applied to slot and flux linkage torque ripple minimization of the IPMSM. The validity of the proposed parameter estimator and AFLC is confirmed by comparing to conventional algorithm.

• International Journal of Automotive Technology
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• 제8권5호
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• pp.563-573
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• 2007

Optimal design of an automotive catalytic converter for minimization of cold-start emissions is numerically performed using a micro genetic algorithm for two optimization problems: optimal geometry design of the monolith for various operating conditions and optimal axial catalyst distribution. The optimal design process considered in this study consists of three modules: analysis, optimization, and control. The analysis module is used to evaluate the objective functions with a one-dimensional single channel model and the Romberg integration method. It obtains new design variables from the control module, produces the CO cumulative emissions and the integral value of a catalyst distribution function over the monolith volume, and provides objective function values to the control module. The optimal design variables for minimizing the objective functions are determined by the optimization module using a micro genetic algorithm. The control module manages the optimal design process that mainly takes place in both the analysis and optimization modules.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2005년도 제36회 하계학술대회 논문집 B
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• pp.1144-1146
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• 2005

This paper presents the optimal design reducing the rotor inertia in order to improve the driving characteristic of BLDCM for robots. The parallel Genetic Algorithm is performed to rotor inertia minimization in optimal design. Also, velocity profile with finite jerk method is introduced to reduce vibration of BLDCM. As a result, a torque characteristic is same although rotor inertia is reduced 2/3 compared with prototype model. And, maximum vibration value is reduced by 63.4[%1 according to apply finite jerk.