Thermal-current density characteristic curves for a single-side permanent magnet synchronous linear motor

Liu, Xiaomei; Yu, Haitao; Guo, Rong; Liu, Yulei

doi:10.3233/jae-190006

Cited by 4 publications

(2 citation statements)

References 22 publications

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“…Along with increasing power density and progressively compact structures, heating and temperature rise become the crux in various applications of LGs [12]. Heating problems affect the reliability of LGs, lead to thermal deformation, and cause temperature rise potentially influencing the performance of other adjacent apparatuses.…”

Section: Introductionmentioning

confidence: 99%

Electromagnetic–Thermal Characteristics Analysis of a Tubular Permanent Magnet Linear Generator for Free-Piston Engines

Liu,

Feng,

et al. 2024

Applied Sciences

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Temperature rise of the tubular permanent magnet linear generator (TPMLG) might lead to insulation failure and demagnetization of permanent magnets, affecting the safe and stable operation of other equipment and the entire system. Herein, a bidirectional electromagnetic–thermal coupling method for analyzing the electromagnetic loss and thermal characteristics of a TPMLG considering the effect of increased temperature on the permanent magnet was proposed. To study the electromagnetic–thermal characteristics of the TPMLG under stable power generation, a two-dimensional electromagnetic field model and a three-dimensional temperature field model were established and coupled. The temperature field of the TPMLG was numerically calculated using computational fluid dynamics over finite volume method under natural air cooling and forced air cooling conditions. Effects of loss and air flow velocity on the steady temperature field were investigated. Results indicated that copper loss increased by 24.5% considering the influence of temperature rise. The windings’ top central position in the TPMLG was the spot with the highest temperature of 127.8 °C and there was a potential demagnetization risk for the permanent magnets. Some reference for future research of clarifying thermal characteristics and cooling design was provided.

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Section: Introductionmentioning

confidence: 99%

Electromagnetic–Thermal Characteristics Analysis of a Tubular Permanent Magnet Linear Generator for Free-Piston Engines

Liu,

Feng,

et al. 2024

Applied Sciences

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“…However, in the process of motor operation, the thermal performance of the motor was not considered, and the thermal effect of the linear motor is an important factor that should not be ignored. Liu et al [17] studied the current density of permanent magnet synchronous linear motor(PMLSM) and designed a PMLSM with high current density by using electromagnetic-thermal coupling field analysis and fitting method. Li et al [18] studied the thermal characteristics of T-type linear ultrasonic motor with longitudinal vibration transducer under no-load by measuring the surface temperature of the driving pin, piezoelectric ceramics and end cover with a thermal imager.…”

Section: Introductionmentioning

confidence: 99%

Thermal Error Model of Linear Motor Feed System Based on Bayesian Neural Network

et al. 2021

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The linear motor feed system has been in service in complex working conditions for a long time, thus causing the nonuniform distribution of the temperature field distribution. Thus, thermal error has become a key factor affecting system motion accuracy. To maximize the accuracy and efficiency of thermal error compensation for linear motor feed system, an improved modeling method for the thermal error of the linear motor feed system based on Bayesian neural networks is proposed in combination with the strong generalization performance and avoidance of overfitting of Bayesian neural networks. And the specific modeling ideas are as follows: Firstly, the X-Y cross-type two-axis linear motor feed system is as the test object. Aiming at the slow convergence , over fitting and under fitting problems of traditional neural network, Bayesian neural network is used to model the thermal error of linear motor feed system .Secondly, In order to avoid the influence of multicollinearity data on the final results, the grey relation analysis method is used to screen the temperature measuring points, and the data with large relation degree is selected for modeling to ensure the prediction accuracy of neural network. Thirdly, and the temperature variables of sensitive points and thermal positioning errors are taken as data input samples. Fourthly, a Bayesian neural network model is established. Fifthly, the hyperparameters of the Bayesian neural network is determined by a calculating method of Hessian matrix by Gauss Newton approximation. And finally, a thermal error prediction model is established. The comparison and analysis with the neural network constructed by ordinary Levenberg-Marquardt algorithm after a series of experimental demonstrations see that the prediction accuracy of the proposed method can be enhanced by up to 10%. It also shows that the prediction model has the advantages of high precision, strong generalization ability, strong anti-disturbance ability and strong robustness, etc. Therefore, the prediction model is expected to be widely used in the prediction and compensation of thermal error of the feed system of high-speed CNC machine tools in practical machining occasions.

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Temperature field analysis and cooling structure optimization of permanent magnet linear synchronous motor

Chen,

Luo,

Cai

et al. 2024

Thermal Science and Engineering Progress

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Thermal-current density characteristic curves for a single-side permanent magnet synchronous linear motor

Cited by 4 publications

References 22 publications

Electromagnetic–Thermal Characteristics Analysis of a Tubular Permanent Magnet Linear Generator for Free-Piston Engines

Electromagnetic–Thermal Characteristics Analysis of a Tubular Permanent Magnet Linear Generator for Free-Piston Engines

Thermal Error Model of Linear Motor Feed System Based on Bayesian Neural Network

Temperature field analysis and cooling structure optimization of permanent magnet linear synchronous motor

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