Neural networks require a lot of training to understand the model of a plant or a process. Issues such as learning speed, stability, and weight convergence remain as areas of research and comparison of many training a...Neural networks require a lot of training to understand the model of a plant or a process. Issues such as learning speed, stability, and weight convergence remain as areas of research and comparison of many training algorithms. The application of neural networks to control interior permanent magnet synchronous motor using direct torque control （DTC） is discussed. A neural network is used to emulate the state selector of the DTC. The neural networks used are the back-propagation and radial basis function. To reduce the training patterns and increase the execution speed of the training process, the inputs of switching table are converted to digital signals, i.e., one bit represent the flux error, one bit the torque error, and three bits the region of stator flux. Computer simulations of the motor and neural-network system using the two approaches are presented and compared. Discussions about the back-propagation and radial basis function as the most promising training techniques are presented, giving its advantages and disadvantages. The system using back-propagation and radial basis function networks controller has quick parallel speed and high torque response.展开更多
Several available mechanistic-empirical pavement design methods fail to include predictive model for permanent deformation(PD)of unbound granular materials(UGMs),which make these methods more conservative.In addition,...Several available mechanistic-empirical pavement design methods fail to include predictive model for permanent deformation(PD)of unbound granular materials(UGMs),which make these methods more conservative.In addition,there are limited regression models capable of predicting the PD under multistress levels,and these models have regression limitations and generally fail to cover the complexity of UGM behaviour.Recent researches are focused on using new methods of computational intelligence systems to address the problems,such as artificial neural network(ANN).In this context,we aim to develop an artificial neural model to predict the PD of UGMs exposed to repeated loads.Extensive repeated load triaxial tests(RLTTs)were conducted on base and subbase materials locally available in Victoria,Australia to investigate the PD properties of the tested materials and to prepare the database of the neural networks.Specimens were prepared over different moisture contents and gradations to cover a wide testing matrix.The ANN model consists of one input layer with five neurons,one hidden layer with twelve neurons,and one output layer with one neuron.The five inputs were the number of load cycles,deviatoric stress,moisture content,coefficient of uniformity,and coefficient of curvature.The sensitivity analysis showed that the most important indicator that impacts PD is the number of load cycles with influence factor of 41%.It shows that the ANN method is rapid and efficient to predict the PD,which could be implemented in the Austroads pavement design method.展开更多
In this paper,the equivalent reluctance network model(ERNM)is used to calculate the magnetic circuit of a permanent magnet-assisted synchronous reluctance motor(PMASynRM)and calculate no-load air-gap magnetic field an...In this paper,the equivalent reluctance network model(ERNM)is used to calculate the magnetic circuit of a permanent magnet-assisted synchronous reluctance motor(PMASynRM)and calculate no-load air-gap magnetic field and electromagnetic torque.Iteration method is used to solve the relative permeability of iron core.A novel reluctance network model based on actual distribution of the magnetic flux inside the motor is established.The magnetomotive force(MMF)generated by armature winding affects the relative permeability of iron core,which is considered in the calculation of ERNM to improve the accuracy when the motor is under load.ERNM can be used to measure air-gap flux density,no-load back electromotive force(EMF),the average value of motor torque,the armature winding voltage under load,and power factor.The method of calculating the motor performance is proposed.The results of calculation are consistent with finite element method(FEM)and the computational complexity is much less than that of the FEM.The results of ERNM has been verified,which will provide a simple method for motor design and analysis.展开更多
To improve the heat dissipation performance,this paper proposes a novel hybrid cooling method for high-speed high-power Permanent Magnet assisted Synchronous Reluctance Starter/Generator(PMa Syn R S/G)in aerospace app...To improve the heat dissipation performance,this paper proposes a novel hybrid cooling method for high-speed high-power Permanent Magnet assisted Synchronous Reluctance Starter/Generator(PMa Syn R S/G)in aerospace applications.The hybrid cooling structure with oil circulation in the housing,oil spray at winding ends and rotor end surface is firstly proposed for the PMa Syn R S/G.Then the accurate loss calculation of the PMa Syn R S/G is proposed,which includes air gap friction loss under oil spray cooling,copper loss,stator and rotor core loss,permanent magnet eddy current loss and bearing loss.The parameter sensitivity analysis of the hybrid cooling structure is proposed,while the equivalent thermal network model of the PMa Syn R S/G is established considering the uneven spraying at the winding ends.Finally,the effectiveness of the proposed hybrid cooling method is demonstrated on a 40 k W/24000 r/min PMa Syn R S/G experimental platform.展开更多
In this paper,a 20kW vehicle built-in permanent magnet synchronous motor is taken as an example,and a magnetic barrier structure is added to the rotor of the motor to solve the uneven saturation problem of the rotor s...In this paper,a 20kW vehicle built-in permanent magnet synchronous motor is taken as an example,and a magnetic barrier structure is added to the rotor of the motor to solve the uneven saturation problem of the rotor side magnetic bridge.This structure improves the air-gap flux density waveform of the motor by influencing the internal magnetic flux path of the motor rotor,thus improving the sine of the no-load back EMF waveform of the motor and reducing the torque ripple of the motor.At the same time,Taguchi method is used to optimize the structural parameters of the added magnetic barrier.In order to facilitate the analysis of its uneven saturation phenomenon and improve the optimization effect,a simple equivalent magnetic network(EMN)model considering the uneven saturation of rotor magnetic bridge is established in this paper,and the initial values of optimization factors are selected based on this model.Finally,the no-load back EMF waveform distortion rate,torque ripple and output torque of the optimized motor are compared and analyzed,and the influence of magnetic barrier structure parameters on the electromagnetic performance of the motor is also analyzed.The results show that the optimized motor can not change the output torque of the motor as much as possible on the basis of reducing the waveform distortion rate of no-load back EMF and torque ripple.展开更多
In this study, finite element analysis based on an Ansoft Maxwell software was used to reveal the temperature stability of a magnet ring and the equivalent structural periodic permanent-magnet（PPM） focusing system. ...In this study, finite element analysis based on an Ansoft Maxwell software was used to reveal the temperature stability of a magnet ring and the equivalent structural periodic permanent-magnet（PPM） focusing system. It is found that with the temperature increasing, the decrease rate of magnetic induction peak（Bz）maxof single magnet ring is greater than that of remanence Brof magnet in the range from room temperature to 200 °C, however,the PPM focusing system do have the same temperature characteristics of permanent-magnet materials. It indicates that the magnetic temperature properties of the PPM system can be effectively controlled by adjusting the temperature properties of the magnets. Moreover, the higher permeability of the magnets indicates the less Hcb, giving rise to lower magnetic induction peak （Bz）′max： Finally, it should be noted that the magnetic orientation deviation angle θ（／15°） of permanent magnets has little effect on the focusing magnetic field of the PPM system at different temperatures and the temperature stability. The obtained results are beneficial to the design and selection of permanent magnets for PPM focusing system.展开更多
Aiming at obtaining high power density of surface-mounted and interior permanent magnet synchronous motor(SIPMSM),it is important to accurately calculate the temperature field distribution of SIPMSM,and a magnetic-the...Aiming at obtaining high power density of surface-mounted and interior permanent magnet synchronous motor(SIPMSM),it is important to accurately calculate the temperature field distribution of SIPMSM,and a magnetic-thermal coupling method is proposed.The magnetic-thermal coupling mechanism is analyzed.The thermal network model and finite element model are built by this method,respectively.The effects of power frequency on iron losses and temperature fields are analyzed by the magnetic-thermal coupling finite element model under the condition of rated load,and the relationship between the load and temperature field is researched under the condition of the synchronous speed.In addition,the equivalent thermal network model is used to verify the magnetic-thermal coupling method.Then the temperatures of various nodes are obtained.The results show that there are advantages in both computational efficiency and accuracy for the proposed coupling method,which can be applied to other permanent magnet motors with complex structures.展开更多
The permanent magnet eddy current coupler(PMEC)solves the problem of flexible connection and speed regulation between the motor and the load and is widely used in electrical transmission systems.It provides torque to ...The permanent magnet eddy current coupler(PMEC)solves the problem of flexible connection and speed regulation between the motor and the load and is widely used in electrical transmission systems.It provides torque to the load and generates heat and losses,reducing its energy transfer efficiency.This issue has become an obstacle for PMEC to develop toward a higher power.This paper aims to improve the overall performance of PMEC through multi-objective optimization methods.Firstly,a PMEC modeling method based on the Levenberg-Marquardt back propagation(LMBP)neural network is proposed,aiming at the characteristics of the complex input-output relationship and the strong nonlinearity of PMEC.Then,a novel competition mechanism-based multi-objective particle swarm optimization algorithm(NCMOPSO)is proposed to find the optimal structural parameters of PMEC.Chaotic search and mutation strategies are used to improve the original algorithm,which improves the shortcomings of multi-objective particle swarm optimization(MOPSO),which is too fast to converge into a global optimum,and balances the convergence and diversity of the algorithm.In order to verify the superiority and applicability of the proposed algorithm,it is compared with several popular multi-objective optimization algorithms.Applying them to the optimization model of PMEC,the results show that the proposed algorithm has better comprehensive performance.Finally,a finite element simulation model is established using the optimal structural parameters obtained by the proposed algorithm to verify the optimization results.Compared with the prototype,the optimized PMEC has reduced eddy current losses by 1.7812 kW,increased output torque by 658.5 N·m,and decreased costs by 13%,improving energy transfer efficiency.展开更多
基金the National Natural Science Foundation of China (60374032).
文摘Neural networks require a lot of training to understand the model of a plant or a process. Issues such as learning speed, stability, and weight convergence remain as areas of research and comparison of many training algorithms. The application of neural networks to control interior permanent magnet synchronous motor using direct torque control （DTC） is discussed. A neural network is used to emulate the state selector of the DTC. The neural networks used are the back-propagation and radial basis function. To reduce the training patterns and increase the execution speed of the training process, the inputs of switching table are converted to digital signals, i.e., one bit represent the flux error, one bit the torque error, and three bits the region of stator flux. Computer simulations of the motor and neural-network system using the two approaches are presented and compared. Discussions about the back-propagation and radial basis function as the most promising training techniques are presented, giving its advantages and disadvantages. The system using back-propagation and radial basis function networks controller has quick parallel speed and high torque response.
文摘Several available mechanistic-empirical pavement design methods fail to include predictive model for permanent deformation(PD)of unbound granular materials(UGMs),which make these methods more conservative.In addition,there are limited regression models capable of predicting the PD under multistress levels,and these models have regression limitations and generally fail to cover the complexity of UGM behaviour.Recent researches are focused on using new methods of computational intelligence systems to address the problems,such as artificial neural network(ANN).In this context,we aim to develop an artificial neural model to predict the PD of UGMs exposed to repeated loads.Extensive repeated load triaxial tests(RLTTs)were conducted on base and subbase materials locally available in Victoria,Australia to investigate the PD properties of the tested materials and to prepare the database of the neural networks.Specimens were prepared over different moisture contents and gradations to cover a wide testing matrix.The ANN model consists of one input layer with five neurons,one hidden layer with twelve neurons,and one output layer with one neuron.The five inputs were the number of load cycles,deviatoric stress,moisture content,coefficient of uniformity,and coefficient of curvature.The sensitivity analysis showed that the most important indicator that impacts PD is the number of load cycles with influence factor of 41%.It shows that the ANN method is rapid and efficient to predict the PD,which could be implemented in the Austroads pavement design method.
基金This work was supported in part by the National Natural Science Foundation of China under Grant 51737008.
文摘In this paper,the equivalent reluctance network model(ERNM)is used to calculate the magnetic circuit of a permanent magnet-assisted synchronous reluctance motor(PMASynRM)and calculate no-load air-gap magnetic field and electromagnetic torque.Iteration method is used to solve the relative permeability of iron core.A novel reluctance network model based on actual distribution of the magnetic flux inside the motor is established.The magnetomotive force(MMF)generated by armature winding affects the relative permeability of iron core,which is considered in the calculation of ERNM to improve the accuracy when the motor is under load.ERNM can be used to measure air-gap flux density,no-load back electromotive force(EMF),the average value of motor torque,the armature winding voltage under load,and power factor.The method of calculating the motor performance is proposed.The results of calculation are consistent with finite element method(FEM)and the computational complexity is much less than that of the FEM.The results of ERNM has been verified,which will provide a simple method for motor design and analysis.
基金co-supported by the National Natural Science Foundation of China(No.52177028)in part by the Aeronautical Science Foundation of China(No.201907051002)。
文摘To improve the heat dissipation performance,this paper proposes a novel hybrid cooling method for high-speed high-power Permanent Magnet assisted Synchronous Reluctance Starter/Generator(PMa Syn R S/G)in aerospace applications.The hybrid cooling structure with oil circulation in the housing,oil spray at winding ends and rotor end surface is firstly proposed for the PMa Syn R S/G.Then the accurate loss calculation of the PMa Syn R S/G is proposed,which includes air gap friction loss under oil spray cooling,copper loss,stator and rotor core loss,permanent magnet eddy current loss and bearing loss.The parameter sensitivity analysis of the hybrid cooling structure is proposed,while the equivalent thermal network model of the PMa Syn R S/G is established considering the uneven spraying at the winding ends.Finally,the effectiveness of the proposed hybrid cooling method is demonstrated on a 40 k W/24000 r/min PMa Syn R S/G experimental platform.
基金supported by the National Natural Science Funds of China No.51907129Technology program of Liaoning province No.2021-MS-236。
文摘In this paper,a 20kW vehicle built-in permanent magnet synchronous motor is taken as an example,and a magnetic barrier structure is added to the rotor of the motor to solve the uneven saturation problem of the rotor side magnetic bridge.This structure improves the air-gap flux density waveform of the motor by influencing the internal magnetic flux path of the motor rotor,thus improving the sine of the no-load back EMF waveform of the motor and reducing the torque ripple of the motor.At the same time,Taguchi method is used to optimize the structural parameters of the added magnetic barrier.In order to facilitate the analysis of its uneven saturation phenomenon and improve the optimization effect,a simple equivalent magnetic network(EMN)model considering the uneven saturation of rotor magnetic bridge is established in this paper,and the initial values of optimization factors are selected based on this model.Finally,the no-load back EMF waveform distortion rate,torque ripple and output torque of the optimized motor are compared and analyzed,and the influence of magnetic barrier structure parameters on the electromagnetic performance of the motor is also analyzed.The results show that the optimized motor can not change the output torque of the motor as much as possible on the basis of reducing the waveform distortion rate of no-load back EMF and torque ripple.
基金financially supported by the National Natural Science Foundation of China (No. 61001120)
文摘In this study, finite element analysis based on an Ansoft Maxwell software was used to reveal the temperature stability of a magnet ring and the equivalent structural periodic permanent-magnet（PPM） focusing system. It is found that with the temperature increasing, the decrease rate of magnetic induction peak（Bz）maxof single magnet ring is greater than that of remanence Brof magnet in the range from room temperature to 200 °C, however,the PPM focusing system do have the same temperature characteristics of permanent-magnet materials. It indicates that the magnetic temperature properties of the PPM system can be effectively controlled by adjusting the temperature properties of the magnets. Moreover, the higher permeability of the magnets indicates the less Hcb, giving rise to lower magnetic induction peak （Bz）′max： Finally, it should be noted that the magnetic orientation deviation angle θ（／15°） of permanent magnets has little effect on the focusing magnetic field of the PPM system at different temperatures and the temperature stability. The obtained results are beneficial to the design and selection of permanent magnets for PPM focusing system.
基金This work was supported by Natural Science Foundation of China(Item number:51777060,U1361109)Natural Science Foundation of Henan province(Item number:162300410117)the he innovative research team plan of Henan Polytechnic University(Item number:T2015-2).
文摘Aiming at obtaining high power density of surface-mounted and interior permanent magnet synchronous motor(SIPMSM),it is important to accurately calculate the temperature field distribution of SIPMSM,and a magnetic-thermal coupling method is proposed.The magnetic-thermal coupling mechanism is analyzed.The thermal network model and finite element model are built by this method,respectively.The effects of power frequency on iron losses and temperature fields are analyzed by the magnetic-thermal coupling finite element model under the condition of rated load,and the relationship between the load and temperature field is researched under the condition of the synchronous speed.In addition,the equivalent thermal network model is used to verify the magnetic-thermal coupling method.Then the temperatures of various nodes are obtained.The results show that there are advantages in both computational efficiency and accuracy for the proposed coupling method,which can be applied to other permanent magnet motors with complex structures.
基金supported by the National Natural Science Foundation of China under Grant 52077027.
文摘The permanent magnet eddy current coupler(PMEC)solves the problem of flexible connection and speed regulation between the motor and the load and is widely used in electrical transmission systems.It provides torque to the load and generates heat and losses,reducing its energy transfer efficiency.This issue has become an obstacle for PMEC to develop toward a higher power.This paper aims to improve the overall performance of PMEC through multi-objective optimization methods.Firstly,a PMEC modeling method based on the Levenberg-Marquardt back propagation(LMBP)neural network is proposed,aiming at the characteristics of the complex input-output relationship and the strong nonlinearity of PMEC.Then,a novel competition mechanism-based multi-objective particle swarm optimization algorithm(NCMOPSO)is proposed to find the optimal structural parameters of PMEC.Chaotic search and mutation strategies are used to improve the original algorithm,which improves the shortcomings of multi-objective particle swarm optimization(MOPSO),which is too fast to converge into a global optimum,and balances the convergence and diversity of the algorithm.In order to verify the superiority and applicability of the proposed algorithm,it is compared with several popular multi-objective optimization algorithms.Applying them to the optimization model of PMEC,the results show that the proposed algorithm has better comprehensive performance.Finally,a finite element simulation model is established using the optimal structural parameters obtained by the proposed algorithm to verify the optimization results.Compared with the prototype,the optimized PMEC has reduced eddy current losses by 1.7812 kW,increased output torque by 658.5 N·m,and decreased costs by 13%,improving energy transfer efficiency.
