As the size of satellites scales down, low-power and compact propulsion systems such as the pulsed plasma thruster(PPT) are needed for stabilizing these miniature satellites in orbit. Most PPT systems are operated at ...As the size of satellites scales down, low-power and compact propulsion systems such as the pulsed plasma thruster(PPT) are needed for stabilizing these miniature satellites in orbit. Most PPT systems are operated at 2 J or more of discharge energy. In this work, the performance of a PPT with a side-fed, tongue-flared electrode configuration operated within a lower discharge energy range of 0.5-2.5 J has been investigated. Ablation and charring of the polytetrafluoroethylene propellant surface were analyzed through field-effect scanning electron microscopy imaging and energy-dispersive X-ray spectroscopy. When the discharge energy fell below 2 J, inconsistencies occurred in the specific impulse and the thrust efficiency due to the measurement of the low mass bit. At energy ≥2 J, the performance parameters are compared with other PPT systems of similar configuration and discussed in depth.展开更多
A new model of a quantum refrigeration cycle composed of two adiabatic and two isomagnetic field processes is established. The working substance in the cycle consists of many non-interacting spin-1/2 systems. The perf...A new model of a quantum refrigeration cycle composed of two adiabatic and two isomagnetic field processes is established. The working substance in the cycle consists of many non-interacting spin-1/2 systems. The performance of the cycle is investigated, based on the quantum master equation and semi-group approach. The general expressions of several important performance parameters, such as the coefficient of performance, cooling rate, and power input, are given. It is found that the coefficient of performance of this cycle is in the closest analogy to that of the classical Carnot cycle. Furthermore, at high temperatures the optimal relations of the cooling rate and the maximum cooling rate are analysed in detail. Some performance characteristic curves of the cycle are plotted, such as the cooling rate versus the maximum ratio between high and low ＂temperatures＂ of the working substances, the maximum cooling rate versus the ratio between high and low ＂magnetic fields＂ and the ＂temperature＂ ratio between high and low reservoirs. The obtained results are further generalized and discussed, so that they may be directly applied to describing the performance of the quantum refrigerator using spin-J systems as the working substance. Finally, the optimum characteristics of the quantum Carnot and Ericsson refrigeration cycles are derived by analogy.展开更多
This paper establishes the energy selective electron （ESE） engine with double resonances as a refrigerator in one dimensional （1D） system. It consists of two infinitely large electron reservoirs with different tem...This paper establishes the energy selective electron （ESE） engine with double resonances as a refrigerator in one dimensional （1D） system. It consists of two infinitely large electron reservoirs with different temperatures and chemical potentials, and they are perfectly thermally insulated from each other and interaction only via a double ＇idealized energy filter＇ whose widths are all finite. Taking advantage of the density of state and Fermi distribution in the 1D system, the heat flux into each reservoir may then be calculated. Moreover, the coefficient of performance may be derived from the expressions for the heat flux into the hot and cold reservoirs. The performance characteristic curves are plotted by numerical analysis. The influences of the resonances widths, the energy position of resonance and the space of two resonances on performance of the ESE refrigerator are discussed. The results obtained here have theoretical significance for the understanding of thermodynamic performance of the micro-nano devices.展开更多
Performance parameter prediction technology is the core research content of aeroengine health management,and more and more machine learning algorithms have been applied in the field.Regularized extreme learning machin...Performance parameter prediction technology is the core research content of aeroengine health management,and more and more machine learning algorithms have been applied in the field.Regularized extreme learning machine(RELM)is one of them.However,the regularization parameter determination of RELM consumes computational resources,which makes it unsuitable in the field of aeroengine performance parameter prediction with a large amount of data.This paper uses the forward and backward segmentation(FBS)algorithms to improve the RELM performance,and introduces an adaptive step size determination method and an improved solution mechanism to obtain a new machine learning algorithm.While maintaining good generalization,the new algorithm is not sensitive to regularization parameters,which greatly saves computing resources.The experimental results on the public data sets prove the above conclusions.Finally,the new algorithm is applied to the prediction of aero-engine performance parameters,and the excellent prediction performance is achieved.展开更多
基金supported by the Ministry of Science,Technology and Innovation,Malaysia(MOSTI)(No.04-02-12-SF0339)。
文摘As the size of satellites scales down, low-power and compact propulsion systems such as the pulsed plasma thruster(PPT) are needed for stabilizing these miniature satellites in orbit. Most PPT systems are operated at 2 J or more of discharge energy. In this work, the performance of a PPT with a side-fed, tongue-flared electrode configuration operated within a lower discharge energy range of 0.5-2.5 J has been investigated. Ablation and charring of the polytetrafluoroethylene propellant surface were analyzed through field-effect scanning electron microscopy imaging and energy-dispersive X-ray spectroscopy. When the discharge energy fell below 2 J, inconsistencies occurred in the specific impulse and the thrust efficiency due to the measurement of the low mass bit. At energy ≥2 J, the performance parameters are compared with other PPT systems of similar configuration and discussed in depth.
基金Project supported by the National Natural Science Foundation of China （Grant No 10465003） and the Natural Science Foundation of Jiangxi Province, China （Grant No 0412011）.
文摘A new model of a quantum refrigeration cycle composed of two adiabatic and two isomagnetic field processes is established. The working substance in the cycle consists of many non-interacting spin-1/2 systems. The performance of the cycle is investigated, based on the quantum master equation and semi-group approach. The general expressions of several important performance parameters, such as the coefficient of performance, cooling rate, and power input, are given. It is found that the coefficient of performance of this cycle is in the closest analogy to that of the classical Carnot cycle. Furthermore, at high temperatures the optimal relations of the cooling rate and the maximum cooling rate are analysed in detail. Some performance characteristic curves of the cycle are plotted, such as the cooling rate versus the maximum ratio between high and low ＂temperatures＂ of the working substances, the maximum cooling rate versus the ratio between high and low ＂magnetic fields＂ and the ＂temperature＂ ratio between high and low reservoirs. The obtained results are further generalized and discussed, so that they may be directly applied to describing the performance of the quantum refrigerator using spin-J systems as the working substance. Finally, the optimum characteristics of the quantum Carnot and Ericsson refrigeration cycles are derived by analogy.
基金supported by National Natural Science Foundation of China (Grant No 10765004)Science and Technology Foundation of Jiangxi Education Bureau,China
文摘This paper establishes the energy selective electron （ESE） engine with double resonances as a refrigerator in one dimensional （1D） system. It consists of two infinitely large electron reservoirs with different temperatures and chemical potentials, and they are perfectly thermally insulated from each other and interaction only via a double ＇idealized energy filter＇ whose widths are all finite. Taking advantage of the density of state and Fermi distribution in the 1D system, the heat flux into each reservoir may then be calculated. Moreover, the coefficient of performance may be derived from the expressions for the heat flux into the hot and cold reservoirs. The performance characteristic curves are plotted by numerical analysis. The influences of the resonances widths, the energy position of resonance and the space of two resonances on performance of the ESE refrigerator are discussed. The results obtained here have theoretical significance for the understanding of thermodynamic performance of the micro-nano devices.
文摘Performance parameter prediction technology is the core research content of aeroengine health management,and more and more machine learning algorithms have been applied in the field.Regularized extreme learning machine(RELM)is one of them.However,the regularization parameter determination of RELM consumes computational resources,which makes it unsuitable in the field of aeroengine performance parameter prediction with a large amount of data.This paper uses the forward and backward segmentation(FBS)algorithms to improve the RELM performance,and introduces an adaptive step size determination method and an improved solution mechanism to obtain a new machine learning algorithm.While maintaining good generalization,the new algorithm is not sensitive to regularization parameters,which greatly saves computing resources.The experimental results on the public data sets prove the above conclusions.Finally,the new algorithm is applied to the prediction of aero-engine performance parameters,and the excellent prediction performance is achieved.
