This study considers a superconducting electrodynamic maglev train of MLX01 type.The characteristics of the electromagnetic spring coefficient of a single bogie under different magnetomotive force(MF) of the supercond...This study considers a superconducting electrodynamic maglev train of MLX01 type.The characteristics of the electromagnetic spring coefficient of a single bogie under different magnetomotive force(MF) of the superconducting coil and standard air gap(Sag) were explored.In view of the small electromagnetic damping,a passive damping control strategy and an active damping control strategy were designed to increase the electromagnetic damping force between the superconducting coil and ground coil.Combined with the coupling numerical model of a single bogie,the vibration characteristics of the bogie in different directions with different damping control strategies were studied when the Sag and MF were fixed.The results can provide important theoretical support for stable operation control of maglev trains.展开更多
The digital twin-driven performance model provides an attractive option for the warn gas-path faults of the gas turbines.However,three technical difficulties need to be solved:(1)low modeling precision caused by indiv...The digital twin-driven performance model provides an attractive option for the warn gas-path faults of the gas turbines.However,three technical difficulties need to be solved:(1)low modeling precision caused by individual differences between gas turbines,(2)poor solution efficiency due to excessive iterations,and(3)the false alarm and missing alarm brought by the traditional fixed threshold method.This paper proposes a digital twin model-based early warning method for gas-path faults that breaks through the above obstacles from three aspects.Firstly,a novel performance modeling strategy is proposed to make the simulation effect close to the actual gas turbine by fusing the mechanism model and measurement data.Secondly,the idea of controlling the relative accuracy of model parameters is developed.The introduction of an error module to the existing model can greatly shorten the modeling cycle.The third solution focuses on the early warning based on the digital twin model,which self-learns the alarm threshold of the warning feature of gas-path parameters using the kernel density estimation.The proposed method is utilized to analyze actual measured data of LM2500+,and the results verify that the new-built digital model has higher accuracy and better efficiency.The comparisons show that the proposed method shows evident superiority in early warning of performance faults for gas turbines over other methods.展开更多
文摘This study considers a superconducting electrodynamic maglev train of MLX01 type.The characteristics of the electromagnetic spring coefficient of a single bogie under different magnetomotive force(MF) of the superconducting coil and standard air gap(Sag) were explored.In view of the small electromagnetic damping,a passive damping control strategy and an active damping control strategy were designed to increase the electromagnetic damping force between the superconducting coil and ground coil.Combined with the coupling numerical model of a single bogie,the vibration characteristics of the bogie in different directions with different damping control strategies were studied when the Sag and MF were fixed.The results can provide important theoretical support for stable operation control of maglev trains.
基金co-supported by the National Postdoctoral Program for Innovative Talent(No.BX20180031)。
文摘The digital twin-driven performance model provides an attractive option for the warn gas-path faults of the gas turbines.However,three technical difficulties need to be solved:(1)low modeling precision caused by individual differences between gas turbines,(2)poor solution efficiency due to excessive iterations,and(3)the false alarm and missing alarm brought by the traditional fixed threshold method.This paper proposes a digital twin model-based early warning method for gas-path faults that breaks through the above obstacles from three aspects.Firstly,a novel performance modeling strategy is proposed to make the simulation effect close to the actual gas turbine by fusing the mechanism model and measurement data.Secondly,the idea of controlling the relative accuracy of model parameters is developed.The introduction of an error module to the existing model can greatly shorten the modeling cycle.The third solution focuses on the early warning based on the digital twin model,which self-learns the alarm threshold of the warning feature of gas-path parameters using the kernel density estimation.The proposed method is utilized to analyze actual measured data of LM2500+,and the results verify that the new-built digital model has higher accuracy and better efficiency.The comparisons show that the proposed method shows evident superiority in early warning of performance faults for gas turbines over other methods.
