Publications by authors named "Chunfa Zhao"

The time delay (TD) in the levitation control system significantly affects the dynamic performance of the closed-loop system in electromagnetic suspension (EMS) maglev vehicles. Excessive TD can cause levitation instability, making it essential to explore effective mitigation methods. To address this issue, a Smith Predictor (SP) is integrated into the traditional PID levitation control system.

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The elastic deformation of the levitation electromagnet (LM) of the high-speed maglev vehicle brings uneven levitation gaps and displacement differences between measured gap signals and the real gap in the middle of the LM, and then reduces dynamic performances of the electromagnetic levitation unit. However, most of the published literature has paid little attention to the dynamic deformation of the LM under complex line conditions. In this paper, considering the flexibility of the LM and the levitation bogie, a rigid-flexible coupled dynamic model is established to simulate deformation behaviors of the LMs of the maglev vehicle passing through the 650 m radius horizontal curve.

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The traditional electromagnetic force calculation method does not consider the non-linear magnetization characteristics of the ferromagnetic material or the magnetic resistance in full circuit, resulting in large calculation errors when the electromagnet operation state is far from the rated state, and causing the dynamics simulation results to diverge from the actual situation. A more accurate analytical formula for electromagnetic force is derived based on the full circuit magnetic resistance modification and considering the non-linear magnetization characteristics of ferromagnetic materials. Then combined with the finite element simulation analysis, the magnetic resistance modification (MRM) method is proposed for calculating electromagnetic levitation force and guiding force.

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Clarifying the current situation of regional water pollutants and the relationship between pollutants and pollution sources is considered essential for managing the water environment. Water quality identification index (WQI), cluster analysis (CA), positive matrix factorization (PMF), and stable isotope analysis in R (SIAR) were employed to interpret a large and complex water quality data set of the Qinhuai River catchment generated during 2015 to 2019 to monitor of 11 parameters at 29 different sampling sites. WQI analysis indicated that water quality in Qinhuai River catchment is considered to have "moderate pollution," and an improving trend of water quality was observed at the interannual scale.

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