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The lateral vibration of the car body affects the lateral stability of high-speed trains (HSTs), and the aim of this study is to design a good control strategy to decrease the lateral vibration of the car body. First, the Maxwell model of the anti-yaw damper was transformed into a Voigt model by the equivalence method, and a modified 17-degree-of-freedom (DOF) lateral vibration model that includes the anti-yaw damper was established. After the design difficulty and control accuracy of the control strategy were weighed, the lateral and yaw motions of the wheelsets were regarded as disturbances, and a disturbance observer of wheelset motions was designed to simplify the 17-DOF model to a 9-DOF lateral vibration model. Second, a model predictive control (MPC) strategy for HST lateral vibration was designed, and the effects of the key parameters on the lateral vibration acceleration of the HST car body and on the iterative computation time of the MPC strategy were studied. In addition, a conditionally triggered model predictive control (CMPC) strategy was proposed. Last, the control effects of the CMPC strategy under the working conditions of speed change, turnout passage, and aerodynamic load were studied by simulation, which verified the validity of the proposed model and control strategy. [ABSTRACT FROM AUTHOR]

The setting curve and control method of internal temperature in trains are studied in this paper to achieve energy saving control for air conditioning systems. First, a model of interior temperature that considers the heat transmission from infiltration air is established according to the relationship between the equivalent leakage area for the train body and the pressure difference between the internal and external carriage. Second, the interior temperature setting curve, which changes adaptively with the ambient temperature, is established considering the energy consumption of air conditioning systems and passengers' thermal comfort. Finally, an adaptive iterative learning control (AILC) algorithm is proposed and applied to control the interior temperature according to the periodicity of train operation. In this paper, meteorological data from a typical meteorological year are used for simulation analysis. Simulation results show that the AILC algorithm can effectively track the interior temperature setting value and has a better control effect when the train runs on the same line repeatedly. Under the action of the AILC algorithm, the root-mean-square error is reduced to 0.069 °C in the third iteration. Furthermore, the energy saving for a single carriage in a single process reaches up to 1.534 kW·h, thus achieving energy saving control. [ABSTRACT FROM AUTHOR]

For the operational noise of a medium-low speed maglev train, this paper proposes a method to achieve sound source identification based on characteristic frequency extraction and coherence analysis through continuous cross wavelet transform. Field tests were conducted on a main line in China on the operational noise, vibration and traction current of linear induction motor in the medium-low speed maglev train. The proposed method was applied to the test data to analyze the operational noise properties. The characteristic frequencies were extracted from all the three test target signals for coherence analysis through continuous cross wavelet transform, which verifies the transmission characteristics of electromagnetic noise. The coherence spectrum of noise-vibration signal and current-vibration signal showed that the operational noise in the starting and braking stages mainly originates from the high-frequency vibration excited by the high-frequency harmonics in the traction current of the traction motor. [ABSTRACT FROM AUTHOR]

To improve the aerodynamic performance of a high-speed train (HST) and reduce the safety risk of wake movement on platform commuters, trackside workers, and surrounding infrastructure, an active flow control method based on synthetic jets (SJs) is proposed to suppress the wake of the HST. The wake of the HST controlled by synthetic jets with different momentum coefficients is simulated by the improved delayed detached eddy simulation (IDDES) method embedded in ANSYS Fluent. Then, the slipstream velocity, aerodynamic force, velocity field in the wake region are analyzed. The results show that synthetic jets can effectively reduce the amplitude of the slipstream velocity in the wake region, the aerodynamic drag, and the fluctuation of the aerodynamic side force of the tail car. Furthermore, the synthetic jets delay the flow separation on the side of the tail car through periodic ejection and suction, and then attenuate the vortex motion in the wake region. [ABSTRACT FROM AUTHOR]

To explore the relationship between the dynamic model parameters of a stabilizing device coupled system and the quality status of a ballast bed, this study establishes a dynamic model of a frame-stabilizing device-rail sleeper system by combining track coupling dynamics with the operating mechanism of a stabilizing device and by using the lumped parameter method. Moreover, by combining the experimental results and nonlinear least squares identification method based on the trust-region reflective algorithm, the mapping relationship between the model parameters and the lateral resistance of the sleeper is discussed. In addition, the dynamic model is validated by numerical simulation using the fourth-order Runge-Kutta method. Results show that the model has good accuracy, and the acceleration peak error rates between the dynamic model response and the test data are all within 5 %; the lateral resistance of the sleeper is positively and negatively correlated with the transverse stiffness and transverse damping of the ballast bed in the system model, and the correlation coefficients are 0.96 and 0.89, respectively. [ABSTRACT FROM AUTHOR]

Amorphous nano-metal catalysts often exhibit appealing catalytic properties, because the intrinsic linear scaling relationship can be broken. However, accurate control synthesis of amorphous nano-metal catalysts with desired size and morphology is a challenge. In this work, we discover that Cu(0) could be oxidized to amorphous CuxO species by supercritical CO2. The formation process of the amorphous CuxO is elucidated with the aid of machine learning. Based on this finding, a method to prepare Cu nanoparticles with an amorphous shell is proposed by supercritical CO2 treatment followed by electroreduction. The unique feature of this method is that the size of the particles with amorphous shell can be easily controlled because their size depends on that of the original crystal Cu nanoparticles. Moreover, the thickness of the amorphous shell can be easily controlled by CO2 pressure and/or treatment time. The obtained amorphous Cu shell exhibits high selectivity for C2+ products with the Faradaic efficiency of 84% and current density of 320 mA cm−2. Especially, the FE of C2+ oxygenates can reach up to 65.3 %, which is different obviously from the crystalline Cu catalysts. Amorphous metal catalysts often exhibit appealing catalytic properties, however, accurate control synthesis of amorphous nano-metal catalysts is a challenge. Here, the authors report a feasible strategy to prepare the amorphous Cu catalysts by supercritical CO2 treatment followed by electroreduction. The resulted catalyst shows high selectivity towards multi-carbon products for CO2 electroreduction. [ABSTRACT FROM AUTHOR]

Methanol is a highly desirable product of CO2 electroreduction due to its wide array of industrial applications. However, the development of CO2-to-methanol electrocatalysts with high performance is still challenging. Here we report an operationally simple in situ dual doping strategy to construct efficient CO2-to-methanol electrocatalysts. In particular, when using Ag,S-Cu2O/Cu as electrocatalyst, the methanol Faradaic efficiency (FE) could reach 67.4% with a current density as high as 122.7 mA cm−2 in an H-type cell using 1-butyl-3-methylimidazolium tetrafluoroborate/H2O as the electrolyte, while the current density was below 50 mA cm−2 when the FE was greater than 50% over the reported catalysts. Experimental and theoretical studies suggest that the anion S can effectively adjust the electronic structure and morphology of the catalysts in favor of the methanol pathway, whereas the cation Ag suppresses the hydrogen evolution reaction. Their synergistic interactions with host material enhance the selectivity and current density for methanol formation. This work opens a way for designing efficient catalysts for CO2 electroreduction to methanol. Production of methanol from CO2 electroreduction is attractive but achieving high selectivity, current density and stability simultaneously remains a grand challenge. Here, the authors present an in situ dual doping strategy to construct efficient CO2-to-methanol electrocatalysts. [ABSTRACT FROM AUTHOR]

Production of methanol from electrochemical reduction of carbon dioxide is very attractive. However, achieving high Faradaic efficiency with high current density using facile prepared catalysts remains to be a challenge. Herein we report that copper selenide nanocatalysts have outstanding performance for electrochemical reduction of carbon dioxide to methanol, and the current density can be as high as 41.5 mA cm−2 with a Faradaic efficiency of 77.6% at a low overpotential of 285 mV. The copper and selenium in the catalysts cooperate very well for the formation of methanol. The current density is higher than those reported up to date with very high Faradaic efficiency for producing methanol. As far as we know, this is the first work for electrochemical reduction of carbon dioxide using copper selenide as the catalyst. While the conversion of CO2 to valuable, storable chemicals is attractive, there are few inexpensive and abundant catalysts that are also active and selective for liquid fuels. Here, the authors study copper selenide as a high-performing and efficient electrocatalyst for CO2 conversion to methanol. [ABSTRACT FROM AUTHOR]