Vol. 5, No. 1 (2021): Regular Issue

This paper describes a methodology for implementing an identification algorithm for an atomic force microscope to estimate a surface profile of a sample. A microbeam of the atomic force microscope is modeled as an Euler-Bernoulli beam. A tip-sample interaction force used here is a piecewise function described by the attractive van der Waals force and the repulsive Derjaguin-Muller-Toporov force which can represent the indentation made by a tip of the microbeam into the sample surface. The identification of the instantaneous gap between the tip of the undeformed microbeam and the sample surface is implemented on line based on a trained input-output recurrent network. With the measured cantilevered-beam-tip motion supplied to the recurrent network, the recurrent network is going to compute the estimated gap. The approach being used in this study is in contrast to the conventional approach in that it is based on the signal in the time domain instead of in the frequency domain; therefore, the number of steps involved is fewer. When comparing the proposed approach with the adaptive-tracking controllers based on adaptive control Lyapunov functions, the proposed approach outperforms the adaptive-tracking controllers significantly. Whereas the adaptive-tracking controllers require much more measured signals to supply to the controllers and the parameter estimator. Instead, the proposed approach has to store two units in the tapped-delay-line memory while the identification algorithm is running.

This study is concerned with the dynamic response of fire doors in cross-passages of subway tunnels. Fire doors in cross-passages of subway tunnels are prone to failure due to the piston effect. First, a full-size model of a section of tunnel in Shenyang Metro was established. Then, computational fluid dynamics (CFD) was used to simulate unsteady airflow under different train speeds. Pressure on the surface of the fire door was obtained and the pressure was set as the excitation on the finite element model of the fire door. Finally, the structural response of the fire door was obtained through explicit dynamics analysis. Maximum stress on the fire door structure was found to be 10.05 MPa. Consistent results were obtained with the actual failure warping deformation, it can be considered that the piston wind is the main factor leading to the warping deformation of the fire door. The findings provide guidance for the installation and maintenance of fire doors in metro tunnels.

In this paper, limit cycles in a discontinuous dynamical system with different vector fields in different domains are constructed. Such limit cycles are obtained through algebraic equations based on specific mapping structures. The stability and bifurcations of limit cycles are studied through eigenvalue analysis. The grazing and sliding bifurcations on the bifurcation trees are presented. Once a grazing or sliding bifurcation occurs, a limit cycle switches to a new limit cycle or vanishes. Such bifurcations (i.e., saddle-node bifurcation, Neimark bifurcation, period-doubling bifurcation, grazing bifurcation, and sliding bifurcation) are for onset and vanishing of limit cycles at specific parameters. This study presents how to construct limit cycles in discontinuous dynamical systems and how to develop the corresponding mathematical conditions of motion switchability at boundaries.

A fuel injector plays an important role in performance and emissions of diesel engines. Among other factors, the quantity of fuel injected into the combustion cylinder is dictated by the nozzle seat and sac geometry, spray hole size and K factor, and the needle lift. These factors can affect the timing of the needle closing, potential bounce and transverse motion of the needle. In order to optimize needle closing, the motion of the needle valve needs to be understood. For this purpose, a mini multilayer conical inductance coil was designed to measure needle valve motion as it is actuated in a nozzle body, with the nozzle being the core of the coil. This approach minimizes intrusion into the injector assembly which can pose a risk for leakage.

In order to improve the accuracy of internal and external fault identification algorithms for T-connected transmission lines, a new method for fault identification of T-connected transmission line based on the probabilistic neural network and the angle of wave impedance is proposed. Extract the initial voltage and current phasor information of each traveling wave protection unit at a specific frequency based on the S transform, then calculate the included angle of the measured wave impedance at this frequency, and use this to form a T-connection transmission line fault characteristic sample set. The T-connection transmission line fault feature training sample set is input into the probabilistic neural network training and testing to establish a fault recognition model to identify the fault. Simulation results show that the proposed algorithm can accurately identify the internal and external faults of the T-connection transmission line and the specific branch roads outside the zone.

A general framework applicable for characterizing dynamic complex networks is presented. The framework 1) incorporates a revised Kuramoto model to define constituent dynamics, 2) explores information entropy for the description of global ensemble behaviors, 3) defines the variation of the state of connected constituents using energy, and 4) introduces two new time-dependent parameters, i.e., degrees of coupling, to delineate the extent to which the state of one constituent impacts the other. Information entropy which defines the randomness of constituent energy at the microscopic level provides a definitive measure for the ensemble dynamics at the macroscopic level. Whether a dynamic complex network is evolving toward synchronization or deteriorating and collapsing can be determined by tracking ensemble entropy in time. Two popular topological network structures are examined under the framework for their respective network responses. It is found that, in addition to misrepresenting the true network dynamics, static network structures do not differentiate themselves in resolving network properties such as average path length and degree distribution, thus rendering similar interpretations for the underlying network.