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This study presents a general approach to identify dominant oscillation modes in bulk power system by using wide-area measurement system. To automatically identify the dominant modes without artificial participation, spectral characteristic of power system oscillation mode is applied to distinguish electromechanical oscillation modes which are calculated by stochastic subspace method, and a proposed mode matching pursuit is adopted to discriminate the dominant modes from the trivial modes, then stepwise-refinement scheme is developed to remove outliers of the dominant modes and the highly accurate dominant modes of identification are obtained. The method is implemented on the dominant modes of China Southern Power Grid which is one of the largest AC/DC paralleling grids in the world. Simulation data and field-measurement data are used to demonstrate high accuracy and better robustness of the dominant modes identification approach.

In this work, we present a theoretical study for the collective oscillation modes, i.e. quadrupole, radial and axial mode, of a mixture of Bose and Fermi superfluids in the crossover from a Bardeen-Cooper-Schrieffer (BCS) superfluid to a molecular Bose-Einstein condensate (BEC) in harmonic trapping potentials with cylindrical symmetry of experimental interest. To this end, we start from the coupled superfluid hydrodynamic equations for the dynamics of Bose-Fermi superfluid mixtures and use the scaling theory that has been developed for a coupled system. The collective oscillation modes of Bose-Fermi superfluid mixtures are found to crucially depend on the overlap integrals of the spatial derivations of density profiles of the Bose and Fermi superfluids at equilibrium. We not only present the explicit expressions for the overlap density integrals, as well as the frequencies of the collective modes provided that the effective Bose-Fermi coupling is weak, but also test the valid regimes of the analytical approximations by numerical calculations in realistic experimental conditions. In the presence of a repulsive Bose-Fermi interaction, we find that the frequencies of the three collective modes of the Bose and Fermi superfluids are all upshifted, and the change speeds of the frequency shifts in the BCS-BEC crossover can characterize the different groundstate phases of the Bose-Fermi superfluid mixtures for different trap geometries.

The presence of oscillation modes in a power system with low damping rates can compromise its proper operation and motivated the development of different damping control projects to improve these damping rates. Central damping controllers using PMU data showed to be able to mitigate oscillation modes of power systems with high damping ratios. However, time delays and vulnerability of communication channels to cyber-attacks can damage the desired operation of this type of damping controller. This paper proposes an optimization problem-based method for designing centralized controllers that are resilient to channel losses and using time delays as variables of the proposed optimization procedure. The bio-inspired algorithms Marine Predators Algorithm, Particle Swarm Optimization and Genetic Algorithms were applied and evaluated in the proposed optimization model. The IEEE 68-bus was used as a test system, and a set of case studies were carried out. The obtained results show that considering time delays as optimization variables can be beneficial to achieve optimal control objectives. Furthermore, the communication failure robustness strategy was effective as observed by the modal analyses and nonlinear time domain simulations of the test system under contingencies.

An economical method for numerical simulation of self-excitation of gas oscillations in low-emission combustors of gas turbine plants is developed and tested. The method is based on using an SAS SST   turbulence model and a turbulent combustion model with a modified equation for a variable degree of combustion completion. Simulating the self-excitation of gas oscillations requires that a factor associated with gas pressure oscillations is introduced into the source term of this equation. Isolation of one of the gas oscillation modes prone to self-excitation is carried out using a resonant filter operating in each cell of the computational domain. The computational results obtained using the proposed method make it possible to study the effect of design measures and operating parameters of self-oscillations and to approach the choice of measures to suppress them.

Based on a reduced model of a linear section of a steel gas pipeline between four supports and with a crack-like through defect, ANSYS FE software is used in this study to develop numerical approaches regarding three key parameters of a composite bandage in the form of a circular lining: the type of composite material and the length and thickness of the composite lining. The approach for assessing the static strength of a damaged section of a steel pipeline with a composite lining that is subjected to internal pressure allows for the determination of the optimal thickness of the composite lining itself, which is equal to the indicator “50.0% to 62.5%” of the pipe thickness. Furthermore, the approach for assessing the dynamic strength and analyzing the possible destruction of the reinforced damaged section of a pipeline experiencing an increase in internal pressure allows for the determination of the optimal length of the composite lining, which, in turn, should be at least 241.2 mm. This work also considers cases when there is no internal pressure and the steel pipeline is subjected to critical pressure. It is found that the frequency spectrum of pipeline oscillations without a composite lining is higher than that with a composite lining. The difference between the corresponding dynamic oscillations increases with the thickness or the length of the composite lining. In the absence of internal pressure, all frequencies of the steel pipeline with a crack closed by a composite lining are paired. This pairing is disrupted when the pipeline is subjected to critical internal pressure, and the difference between its oscillation frequency spectrum without and with a composite lining increases. In this case, the oscillation modes significantly differ from those of the same pipeline structure when unloaded. The results ensure the optimal stress distribution in the defect area of a steel pipeline wall and improve the reliability and safety of pipelines under seismic actions. The approach for increasing dynamic strength and eliminating defects can be applied to pipelines with a large diameter regardless of the causes and geometric dimensions of the defects. Moreover, this approach to increasing the strength can be used by various industries and/or institutes which work on the design of new, earthquake-resistant, reinforced pipelines.

Modern measurement systems have allowed the recording of ambient data containing oscillatory phenomena and high noise levels. The observed phenomena are associated with ambient oscillations, which can be identified for monitoring the power system’s dynamic behavior. Therefore, a cross-correlation decomposition-based approach is proposed in this paper. This technique combines an extended natural excitation technique (ENEXT) with a parallel factor (PARAFAC) analysis. The ENEXT allows calculating correlation functions of the measured data, which can be employed to build a data tensor. From this data array, a set of dominant impulse responses and their spatial correlations can be extracted through the PARAFAC analysis. The cross-correlation decomposition-based approach is applied to ambient data from the IEEE 16-generator 5-area power system to characterize dominant inter-area oscillation modes. The results demonstrate that the impulse responses may be used to identify the modal parameters, and the spatial correlations may be used to identify the spatial distribution of the oscillatory modes.

The oscillation mode of an atomic force microscope (AFM) was used to create a highly sensitive real-time detection system for antibiotic resistance. This mode allows one to evaluate the sensitivity or resistance of Gram-negative ( Escherichia coli ) and Gram-positive ( Staphylococcus aureus ) bacteria to an antibiotic in 15–30 minutes. The analytical signal (changes in the amplitude-frequency characteristics of the cantilever) is based on the metabolic activity of bacteria. Bacteria were placed on the cantilever and causing it to oscillate with high amplitude. If the bacteria are sensitive to the antibiotic, the amplitude drops statistically significant within 15–30 minutes, if the bacteria are resistant, then the amplitude either does not change or increases. The results were comparable with the disk diffusion method.

The results of rheological measurements of a composition with two gelling components obtained in rotational mode and oscillation mode at different temperatures are presented. The kinetics of gel formation was characterized, and the strength of the formed structure was assessed. The gelation points and elastic moduli of the gel were determined.

Self-oscillating systems that enable autonomous, continuous motions driven by an unchanging, constant stimulus would have significant applications in intelligent machines, advanced robotics, and biomedical devices. Despite efforts to gain self-oscillations have been made through artificial systems using responsive soft materials of gels or liquid crystal polymers, these systems are plagued with problems that restrict their practical applicability: few available oscillation modes due to limited degrees of freedom, inability to control the evolution between different modes, and failure under loading. Here we create a phototunable self-oscillating system that possesses a broad range of oscillation modes, controllable evolution between diverse modes, and loading capability. This self-oscillating system is driven by a photoactive self-winding fiber actuator designed and prepared through a twistless strategy inspired by the helix formation of plant-tendrils, which endows the system with high degrees of freedom. It enables not only controllable generation of three basic self-oscillations but also production of diverse complex oscillatory motions. Moreover, it can work continuously over 1270000 cycles without obvious fatigue, exhibiting high robustness. We envision that this system with controllable self-oscillations, loading capability, and mechanical robustness will be useful in autonomous, self-sustained machines and devices with the core feature of photo-mechanical transduction. Self-oscillating systems that enable autonomous motions driven by a constant stimulus find applications in numerous fields but these systems are plagued with problems that restrict their practical applicability. Here, the authors create a photoactive self-winding fiber actuator that possesses a broad range of oscillation modes, controllable evolution between diverse modes, and loading capability.

A data‐driven dynamical filter is developed to characterize Madden‐Julian Oscillation (MJO) variability, by representing tropical variability with nonorthogonal empirical‐dynamical modes that allow for constructive and destructive interference. We find that two intraseasonal atmospheric modes, an “MJO‐fast” mode (∼ ${\\sim} $45 day period) and a newly identified “MJO‐slow” mode (∼ ${\\sim} $70 day period), alongside El Niño‐Southern Oscillation modes that are not entirely removed by temporal filtering, explain nearly all observed Real‐time Multivariate MJO (RMM) index‐based variability. The fastest growing, and most predictable, MJO events are initiated primarily by the MJO‐fast mode over the Indian Ocean, with subsequent progression across the Maritime Continent resulting from destructive and then constructive interference of the MJO‐fast and MJO‐slow modes. These events, which we demonstrate can be identified at forecast initialization time, are shown to be forecasts of opportunity in the ECMWF operational forecast model, with MJO skill extended by roughly a week compared to all other forecasts.