Load forecasting of refrigerated display cabinet based on CEEMD–IPSO–LSTM combined model

The load of the showcase is a nonlinear and unstable time series data, and the traditional forecasting method is not applicable. Deep learning algorithms are introduced to predict the load of the showcase. Based on the CEEMD–IPSO–LSTM combination algorithm, this paper builds a refrigerated display cabinet load forecasting model. Compared with the forecast results of other models, it finally proves that the CEEMD–IPSO–LSTM model has the highest load forecasting accuracy, and the model’s determination coefficient is 0.9105, which is obviously excellent. Compared with other models, the model constructed in this paper can predict the load of showcases, which can provide a reference for energy saving and consumption reduction of display cabinet.

Frequency synchronization detection method based on adaptive frequency standard tracking

In order to achieve wide-band high-resolution frequency measurement, a frequency synchronization detection method based on adaptive frequency standard tracking is proposed based on the quantized phase processing. First, the nominal value of the measured frequency signal was obtained from the rough frequency measurement module. Then, the field programmable gate array generated the nominal value of the measured frequency. After that, the direct comparison between the tracking frequency and the measured signal was carried out. Finally, the group quantized processing module gave the final result according to the phase full-period change time. Experimental results showed that the method has a wide frequency measurement range and high accuracy and can obtain frequency stability of the order of 10−13/s.

The diffusion mechanism of the application of intelligent manufacturing in SMEs model based on cellular automata

Based on the characteristics of intelligent manufacturing and the theory of technology diffusion, this paper constructs a cellular automata model with government support policy, information exchange, technology maturity, diffusion intermediary, and market competition as the influencing factors and analyzes the influence mechanism of the first three main factors on the diffusion of intelligent manufacturing technology in industrial clusters using MATLAB. This paper also makes an empirical analysis of the diffusion of intelligent manufacturing technology in the bearing industry cluster in Xinchang County and finds that the results are basically consistent by comparing the simulation data with the fit degree of the real data. In this paper, the diffusion intermediary and government support policy have the greatest influence on the application of intelligent manufacturing in small- and medium-sized enterprises, and the model proposed in this paper is effective.

Study of dynamic pressure on the packer for deep-water perforation

The packer is one of the most important tools in deep-water perforation combined well testing, and its safety directly determines the success of perforation test operations. The study of dynamic perforating pressure on the packer is one of the key technical problems in the production of deep-water wells. However, there are few studies on the safety of packers with shock loads. In this article, the three-dimensional finite element models of downhole perforation have been established, and a series of numerical simulations are carried out by using orthogonal design. The relationship between the perforating peak pressure on the packer with the factors such as perforating charge quantity, wellbore pressure, perforating explosion volume, formation pressure, and elastic modulus is established. Meanwhile, the database is established based on the results of numerical simulation, and the calculation model of peak pressure on the packer during perforating is obtained by considering the reflection and transmission of shock waves on the packer. The results of this study have been applied in the field case of deep-water well, and the safety optimization program for deep-water downhole perforation safety has been put forward. This study provides important theoretical guidance for the safety of the packer during deep-water perforating.

Circular Rydberg states of helium atoms or helium-like ions in a high-frequency laser field

In the literature, there were studies of Rydberg states of hydrogenic atoms/ions in a high-frequency laser field. It was shown that the motion of the Rydberg electron is analogous to the motion of a satellite around an oblate planet (for a linearly polarized laser field) or around a (fictitious) prolate planet (for a circularly polarized laser field): it exhibits two kinds of precession – one of them is the precession within the orbital plane and another one is the precession of the orbital plane. In this study, we study a helium atom or a helium-like ion with one of the two electrons in a Rydberg state, the system being under a high-frequency laser field. For obtaining analytical results, we use the generalized method of the effective potentials. We find two primary effects of the high-frequency laser field on circular Rydberg states. The first effect is the precession of the orbital plane of the Rydberg electron. We calculate analytically the precession frequency and show that it differs from the case of a hydrogenic atom/ion. In the radiation spectrum, this precession would manifest as satellites separated from the spectral line at the Kepler frequency by multiples of the precession frequency. The second effect is a shift of the energy of the Rydberg electron, also calculated analytically. We find that the absolute value of the shift increases monotonically as the unperturbed binding energy of the Rydberg electron increases. We also find that the shift has a nonmonotonic dependence on the nuclear charge Z: as Z increases, the absolute value of the shift first increases, then reaches a maximum, and then decreases. The nonmonotonic dependence of the laser field-caused energy shift on the nuclear charge is a counterintuitive result.

Effect of buried depth on thermal performance of a vertical U-tube underground heat exchanger

Many researchers numerically investigated U-tube underground heat exchanger using a two-dimensional simplified pipe. However, a simplified model results in large errors compared to the data from construction sites. This research is carried out using a three-dimensional full-size model. A model validation is conducted by comparing with experimental data in summer. This article investigates the effects of fluid velocity and buried depth on the heat exchange rate in a vertical U-tube underground heat exchanger based on fluid–structure coupled simulations. Compared with the results at a flow rate of 0.4 m/s, the results of this research show that the heat transfer per buried depth at 1.0 m/s increases by 123.34%. With the increase of the buried depth from 80 to 140 m, the heat transfer per unit depth decreases by 9.72%.

A facile fabrication of superhydrophobic and superoleophilic adsorption material 5A zeolite for oil–water separation with potential use in floating oil

A facile method for fabricating superhydrophobic and superoleophilic powder with 5A zeolite and stearic acid (SA) is reported in this study. The effect of different contents of SA on contact angle (CA) was investigated. The maximum water CA was 156.2°, corresponding to the optimum SA content of 1.5 wt%. The effects of SA and the mechanism of modified 5A zeolite powder by SA were analyzed by sedimentation analysis experiment, FTIR analysis, particle size analysis, and SEM characterization. The SA-modified 5A zeolite was used as an oil sorbent to separate oil–water mixture with potential use in floating oil. The separation efficiency was above 98%.

Research on maintenance spare parts requirement prediction based on LSTM recurrent neural network

The aim of this study was to improve the low accuracy of equipment spare parts requirement predicting, which affects the quality and efficiency of maintenance support, based on the summary and analysis of the existing spare parts requirement predicting research. This article introduces the current latest popular long short-term memory (LSTM) algorithm which has the best effect on time series data processing to equipment spare parts requirement predicting, according to the time series characteristics of spare parts consumption data. A method for predicting the requirement for maintenance spare parts based on the LSTM recurrent neural network is proposed, and the network structure is designed in detail, the realization of network training and network prediction is given. The advantages of particle swarm algorithm are introduced to optimize the network parameters, and actual data of three types of equipment spare parts consumption are used for experiments. The performance comparison of predictive models such as BP neural network, generalized regression neural network, wavelet neural network, and squeeze-and-excitation network prove that the new method is effective and provides an effective method for scientifically predicting the requirement for maintenance spare parts and improving the quality of equipment maintenance.

Hopf bifurcation analysis for liquid-filled Gyrostat chaotic system and design of a novel technique to control slosh in spacecrafts

In this article, a fuel tank is coupled with gyrostat in a moving spacecraft to discuss its dynamical behaviour and bringing stability in velocity vectors. Parametric study is performed using Hopf bifurcation to find the bifurcation parameter for a considered mechanical model. Furthermore, a region is constructed in which negligible limit cycles appear around unstable spirals for angular momentum greater than bifurcation point. Based on local dynamical analysis, trajectories of angular velocities are observed with respect to damping constant, which is formulated in the form of bifurcation parameter. Moreover, a controller is designed in this article for considered dynamical system by achieving global stability, with the help of Lyapunov theory, into the spacecraft coupled with filled fuel tank, and their results are compared with effective spacecraft control strategies to observe the effectiveness of our proposed control technique. Finally, in presented research, numerical simulations are performed using MATLAB for validation of analytical results, which the authors have achieved for Hopf bifurcation and designed controller.

Fractional modeling of COVID-19 epidemic model with harmonic mean type incidence rate

Coronavirus disease 2019 (COVID-19) is a disease caused by severe acute respiratory syndrome coronavirus 2 (SARS CoV-2). It was declared on March 11, 2020, by the World Health Organization as a pandemic disease. Regrettably, the spread of the virus and mortality due to COVID-19 have continued to increase daily. The study is performed using the Atangana–Baleanu–Caputo operator with a harmonic mean type incidence rate. The existence and uniqueness of the solutions of the fractional COVID-19 epidemic model have been developed using the fixed point theory approach. Along with stability analysis, all the basic properties of the given model are studied. To highlight the most sensitive parameter corresponding to the basic reproductive number, sensitivity analysis is taken into account. Simulations are conducted using the first-order convergent numerical approach to determine how parameter changes influence the system’s dynamic behavior.