The Study on the Shock Wave Propagation Rule of a Gas Explosion in a Gas Compartment

Combined with the k-ε turbulence model of general application, a refined finite element model of a utility tunnel’s gas compartment filled with the methane/air mixture is developed. A series of analyses are made by using the powerful industry-leading computational fluid dynamics (CFD) software flame acceleration simulator (FLACS) to study the shock wave propagation rule in the gas compartment. The longitudinal and transversal distribution laws of the explosion shock wave are gained taking into consideration the spatial characteristics of the gas compartment. The influences of a few parameters, such as initial conditions and section size of the gas compartment, on the shock wave propagation rule are further discussed. The basic procedure for predicting the peak pressure of the blast wave is provided by considering the initial conditions and the gas compartment, and the corresponding injury effect of the explosion wave on the living beings is assessed. The investigation demonstrates that the peak pressure by the coupled effect between the initial conditions is significantly influenced, especially at the upper and lower gas explosion limits. The peak pressure increases gradually as the width or height increases, and both basically meet the linear relation. The proposed method can forecast the peak pressure of the explosion shock wave in the gas compartment accurately. According to the peak pressure longitudinal and transversal distributions of the blast wave, the peak pressure is far greater than the killing pressure threshold in the underground and closed space; consequently, it is not safe for the living beings in the gas compartment.

Online public opinion evaluation through the functional resonance analysis method and deep analysis

A conventional model of public opinion analysis is no longer suitable when the internet is the primary arena of information dissemination. Thus, a more practical approach is urgently needed to deal with this dynamic and complicated phenomenon of propagating public opinion. This paper proposes that the outbreak of internet public opinion and its negative impacts, such as the occurrence of major security incidents, are a result of coupling and the complex interaction of many factors. The Functional Resonance Analysis Method model is composed of those factors and considers the stages of network information dissemination, the unique propagation rule, and textual sentiment resonance on the internet. Moreover, it is the first public opinion governance method that simultaneously highlights the complex system, functional identification, and functional resonance. It suggests a more effective method to shorten the dissipation time of negative public opinion and is a considerable improvement over previous models for risk-prediction. Based on resonance theory and deep learning, this study establishes public opinion resonance functions, which made it possible to analyze public opinion triggers and build a simulation model to explore the patterns of public opinion development through long-term data capture. The simulation results of the Functional Resonance Analysis Method suggest that the resonance in the model is consistent with the evolution of public opinion in real situations and that the components of the resonance of public opinion can be separated into eleven subjective factors and three objective factors. In addition, managing the subjective factors can significantly accelerate the dissipation of negative opinions.

Research on the influence of bubble curtains on shock wave fields of the underwater explosion

Shock waves, which derive from explosions, generate reflected and refracted waves when propagating in the layered medium with various acoustic stiffness. Depending on the acoustic characteristic of each layer of the medium, properties of reflected and refracted waves will increase or decrease pressures/stresses at the investigated point of medium, compared to influences of explosive shock waves (incident waves) propagated in a homogeneous and isotropic medium. Based on this mechanical physical property, scientists have studied a diversity of solutions decreasing effects of explosive shock waves in various medium such as rock and soil, water, air. However, currently there have not been any comprehensive theoretical studies on the reduction in intensity of the underwater explosion shock wave when interacting with bubble curtain. By using the analytical method and the virtual explosive method, the paper presents the propagation rule of new waves formed when the underwater explosion shock wave interacts with the bubble curtain. The results showed that the more the thickness of the bubble curtain or the higher the bubble content or the longer the distance from the explosive to the curtain, the weaker the intensity of the shock wave when passing through the curtain.

Research of Near-Wellbore Fracture Morphology, Formation Mechanism, and Propagation Law for Different Perforation Modes During the Perforation Process

Perforation plays an important role in the fracture morphology near the wellbore and the propagation of hydraulic fracturing fractures. Therefore, it is of great significance to find out the fracture morphology and propagation law during perforation for optimizing perforation technology, enhancing fracture control, and realizing complementary advantages of different perforation schemes. Based on analyzing the characteristics of perforation fracturing at each stage and existing perforation technology, two types of deep-penetrating perforating bullets were used to carry out large-scale perforation shooting experiments. The real processes of spiral perforation, directional perforation, conventional fixed-plane perforation, and interlaced fixed-plane perforation were simulated, respectively. The near-wellbore fracture morphology, formation mechanism, and propagation rule during perforation with different perforation modes were analyzed. The results show that (1) perforation is accompanied by the formation of tunnels, and there are three kinds of source microfractures developed around the tunnels, namely Type I radial microfractures, Type II oblique microfractures, and Type III perforation tip divergent microfractures. The three microfractures are interconnected to form more complex near-wellbore fractures. (2) Under different perforation modes and parameters, the near-wellbore fracture morphology and propagation law formed by microfractures around tunnels are also different. (3) The existence and expansion of near-wellbore fractures validate Chen et al.’s (2005) conjecture that there are “pre-existing fractures” in perforation and negate the assumption that the perforation tunnels are complete. There are no near-wellbore fractures when the perforation method is optimized. The research results in this paper can provide guidance and reference for improving the perforation fracturing effect in oil and gas reservoirs.

Temporal network embedding using graph attention network

Graph convolutional network (GCN) has made remarkable progress in learning good representations from graph-structured data. The layer-wise propagation rule of conventional GCN is designed in such a way that the feature aggregation at each node depends on the features of the one-hop neighbouring nodes. Adding an attention layer over the GCN can allow the network to provide different importance within various one-hop neighbours. These methods can capture the properties of static network, but is not well suited to capture the temporal patterns in time-varying networks. In this work, we propose a temporal graph attention network (TempGAN), where the aim is to learn representations from continuous-time temporal network by preserving the temporal proximity between nodes of the network. First, we perform a temporal walk over the network to generate a positive pointwise mutual information matrix (PPMI) which denote the temporal correlation between the nodes. Furthermore, we design a TempGAN architecture which uses both adjacency and PPMI information to generate node embeddings from temporal network. Finally, we conduct link prediction experiments by designing a TempGAN autoencoder to evaluate the quality of the embedding generated, and the results are compared with other state-of-the-art methods.

Optimization Analysis on Layout of 220kV Outdoor Substation for Noise Control at the Boundary

The noise in substations has become one of the most concerning problems in the field of power grid. In this paper, the principle of substation layout optimization based on the maximum acoustic ray shielding method is established in the premise of the sound wave propagation rule in complicated air medium to meet the requirements of relevant national environmental standards for noise at the boundary of the substation. By establishing the acoustic simulation analysis model of the 220kV outdoor substation, the noise level at the boundary of the substation before and after the layout optimization is compared and analysed, and the distribution rule of the sound field inside and outside the substation is obtained. The analysis results show that the optimized layout of the substation can effectively reduce the noise at the boundary of the substation, which provides a control method for the noise optimization design of the substation.

Study on Sound Field Distribution Rule for Tank Structures of Large Oil-immersed Transformers

Large oil-immersed transformers are an important part of the transmission and distribution network in power systems. Power transformers are the main noise source of substations. Because of the uneven manufacturing process, aging equipment, long-term operation, and close distance from sensitive points, the problem of transformer noise pollution has become increasingly prominent. In this paper, the transmission and analysis model is established for transformer sound waves on the interface between insulating oil and tank body according to the sound wave propagation rule in complicated medium, and the simplified acoustic simulation model is constructed for large oil-immersed transformers by simulating the vibration noise of transformer core with monopole sound source, with which, the sound field distribution rule inside and outside the transformer tank structure is obtained, and finally, the influence factors for noise distribution are given. The results of the study provide control basis for reducing transformer noise.

Assessment of HY-2A GM data by deriving the gravity field and bathymetry over the Gulf of Guinea

Nine cycles of Haiyang-2A (HY-2A) IGDR (Interim Geophysical Data Record) data are used to derive gravity products over the Gulf of Guinea (15° W–5° E, 4° S–4° N). Firstly, the sea surface heights (SSH) and vertical deflections are derived and their precisions are evaluated. The comparison results show that the east component of vertical deflections has a poorer precision than the north component by 4.15 times. A theoretical proof was given to explain this point according to the error propagation rule. Gravity anomalies are then derived from vertical deflections using the remove–restore method. The precision of the derived HY-2A gravity anomalies is evaluated with SIO, DTU13, EGM2008, EIGEN-6C4 products. The results showed that the differences between HY-2A-derived gravity anomalies and these models have mean values larger than 0.5 mGal and std values around 7.0–7.3 mGal. In order to improve the precision, an improved new version of gravity anomalies was derived by assigning a small weight to the east component of vertical deflections, since the precision of which is poorer than the north component. Comparison with the initial model showed that the precision of the new gravity anomalies is an improvement of the initial model by approximately two times. When compared with EGM2008, EIGEN-6C4, SIOv28 and DTU13, the mean values of the differences are close to zero and standard deviation of the differences are around 2.7–3.0 mGal. The improved gravity anomalies were used to invert the bathymetry of the region using the gravity-geologic method. The modeled bathymetry compared well with a previous bathymetric study by the authors that used DTU13 gravity anomalies. It also performed well against ETOPO1 and SRTM15+V2; with difference means, standard deviations and correlation coefficients of 26.67 m, 183.09 m, 0.9562; and 12.26 m, 174.55 m, 0.9590, respectively. This implies that SSH data from HY-2A are geophysically reliable; and hence, can be incorporated with SSH data from other satellite altimeters.

Exploring Secret Keys in Searching Integral Distinguishers Based on Division Property

Division property proposed by Todo at EUROCRYPT 2015 is a generalized integral property. Then, conventional bit-based division property (CBDP) and bitbased division property using three subsets (BDPT) were proposed by Todo and Morii at FSE 2016. At ASIACRYPT 2016, Xiang et al. extended Mixed Integer Linear Programming (MILP) method to search integral distinguishers based on CBDP. And at ASIACRYPT 2019, Wang et al. proposed an MILP-aided method of searching integral distinguishers based on BDPT. Although BDPT is powerful in searching integral distinguishers, the accuracy is not perfect.For block cipher SPECK32, as the block size is only 32 bits, we can experimentally observe the behaviors of all the plaintexts under a fixed key. By testing 210 random secret keys, we experimentally find a better integral distinguisher of 6-round SPECK32 with 30 active bits. But this experimental integral distinguisher cannot be proved by existing methods. So there still exists a gap between the proved distinguisher and the experimental one.To fill the gap, we explore secret keys in searching integral distinguishers based on BDPT. We put forward a situation where “Xor with The Secret Key” operation can be bypassed. Based on the new BDPT propagation rule, an improved automatic algorithm of searching integral distinguishers is proposed. For SPECK32, our improved algorithm can find the 6-round integral distinguisher with 230 chosen plaintexts. The gap between the proved distinguisher and the experimental one is filled. Moreover, we apply this improved method to search the integral distinguishers of SPECK, KATAN/KTANTAN, SIMON, SIMECK, SIMON(102), PRESENT and RECTANGLE block ciphers. The integral distinguishers found by our improved method are better than or consistent with the previous longest distinguishers.

Modeling and Prediction of COVID-19 Outbreak in India

As the outbreak of coronavirus disease 2019 (COVID-19) is continuously increasing in India, so epidemiological modeling of COVID-19 data is urgently required for administrative strategies. Time series and is capable to predict future observations by modeling the data based on past and present data. Here, we have modeled the epidemiological COVID-19 Indian data using various models. Based on the collected COVID-19 outbreak data, we try to find the propagation rule of this outbreak disease and predict the outbreak situations in India. For India data, the time series model gives the best results in the form of predication as compared to other models for all variables of COVID-19. For new cases, new deaths, total cases and total deaths, the best fitted ARIMA models are as follows: ARIMA(0,2,3), ARIMA(0,1,1), ARIMA(0,2,0) and ARIMA(0,2,1). Based on time series analysis, we predict all variables for next month and conclude that the predictive value of Indian COVID-19 data of total cases is more than 20 lakhs with more than 43 thousand total deaths. The present chapter recommended that a comparison between various predictive models provide the accurate and better forecast value of the COVID-19 outbreak for all study variables.