Structure fusion based on graph convolutional networks for semi-supervised classification

Suffering from the multi-view data diversity and complexity for semi-supervised classification, most of existing graph convolutional networks focus on the networks architecture construction or the salient graph structure preservation, and ignore the the complete graph structure for semi-supervised classification contribution. To mine the more complete distribution structure from multi-view data with the consideration of the specificity and the commonality, we propose structure fusion based on graph convolutional networks (SF-GCN) for improving the performance of semi-supervised classification. SF-GCN can not only retain the special characteristic of each view data by spectral embedding, but also capture the common style of multi-view data by distance metric between multi-graph structures. Suppose the linear relationship between multi-graph structures, we can construct the optimization function of structure fusion model by balancing the specificity loss and the commonality loss. By solving this function, we can simultaneously obtain the fusion spectral embedding from the multi-view data and the fusion structure as adjacent matrix to input graph convolutional networks for semi-supervised classification. Experiments demonstrate that the performance of SF-GCN outperforms that of the state of the arts on three challenging datasets, which are Cora,Citeseer and Pubmed in citation networks

Estimate of Saturation Pressures of Crude Oil by Using Ensemble-Smoother-Assisted Equation of State

The equation of state (EOS) has been extensively used to evaluate the saturation pressures of petroleum fluids. However, the accurate determination of empirical parameters in the EOS is challenging and time-consuming, especially when multiple measurements are involved in the regression process. In this work, an ensemble smoother (ES) -assisted EOS method has been proposed to compute the saturation pressure by intelligently optimizing the to-be-tuned parameters. To be specific, the to-be-tuned parameters for the Peng–Robinson EOS (PR EOS) are integrated into a model input matrix and the measured saturation pressures are collected into a model output matrix. The model input matrix is then integrally and iteratively updated with respect to the model output matrix by using the iterative ES algorithm. For convenience, an in-house module is compiled to implement the ES-assisted EOS for determining the saturation pressures of crude oils. Subsequently, the experimentally measured saturation pressures of 45 mixtures of heavy oil and solvents are used to validate the performance of the in-house module. In addition, 130 measured saturation pressures of worldwide light oil samples are collected to verify the applicability of the developed ES-assisted EOS method. The in-house module is found to be competent by not only matching 45 measured saturation pressures with a better agreement than a commercial simulator but also providing a quantitative means to analyze the uncertainties associated with the estimated model parameters and the saturation pressure. Moreover, the application of the ES-assisted EOS to 130 light oil samples distinctly demonstrates that the new method greatly improves the accuracy and reliability of the EOS regression. Consequently, the in-house module representing the ES-assisted EOS is proven as an efficient and flexible tool to determine the saturation pressure under various conditions and implement uncertain analyses associated with the saturation pressure

Crosslinked polymeric nanocapsules with controllable structure via a ‘self-templating’approach

info:eu-repo/semantics/publishe

Determination of Klinkenberg Permeability Conditioned to Pore-Throat Structures in Tight Formations

This paper has developed a pragmatic technique to efficiently and accurately determine the Klinkenberg permeability for tight formations with different pore-throat structures. Firstly, the authors use steady-state experiments to measure the Klinkenberg permeability of 56 tight core samples under different mean pore pressures and confining pressures. Secondly, pressure-controlled mercury injection (PMI) experiments and thin-section analyses are conducted to differentiate pore-throat structures. After considering capillary pressure curve, pore types, throat size, particle composition, and grain size, the pore-throat structure in the target tight formation was classified into three types: a good sorting and micro-fine throat (GSMFT) type, a moderate sorting and micro-fine throat (MSMFT) type, and a bad sorting and micro throat (BSMT) type. This study found that a linear relationship exists between the Klinkenberg permeability and measured gas permeability for all three types of pore-throat structures. Subsequently, three empirical equations are proposed, based on 50 core samples of data, to estimate the Klinkenberg permeability by using the measured gas permeability and mean pore pressure for each type of pore-throat structure. In addition, the proposed empirical equations can generate accurate estimates of the Klinkenberg permeability with a relative error of less than 5% in comparison to its measured value. The application of the proposed empirical equations to the remaining six core samples has demonstrated that it is necessary to use an appropriate equation to determine the Klinkenberg permeability of a specific type of pore-throat structure. Consequently, the newly developed technique is proven to be qualified for accurately determining the Klinkenberg permeability of tight formations in a timely manner

Numerical study on steady pitting process of metal surface

[Objectives] The service life of the shipboard equipment will be seriously affected in the harsh corrosive environment of seawater. Therefore,it can effectively reduce the equipment damages due to corrosion by understanding corrosion mechanism and predicting corrosion.[Methods] Lattice Boltzmann (LBM) corrosion model can be used to describe multiphase multicomponent flow and transmission, electrochemical reaction and metal steady pitting process. Based on this model,a numerical study is conducted to simulate a single corrosion pit on a metal surface immersed in the liquid corrosive environment;the effects of corrosion reaction rate,corrosion solution diffusion coefficient and corrosion product diffusion coefficient on the degree of corrosion are analyzed.[Results] Morphological changes of the pit on the metal surface can be obtained by the numerical simulation. The numerical results show that, the pitting corrosion will be deeper because of the electrochemical corrosion system of large cathode and small anode formed by passivation film and metal matrix during the steady corrosion process of the single pit on the metal surface;the primary pitting hole will produce secondary pitting hole at the bottom. A conclusion obtained by changing the factors in the model is that,the degree of corrosion increases as the corrosion reaction rate increases, increases as the diffusion coefficient of the reactant components increases,and decreases as the corrosion product diffusion coefficient increases.[Conclusions] The similar corrosion morphology of the real metal can be simulated through this corrosion model

Numerical Investigation of the Effect of Hub Gaps on the 3D Flows Inside the Stator of a Highly Loaded Axial Compressor Stage

Both the compressor performance and the 3D flows inside the stator passage are significantly impacted by the stator hub gap. The interplay between leakage flow and corner separation within a cantilevered stator of a highly loaded, low-speed axial compressor with a succession of stator hub gaps was examined numerically in this paper. Firstly, the simulated results were compared with the measured results, including the compressor characteristics, the 3D flow structures, and the flow fields at the stator outlet. The results revealed that the used CFD solver, as well as the corresponding setup, can reproduce the flow not only in terms of the trend along with the stator hub gap, but also in terms of the specific scale of the 3D flow structure. Hence, it is feasible enough to be applied in the present investigation. Secondly, the flow mechanisms of the interplay between the corner separation and the leakage flow with different stator hub gaps were analyzed. It was found that the velocity of the leakage flow is the key parameter that dominates the flow structures as well as the compressor performance. Additionally, a simple metric was proposed to be used to choose the optimum stator hub gap. By comparing our results with those from published research, this metric was proven to be feasible. Finally, it is also discussed how the stator hub gap affected the stator inlet flow and rotor performance. It is demonstrated that the stator passage flow blockage can affect the upstream flow field. As a result, the performance of the rotor tends to vary in the opposite direction to that of the stator

Fault diagnosis for multiple current sensors in grid‐connected inverter based on average modulation voltage

International audienceAbstract Grid‐connected inverters are the core equipment in the renewable power system. There are multiple current sensors which may affect the driving module of the switch in inverter. In multiple current sensor fault diagnosis, the coupling between fault components makes fault diagnosis difficult. This paper presents an offset fault diagnosis method of multiple current sensors based on the average modulation voltage model. Based on the influence of current sensor offset fault in the system, the average modulation voltage model is established in three‐phase stationary coordinates. The difference between the measured value of the current and the actual value is estimated through the model when the offset fault occurs. And then the fault is located. Experimental results show that the fault can be located accurately and fault tolerant control can be performed by this method when there are offset faults in multiple sensors simultaneously