Both connate water and the injected water through hydraulic fracturing can coexist with methane inside shale nanopores where two-phase flow possibly occurs. Few studies have been pertaining to two-phase flow of water and methane in shale reservoirs at nanoscale. In this work, molecular dynamics simulations are employed to investigate two-phase flow of water and methane in slit-shaped silica nanopores with hydrophilic surfaces. A sandwich structure of water film−methane−water film or a structure of the methane gas bubble wrapped in water bridge exists in the nanopore because water wets the surfaces. Darcy's law breaks down for methane single-phase flow in the nanopore because of the slippage near the surfaces. For two-phase flow of water and methane within the nanopore, water flow pattern varies in the form of water film, water bridge and water pillar at different applied accelerations (different pressure gradients), showing the breakdown of Darcy's law for water flow. This is attributed to the inhomogeneous number density distribution near the surfaces, which arises from the electrostatic interactions and the hydrogen bonds between water molecules and the surfaces. However, the varied water flow patterns have no effect on methane flow rate, suggesting that Darcy's law holds for methane flow in two-phase flow of water and methane inside the nanopore. This can be explained by the increased friction between methane and fluctuating water films. The results will advance understanding the mechanism of water and gas transport in nanoporous media and the exploitation of shale resources.

Ocean turbulence measurement in the wild sea has contributed significantly to improving our understanding of ocean mixing processes. Restricted by observation instruments and methods, the measured turbulence signal contains much information about the marine energy evolution mixed with a large amount of noise. Aiming at eliminating noise in deep sea exploration, a novel EMD-based (empirical mode decomposition) denoising method was designed. In this method, the collected ocean turbulence signal is first decomposed using EMD algorithm to obtain the intrinsic mode functions (IMFs). Then, the correlation coefficient between each IMF and the raw turbulence shear as well as the accelerations signal is calculated, which is taken as vibration reference signal. Finally, search for the proper IMF that has the following features (i) the correlation coefficient with the raw shear is larger than that with the accelerations signal; (ii) maximum difference exists between two adjacent correlation coefficients with the accelerations. IMFs that have these features are searched for signal reconstruction to realize the denoising of non-stationary turbulence. Turbulence signals collected with a self-designed autonomous reciprocating turbulence observation profiler (ARTP) deployed in the South China Sea (SCS) are used to validate the effectiveness and feasibility of the novel denoising method. Through comparison with the Nasmyth theoretical spectrum, the results show that the denoising method can not only effectively remove the noise component, but also maintain the detail characteristics of the effective turbulence signal under high noise, which offers a good theoretical foundation for the analysis of the ocean turbulent characteristics and energy evolution.

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When ocean turbulence signals are collected using turbulence observation instruments in real marine environments, the effective signals in the acquired data set are often polluted by noise. In order to eliminate the noise component contained in the non-stationary and nonlinear ocean turbulence signals, a new multi-scale turbulence signal denoising method is proposed by combining the empirical mode decomposition (EMD) and principle component analysis (PCA). First, the time series of turbulence signals are decomposed into a couple of components by EMD algorithm and approximately calculate the noise energy in each intrinsic mode function (IMF). Then, PCA is implemented on each IMF. The appropriate principal components are selected according to the decomposition characteristics of PCA and the noise energy proportion in IMF. Each IMF is reconstructed by the selected principle components. At last, the effective ocean turbulence signals are reconstructed by the corrected IMFs and the residue. Ocean turbulence signals collected in the South China Sea (SCS) are used to evaluate the effectiveness of the proposed method. The results show that the proposed method can effectively eliminate the noise and maintain the characteristics of the effective turbulence signals under high noise. Turbulence kinetic energy (TKE) is also estimated from the denoised signals, which provide a reliable data basis for the analysis of the turbulent characteristics in later stage.

Because industrial robots have uneven positioning accuracy in the overall working space, they have greatly limited their applications in the fields of high -precision production and processing. Regarding this status quo, this article focuses on analyzing the optimal working space of industrial robots, and screening and limiting the optimal working scope of the selected industrial robots to ensure that the robot end is at a relatively high position with high positioning accuracy. This article selects the collaborative robot AUBO E3 for modeling, analysis and simulation. Calculate the robot based on the Jacques matrix and the number of conditions under the spiral theory, quantitatively depict the exercise performance of the robot. The excellent working space is visually simulated, and finally uses the API laser tracker to measure the end error of the robot. As a result, the accuracy of the end point in the optimal working space is higher than the endpoint accuracy of the overall work space.