Physics-informed machine learning for solving partial differential equations in porous media

Physical phenomenon in nature is generally simulated by partial differential equations. Among different sorts of partial differential equations, the problem of two-phase flow in porous media has been paid intense attention. As a promising direction, physics-informed neural networks shed new light on the solution of partial differential equations. However, current physics-informed neural networks’ ability to learn partial differential equations relies on adding artificial diffusion or using prior knowledge to increase the number of training points along the shock trajectory, or adaptive activation functions. To address these issues, this study proposes a physics-informed neural network with long short-term memory and attention mechanism, an ingenious method to solve the Buckley-Leverett partial differential equations representing two-phase flow in porous media. The designed network structure overcomes the dependency on artificial diffusion terms and enhances the importance of shallow features. The experimental results show that the proposed method is in good agreement with analytical solutions. Accurate approximations are shown even when encountering shock points in saturated fields of porous media. Furthermore, experiments show our innovative method outperforms existing traditional physics-informed machine learning approaches.Cited as: Shan, L., Liu, C., Liu, Y., Tu, Y., Dong, L., Hei, X. Physics-informed machine learning for solving partial differential equations in porous media. Advances in Geo-Energy Research, 2023, 8(1): 37-44. https://doi.org/10.46690/ager.2023.04.0

Detection and exposure assessment of pesticide residues in leek in He’nan Province

ObjectiveTo evaluate the health risk of pesticide exposure from leek， the pesticide residue in leek from Henan market was investigated.MethodsThe residues of 16 pesticides in leek sold on Henan market in 2020 were detected and analyzed. According to health guidance values such as food consumption data of the World Health Organization， acute reference dose formulated by Joint Meeting on Pesticide Residues and adaptable daily intake in “National food safety standard-Maximum residue limits for pesticides in food”， the acute and chronic exposure risks of pesticide residues in leek were evaluated by point assessment method， and the cumulative exposure was evaluated by hazard index method.ResultsThere were many types of pesticide residues in leek samples and 93.81% （424/452） of the samples were positive. 7 of the 14 pesticides exceeded their MRLs， and the violation rate of all samples was 16.15%. The detection of multiple pesticides was relatively serious， and 56.42% of the samples contained more than two pesticide residues. In the acute exposure assessment， the acute risks of carbofuran， procymidone and phorate exceeded the acceptable level. In the chronic exposure assessment， the chronic risk of omethoate exceeded the acceptable level. And insecticide pesticides had cumulative poisoning risk.ConclusionThe situation of pesticide residues in leek in Henan province was relatively prominent. To ensure the safety of agricultural products， it was recommended that the routine monitoring and use of pesticide， especially high-risk pesticides such as omethoate， carbofuran， procymidone and phorate should be strengthened

Study and Simulation of Deformation Mechanics Modeling of Flexible Workpiece Processing by Rayleigh-Ritz Method

This paper discusses the calculation problems of bending deformation of FWP processing. Take three axis CNC machining as an example, to establish mechanics model of flexible workpiece processing process. The flexible workpiece balance equation is a two-dimensional partial differential equation, to solve the problem of flexible workpiece bending deformation using Rayleigh-Ritz method and designing the test function of bending deformation of flexible workpiece. By satisfying the minimum potential energy condition of FWP processing to work out the approximate solution of bending deformation of flexible workpiece, find out the relationship between material properties of flexible piece, acting force Fz, and deformation value. Finally, the rectangle flexible workpiece which is made up of polyurethane sponge is selected as an experiment subject. The results show that the average relative deviation between theoretical value and observed value is only 5.51%. It is proved that the bending deformation test function satisfies the actual deformation calculation requirements

Effectiveness of Oral Fluid in Pathogenic Surveillance of Acute Respiratory Infection

Oral fluid (OF) is a new safe, non-invasive, convenient, and efficient biological sample that can be used for virus nucleic acid and antibody detection. Because few studies have performed surveillance of multiple respiratory pathogens, this study sought to explore the application value of OF in this field. OF and throat swabs were collected from December 2020 to December 2021 in patients with acute respiratory tract infections in Beijing. Multiplex real-time PCR was performed, and the detection performance of two samples was compared. A total of 769 OF and throat swab samples were collected. The detection rates of respiratory pathogens in throat swabs and OF were 29.26% (225/769) and 20.81% (160/769), respectively. The sensitivity and specificity of the OF assay, compared with the throat swab assay, were 71.11% (160/225) and 100% (544/544), respectively. The two assays had excellent agreement (kappa = 0.78). The detection consistency varied among pathogens. For OF samples, the most common pathogen was the influenza B virus, and the highest detection rate was in the ≤5-year-old group. The highest positivity rate was observed in December 2021. OF samples have excellent potential for the epidemiological surveillance of respiratory pathogens, and may have application prospects in preventing and controlling infectious diseases

Rock CT Image Super-Resolution Using Residual Dual-Channel Attention Generative Adversarial Network

Because of its benefits in terms of high speed, non-destructiveness, and three-dimensionality, as well as ease of integration with computer simulation, computed tomography (CT) technology is widely applied in reservoir geology research. However, rock imaging is restricted by the device used as there is not a win–win for both the image receptive field and corresponding resolution. Convolutional neural network-based super-resolution reconstruction has become a hot topic in improving the performance of CT images. With the help of a convolution kernel, it can effectively extract characteristics and ignore disturbance information. The dismal truth is that convolutional neural networks still have numerous issues, particularly unclear texture details. To address these challenges, a generative adversarial network (RDCA-SRGAN) was designed to improve rock CT image resolution using the combination of residual learning and a dual-channel attention mechanism. Specifically, our generator employs residual attention to extract additional features; similarly, the discriminator builds on dual-channel attention and residual learning to distinguish generated contextual information and decrease computational consumption. Quantitative and qualitative analyses demonstrate that the proposed model is superior to earlier advanced frameworks and is capable to constructure visually indistinguishable high-frequency details. The quantitative analysis shows our model contributes the highest value of structural similarity, enriching the more detailed texture information. From the qualitative analysis, in enlarged details of the reconstructed images, the edges of the images generated by the RDCA-SRGAN can be shown to be clearer and sharper. Our model not only performs well in subtle coal cracks but also enriches more dissolved carbonate and carbon minerals. The RDCA-SRGAN has substantially enhanced the reconstructed image resolution and our model has great potential to be used in geomorphological study and exploration

Mechanical Fault Sound Source Localization Estimation in a Multisource Strong Reverberation Environment

Aiming at the sound source localization of mechanical faults in a strong reverberation scenario with multiple sound sources, this paper investigates a mechanical fault source localization method using the U-net deep convolutional neural network. The method utilizes the SRP-PHAT algorithm to calculate the response power spectra of the collected multichannel fault signals. Through the utilization of the U-net neural network, the response power spectra containing spurious peaks are transformed into “clean” estimated source distribution maps. By employing interpolation search, the estimated source distribution maps are processed to obtain location estimations for multiple fault sources. To validate the effectiveness of the proposed method, this paper constructs an experimental dataset using mechanical fault data from electromechanical equipment relays and conducts sound source localization experiments. The experimental results show that the U-net network under 0.2 s/0.5 s/0.7 s reverberation time can effectively eliminate spurious peak interference in the response power spectrum. As the signal-to-noise ratio decreases, it can still distinguish the sound sources with a distance of 0.2 m. In the context of multifault source localization, the method is capable of simultaneously locating the positions of four fault sources, with an average localization error of less than 0.02 m. The method in this paper effectively eliminates spurious peaks in the response power spectra under conditions of multisource strong reverberation. It accurately locates multiple mechanical fault sources, thereby significantly enhancing the efficiency of mechanical fault detection

Rock CT Image Super-Resolution Using Residual Dual-Channel Attention Generative Adversarial Network

Because of its benefits in terms of high speed, non-destructiveness, and three-dimensionality, as well as ease of integration with computer simulation, computed tomography (CT) technology is widely applied in reservoir geology research. However, rock imaging is restricted by the device used as there is not a win–win for both the image receptive field and corresponding resolution. Convolutional neural network-based super-resolution reconstruction has become a hot topic in improving the performance of CT images. With the help of a convolution kernel, it can effectively extract characteristics and ignore disturbance information. The dismal truth is that convolutional neural networks still have numerous issues, particularly unclear texture details. To address these challenges, a generative adversarial network (RDCA-SRGAN) was designed to improve rock CT image resolution using the combination of residual learning and a dual-channel attention mechanism. Specifically, our generator employs residual attention to extract additional features; similarly, the discriminator builds on dual-channel attention and residual learning to distinguish generated contextual information and decrease computational consumption. Quantitative and qualitative analyses demonstrate that the proposed model is superior to earlier advanced frameworks and is capable to constructure visually indistinguishable high-frequency details. The quantitative analysis shows our model contributes the highest value of structural similarity, enriching the more detailed texture information. From the qualitative analysis, in enlarged details of the reconstructed images, the edges of the images generated by the RDCA-SRGAN can be shown to be clearer and sharper. Our model not only performs well in subtle coal cracks but also enriches more dissolved carbonate and carbon minerals. The RDCA-SRGAN has substantially enhanced the reconstructed image resolution and our model has great potential to be used in geomorphological study and exploration

Research on Fault Diagnosis of Flexible Material R2R Manufacturing System Based on Quality Control Chart and SoV

The Stream of Variation (SoV) model and control chart are combined to study the fault diagnosis method of flexible materials R2R manufacturing system. Based on the analysis of the correlation between the fault source and product quality in the manufacturing process and also the statistical distribution rule of the processing quality characteristic vector Li and the fault source fi, SoV model under controlled or uncontrolled states and the mathematical model of the probability distribution of the statistic Ti,m2 of the quality characteristic variable Li are deduced. And the calculation equation of the centerline, the upper limit, and the lower limit of the control chart are deduced. The experimental results show that, under controlled or uncontrolled condition, when the program runs to 500 steps, the Average Run Length (ARL) of the performance parameters tends to be stable; and when program reaches 1000 steps, the actual ARL value is almost the same as the theoretical value. The fault diagnosis experiment shows that, under the condition when the fault source is strongly correlated or the fault source correlation coefficient is the same, using the control chart established in this paper can simply and quickly determine the fault location in the system

Improved Feature Learning: A Maximum-Average-Out Deep Neural Network for the Game Go

Computer game-playing programs based on deep reinforcement learning have surpassed the performance of even the best human players. However, the huge analysis space of such neural networks and their numerous parameters require extensive computing power. Hence, in this study, we aimed to increase the network learning efficiency by modifying the neural network structure, which should reduce the number of learning iterations and the required computing power. A convolutional neural network with a maximum-average-out (MAO) unit structure based on piecewise function thinking is proposed, through which features can be effectively learned and the expression ability of hidden layer features can be enhanced. To verify the performance of the MAO structure, we compared it with the ResNet18 network by applying them both to the framework of AlphaGo Zero, which was developed for playing the game Go. The two network structures were trained from scratch using a low-cost server environment. MAO unit won eight out of ten games against the ResNet18 network. The superior performance of the MAO unit compared with the ResNet18 network is significant for the further development of game algorithms that require less computing power than those currently in use

PTS-FNN-Based Health Prediction Method for Flexible Photoelectric Film Material Processing Equipment

Roll-to-Roll (R2R) processing is a common processing method for flexible photoelectric film materials. Due to the physical properties of the materials, the change in the performance of the R2R processing equipment can easily cause deformation of the flexible film material, it is particularly important to predict the performance degradation of the processing equipment. Based on the accuracy and real-time requirements of performance degradation prediction, a PTS-FNN model for performance degradation prediction was proposed in this paper, which combines the Possibilistic C-Means (PCM) fuzzy clustering and Takagi–Sugeno Fuzzy Neural Network (TS-FNN). We also studied the PCM classification algorithm of input data of PTS-FNN model, the predecessor network of TS-FNN prediction model and the construction method of post-component network. Finally, the implementation process of PCM classification algorithm and TS-FNN prediction model were given. The R2R processing equipment health prediction experiment system was built and the PTS-FNN model experiment was carried out. The experimental results showed that the training time of PTS-FNN model was 50.37% less than the standard TS-FNN prediction model. The prediction accuracy increased by 5.48%, and the PTS-FNN had no error in the judgment of state 1 and state 4