Current hyperelastic constitutive models of hydrogels face difficulties in capturing the stress-strain behaviors of hydrogels under extremely large deformation because the effect of non-affine deformation of the polym...Current hyperelastic constitutive models of hydrogels face difficulties in capturing the stress-strain behaviors of hydrogels under extremely large deformation because the effect of non-affine deformation of the polymer network inside is ambiguous.In this work,we construct periodic random network(PRN)models for the effective polymer network in hydrogels and investigate the non-affine deformation of polymer chains intrinsically originates from the structural randomness from bottom up.The non-affine deformation in PRN models is manifested as the actual stretch of polymer chains randomly deviated from the chain stretch predicted by affine assumption,and quantified by a non-affine ratio of each polymer chain.It is found that the non-affine ratios of polymer chains are closely related to bulk deformation state,chain orientation,and initial chain elongation.By fitting the non-affine ratio of polymer chains in all PRN models,we propose a non-affine constitutive model for the hydrogel polymer network based on micro-sphere model.The stress-strain curves of the proposed constitutive models under uniaxial tension condition agree with the simulation results of different PRN models of hydrogels very well.展开更多
The empirical models for wavenumber-frequency spectra of wall pressure are broadly used in the fast prediction of aerodynamic and hydrodynamic noise.However,it needs to fit the parameter using massive data and is only...The empirical models for wavenumber-frequency spectra of wall pressure are broadly used in the fast prediction of aerodynamic and hydrodynamic noise.However,it needs to fit the parameter using massive data and is only used for limited cases.In this letter,we propose Kolmogorov-Arnold networks(KAN)base models for wavenumber-frequency spectra of pressure fluctuations under turbulent boundary layers.The results are compared with DNS results.In turbulent channel flows,it is found that the KAN base model leads to a smooth wavenumber-frequency spectrum with sparse samples.In the turbulent flow over an axisymmetric body of revolution,the KAN base model captures the wavenumber-frequency spectra near the convective peak.展开更多
Natural slopes usually display complicated exposed rock surfaces that are characterized by complex and substantial terrain undulation and ubiquitous undesirable phenomena such as vegetation cover and rockfalls.This st...Natural slopes usually display complicated exposed rock surfaces that are characterized by complex and substantial terrain undulation and ubiquitous undesirable phenomena such as vegetation cover and rockfalls.This study presents a systematic outcrop research of fracture pattern variations in a complicated rock slope,and the qualitative and quantitative study of the complex phenomena impact on threedimensional(3D)discrete fracture network(DFN)modeling.As the studies of the outcrop fracture pattern have been so far focused on local variations,thus,we put forward a statistical analysis of global variations.The entire outcrop is partitioned into several subzones,and the subzone-scale variability of fracture geometric properties is analyzed(including the orientation,the density,and the trace length).The results reveal significant variations in fracture characteristics(such as the concentrative degree,the average orientation,the density,and the trace length)among different subzones.Moreover,the density of fracture sets,which is approximately parallel to the slope surface,exhibits a notably higher value compared to other fracture sets across all subzones.To improve the accuracy of the DFN modeling,the effects of three common phenomena resulting from vegetation and rockfalls are qualitatively analyzed and the corresponding quantitative data processing solutions are proposed.Subsequently,the 3D fracture geometric parameters are determined for different areas of the high-steep rock slope in terms of the subzone dimensions.The results show significant variations in the same set of 3D fracture parameters across different regions with density differing by up to tenfold and mean trace length exhibiting differences of 3e4 times.The study results present precise geological structural information,improve modeling accuracy,and provide practical solutions for addressing complex outcrop issues.展开更多
Multiphase flow in low permeability porous media is involved in numerous energy and environmental applications.However,a complete description of this process is challenging due to the limited modeling scale and the ef...Multiphase flow in low permeability porous media is involved in numerous energy and environmental applications.However,a complete description of this process is challenging due to the limited modeling scale and the effects of complex pore structures and wettability.To address this issue,based on the digital rock of low permeability sandstone,a direct numerical simulation is performed considering the interphase drag and boundary slip to clarify the microscopic water-oil displacement process.In addition,a dual-porosity pore network model(PNM)is constructed to obtain the water-oil relative permeability of the sample.The displacement efficiency as a recovery process is assessed under different wetting and pore structure properties.Results show that microscopic displacement mechanisms explain the corresponding macroscopic relative permeability.The injected water breaks through the outlet earlier with a large mass flow,while thick oil films exist in rough hydrophobic surfaces and poorly connected pores.The variation of water-oil relative permeability is significant,and residual oil saturation is high in the oil-wet system.The flooding is extensive,and the residual oil is trapped in complex pore networks for hydrophilic pore surfaces;thus,water relative permeability is lower in the water-wet system.While the displacement efficiency is the worst in mixed-wetting systems for poor water connectivity.Microporosity negatively correlates with invading oil volume fraction due to strong capillary resistance,and a large microporosity corresponds to low residual oil saturation.This work provides insights into the water-oil flow from different modeling perspectives and helps to optimize the development plan for enhanced recovery.展开更多
Coalbed methane(CBM)recovery is attracting global attention due to its huge reserve and low carbon burning benefits for the environment.Fully understanding the complex structure of coal and its transport properties is...Coalbed methane(CBM)recovery is attracting global attention due to its huge reserve and low carbon burning benefits for the environment.Fully understanding the complex structure of coal and its transport properties is crucial for CBM development.This study describes the implementation of mercury intrusion and μ-CT techniques for quantitative analysis of 3D pore structure in two anthracite coals.It shows that the porosity is 7.04%-8.47%and 10.88%-12.11%,and the pore connectivity is 0.5422-0.6852 and 0.7948-0.9186 for coal samples 1 and 2,respectively.The fractal dimension and pore geometric tortuosity were calculated based on the data obtained from 3D pore structure.The results show that the pore structure of sample 2 is more complex and developed,with lower tortuosity,indicating the higher fluid deliverability of pore system in sample 2.The tortuosity in three-direction is significantly different,indicating that the pore structure of the studied coals has significant anisotropy.The equivalent pore network model(PNM)was extracted,and the anisotropic permeability was estimated by PNM gas flow simulation.The results show that the anisotropy of permeability is consistent with the slice surface porosity distribution in 3D pore structure.The permeability in the horizontal direction is much greater than that in the vertical direction,indicating that the dominant transportation channel is along the horizontal direction of the studied coals.The research results achieve the visualization of the 3D complex structure of coal and fully capture and quantify pore size,connectivity,curvature,permeability,and its anisotropic characteristics at micron-scale resolution.This provides a prerequisite for the study of mass transfer behaviors and associated transport mechanisms in real pore structures.展开更多
Analyzing rock mass seepage using the discrete fracture network(DFN)flow model poses challenges when dealing with complex fracture networks.This paper presents a novel DFN flow model that incorporates the actual conne...Analyzing rock mass seepage using the discrete fracture network(DFN)flow model poses challenges when dealing with complex fracture networks.This paper presents a novel DFN flow model that incorporates the actual connections of large-scale fractures.Notably,this model efficiently manages over 20,000 fractures without necessitating adjustments to the DFN geometry.All geometric analyses,such as identifying connected fractures,dividing the two-dimensional domain into closed loops,triangulating arbitrary loops,and refining triangular elements,are fully automated.The analysis processes are comprehensively introduced,and core algorithms,along with their pseudo-codes,are outlined and explained to assist readers in their programming endeavors.The accuracy of geometric analyses is validated through topological graphs representing the connection relationships between fractures.In practical application,the proposed model is employed to assess the water-sealing effectiveness of an underground storage cavern project.The analysis results indicate that the existing design scheme can effectively prevent the stored oil from leaking in the presence of both dense and sparse fractures.Furthermore,following extensive modification and optimization,the scale and precision of model computation suggest that the proposed model and developed codes can meet the requirements of engineering applications.展开更多
Based on the percolation network model characterizing reservoir rock＇s pore structure and fluid characteristics, this paper qualitatively studies the effects of pore size, pore shape, pore connectivity, and the amoun...Based on the percolation network model characterizing reservoir rock＇s pore structure and fluid characteristics, this paper qualitatively studies the effects of pore size, pore shape, pore connectivity, and the amount of micropores on the I - Sw curve using numerical modeling. The effects of formation water salinity on the electrical resistivity of the rock are discussed. Then the relative magnitudes of the different influencing factors are discussed. The effects of the different factors on the I - Sw curve are analyzed by fitting simulation results. The results show that the connectivity of the void spaces and the amount of micropores have a large effect on the I - S, curve, while the other factors have little effect. The formation water salinity has a large effect on the absolute resistivity values. The non-Archie phenomenon is prevalent, which is remarkable in rocks with low permeability.展开更多
Complex network models are frequently employed for simulating and studyingdiverse real-world complex systems.Among these models,scale-free networks typically exhibit greater fragility to malicious attacks.Consequently...Complex network models are frequently employed for simulating and studyingdiverse real-world complex systems.Among these models,scale-free networks typically exhibit greater fragility to malicious attacks.Consequently,enhancing the robustness of scale-free networks has become a pressing issue.To address this problem,this paper proposes a Multi-Granularity Integration Algorithm(MGIA),which aims to improve the robustness of scale-free networks while keeping the initial degree of each node unchanged,ensuring network connectivity and avoiding the generation of multiple edges.The algorithm generates a multi-granularity structure from the initial network to be optimized,then uses different optimization strategies to optimize the networks at various granular layers in this structure,and finally realizes the information exchange between different granular layers,thereby further enhancing the optimization effect.We propose new network refresh,crossover,and mutation operators to ensure that the optimized network satisfies the given constraints.Meanwhile,we propose new network similarity and network dissimilarity evaluation metrics to improve the effectiveness of the optimization operators in the algorithm.In the experiments,the MGIA enhances the robustness of the scale-free network by 67.6%.This improvement is approximately 17.2%higher than the optimization effects achieved by eight currently existing complex network robustness optimization algorithms.展开更多
[Objective]To construct an Escherichia coli mutant strain that accumulates pyruvate by genetic modification guided by the genome-scale metabolic network model.[Methods]Using a genome-scale metabolic network model as a...[Objective]To construct an Escherichia coli mutant strain that accumulates pyruvate by genetic modification guided by the genome-scale metabolic network model.[Methods]Using a genome-scale metabolic network model as a guide,we simulated pyruvate production of E.coli,screened key genes in metabolic pathways,and developed gene editing procedures accordingly.We knocked out the acetate kinase gene ackA,phosphate acetyltransferase gene pta,alcohol dehydrogenase adhE,glycogen synthase gene glgA,glycogen phosphorylase gene glgP,phosphoribosyl pyrophosphate(PRPP)synthase gene prs,ribose 1,5-bisphosphate phosphokinase gene phnN,and transporter encoding gene proP.Furthermore,we knocked in the transporter encoding gene ompC,flavonoid toxin gene fldA,and D-serine ammonia lyase gene dsdA.[Results]A shake flask process with the genetically edited mutant strain MG1655-6-2 under anaerobic conditions produced pyruvate at a titer of 10.46 g/L and a yield of 0.69 g/g.Metabolomic analysis revealed a significant increase in the pyruvate level in the fermentation broth,accompanied by notable decreases in the levels of certain related metabolic byproducts.Through 5 L fed-batch fermentation and an adaptive laboratory evolution,the strain finally achieved a pyruvate titer of 45.86 g/L.[Conclusion]This study illustrated the efficacy of a gene editing strategy predicted by a genome-scale metabolic network model in enhancing pyruvate accumulation in E.coli under anaerobic conditions and provided novel insights for microbial metabolic engineering.展开更多
The flow characteristics of coalbed methane(CBM)are influenced by the coal rock fracture network,which serves as the primary gas transport channel.This has a significant effect on the permeability performance of coal ...The flow characteristics of coalbed methane(CBM)are influenced by the coal rock fracture network,which serves as the primary gas transport channel.This has a significant effect on the permeability performance of coal reservoirs.In any case,the traditional techniques of coal rock fracture observation are unable to precisely define the flow of CBM.In this study,coal samples were subjected to an in situ loading scanning test in order to create a pore network model(PNM)and determine the pore and fracture dynamic evolution law of the samples in the loading path.On this basis,the structural characteristic parameters of the samples were extracted from the PNM and the impact on the permeability performance of CBM was assessed.The findings demonstrate that the coal samples'internal porosity increases by 2.039%under uniaxial loading,the average throat pore radius increases by 205.5 to 36.1μm,and the loading has an impact on the distribution and morphology of the pores in the coal rock.The PNM was loaded into the finite element program COMSOL for seepage modeling,and the M3 stage showed isolated pore connectivity to produce microscopic fissures,which could serve as seepage channels.In order to confirm the viability of the PNM and COMSOL docking technology,the streamline distribution law of pressure and velocity fields during the coal sample loading process was examined.The absolute permeability of the coal samples was also obtained in order for comparison with the measured results.The macroscopic CBM flow mechanism in complex lowpermeability coal rocks can be revealed through three-dimensional reconstruction of the microscopic fracture structure and seepage simulation.This study lays the groundwork for the fine description and evaluation of coal reservoirs as well as the precise prediction of gas production in CBM wells.展开更多
Ensuring stable frequency and voltage has recently become increasingly challenging for modern power systems. This is primarily due to the fluctuating and intermittent nature of renewable energy sources and the uncerta...Ensuring stable frequency and voltage has recently become increasingly challenging for modern power systems. This is primarily due to the fluctuating and intermittent nature of renewable energy sources and the uncertain electricity demand. To address these issues, this study proposes a load resource management(LRM) method to cope with the sudden power disturbances. The LRM method supports primary frequency and voltage regulation, and its integration with network dynamics minimizes the established disutility function caused by load participation. For better control performance, a non-dominated sorting genetic algorithm-Ⅱ(NSGA-Ⅱ)-based gain-tuning procedure was utilized for LRM, aiming to enhance the frequency/voltage nadir, reduce the frequency/voltage steady-state error, and minimize the total load control efforts. To validate the effectiveness of the proposed approach, comparative experiments were conducted with three load–resource management technologies for primary regulation auxiliary services in MATLAB/Simulink. Compared to the conventional optimal load control or using LRM alone, the improved NSGA-Ⅱ-based LRM demonstrates superior performance. It achieves better frequency response, voltage transients, and steady-state responses, while also considering disutility.展开更多
The press-fit connector is a typical plug-and-play solderless connection,and it is widely used in signal transmission in fields such as communication and automotive devices.This paper focuses on inverse designing and ...The press-fit connector is a typical plug-and-play solderless connection,and it is widely used in signal transmission in fields such as communication and automotive devices.This paper focuses on inverse designing and optimization of geometric structure,as well as insertion-withdrawal forces of press-fit connector using artificial neural network(ANN)-assisted optimization method.The ANN model is established to approximate the relationship between geometric parameters and insertion-withdrawal forces,of which hyper-parameters of neural network are optimized to improve model performance.Two numerical methods are proposed for inverse designing structural parameters(Model-I)and multi-objective optimization of insertion-withdrawal forces(Model-II)of press-fit connector.In Model-I,a method for inverse designing structure parameters is established,of which an ANN model is coupled with single-objective optimization algorithm.The objective function is established,the inverse problem is solved,and effectiveness is verified.In Model-II,a multi-objective optimization method is proposed,of which an ANN model is coupled with genetic algorithm.The Pareto solution sets of insertion-withdrawal forces are obtained,and results are analyzed.The established ANN-coupled numerical optimization methods are beneficial for improving the design efficiency,and enhancing the connection reliability of the press-fit connector.展开更多
Accurately characterizing the pore structure of Tamusu mudstone and simulating fluid flow within it are crucial for assessing underground disposal of high-level radioactive waste(HLW)in this formation.Modeling mudston...Accurately characterizing the pore structure of Tamusu mudstone and simulating fluid flow within it are crucial for assessing underground disposal of high-level radioactive waste(HLW)in this formation.Modeling mudstone presents challenges due to its multiscale pore structure and the necessity that accounts for the effects of high clay content.A method for constructing a dual-scale pore network model(PNM)for the Tamusu mudstone,which considers the hydrological expansion of clays,was proposed.This model integrates N2 adsorption data with focused ion beam/scanning electron microscopy(FIB/SEM)images and labels pores based on clay content.Simulations of single-phase flow were conducted to validate the proposed model.Additionally,the influences of cell number,connectivity,slip effects,and clay minerals on permeability were examined.The findings indicate that a configuration of 45×45×45 cells adequately represents the model.The permeability of the Tamusu mudstone,about 1020 m^(2),aligns with the experimental values.During the simulation,Knudsen diffusion is considered.Factors such as increased roughness,tortuosity,clay content,and water film thickness decrease the permeability,whereas increased connectivity enhances permeability.In the model,numerical coordination numbers 2 and 3 are deemed suitable for the Tamusu mudstone.The proposed model is effective as a tool for constructing and simulating fluid flow in the Tamusu mudstone.展开更多
Studying immiscible fluid displacement patterns can provide a better understanding of displacement processes within heterogeneous porous media,thereby helping improving oil recovery and optimizing geological CO_(2) se...Studying immiscible fluid displacement patterns can provide a better understanding of displacement processes within heterogeneous porous media,thereby helping improving oil recovery and optimizing geological CO_(2) sequestration.As the injection rate of water displacing oil increases and the displacement pattern transits from capillary fingering to viscous fingering,there is a broad crossover zone between the two that can adversely affect the oil displacement efficiency.While previous studies have utilized phase diagrams to investigate the influence of the viscosity ratio and wettability of the crossover zone,fewer have studied the impact of rock heterogeneity.In this study,we created pore network models with varying degrees of heterogeneity to simulate water flooding at different injection rates.Our model quantifies capillary and viscous fingering characteristics while investigating porous media heterogeneity's role in the crossover zone.Analysis of simulation results reveals that a higher characteristic front flow rate within the crossover zone leads to earlier breakthrough and reduced displacement efficiency.Increased heterogeneity in the porous media raises injection-site pressure,lowers water saturation,and elevates the characteristic front flow rate,thereby expanding the extent of crossover zone.展开更多
Clayey-silt natural gas hydrate reservoirs in the South China Sea exhibit loose and unconsolidated structures, heterogeneous pore structures, high clay mineral contents, and strong hydrophilicity. These characteristic...Clayey-silt natural gas hydrate reservoirs in the South China Sea exhibit loose and unconsolidated structures, heterogeneous pore structures, high clay mineral contents, and strong hydrophilicity. These characteristics complicate the gas-water two-phase flow process in porous media following hydrate decomposition, posing challenges for efficient development. This study examines the transport response of clayey-silt reservoir samples from the Shenhu area using gas-water two-phase flow experiments and CT scanning to explore changes in pore structure, gas-water distribution, and relative permeability under varying flow conditions. The results indicate that pore heterogeneity significantly influences flow characteristics. Gas preferentially displaces water in larger pores, forming fracture-like pores, which serve as preferential flow channels for gas migration. The preferential flow channels enhance gas-phase permeability up to 19 times that of the water phase when fluid pressures exceed total stresses. However,small pores retain liquid, leading to a high residual water saturation of 0.561. CT imaging reveals that these hydro-fractures improve gas permeability but also confine gas flow to specific channels. Pore network analysis shows that gas injection expands the pore-throat network, enhancing connectivity and forming fracture-like pores. Residual water remains trapped in smaller pores and throats, while structural changes, including new fractures, improve gas flow pathways and overall connectivity. Relative permeability curves demonstrate a narrow gas-water cocurrent-flow zone, a right-shifted iso-permeability point and high reservoir capillary pressure, indicating a strong "water-blocking" effect. The findings suggest that optimizing reservoir stimulation techniques to enhance fracture formation, reduce residual water saturation, and improve gas flow capacity is critical for efficient hydrate reservoir development.展开更多
Software defect prediction plays a critical role in software development and quality assurance processes. Effective defect prediction enables testers to accurately prioritize testing efforts and enhance defect detecti...Software defect prediction plays a critical role in software development and quality assurance processes. Effective defect prediction enables testers to accurately prioritize testing efforts and enhance defect detection efficiency. Additionally, this technology provides developers with a means to quickly identify errors, thereby improving software robustness and overall quality. However, current research in software defect prediction often faces challenges, such as relying on a single data source or failing to adequately account for the characteristics of multiple coexisting data sources. This approach may overlook the differences and potential value of various data sources, affecting the accuracy and generalization performance of prediction results. To address this issue, this study proposes a multivariate heterogeneous hybrid deep learning algorithm for defect prediction (DP-MHHDL). Initially, Abstract Syntax Tree (AST), Code Dependency Network (CDN), and code static quality metrics are extracted from source code files and used as inputs to ensure data diversity. Subsequently, for the three types of heterogeneous data, the study employs a graph convolutional network optimization model based on adjacency and spatial topologies, a Convolutional Neural Network-Bidirectional Long Short-Term Memory (CNN-BiLSTM) hybrid neural network model, and a TabNet model to extract data features. These features are then concatenated and processed through a fully connected neural network for defect prediction. Finally, the proposed framework is evaluated using ten promise defect repository projects, and performance is assessed with three metrics: F1, Area under the curve (AUC), and Matthews correlation coefficient (MCC). The experimental results demonstrate that the proposed algorithm outperforms existing methods, offering a novel solution for software defect prediction.展开更多
Subsurface rocks,as complex porous media,exhibit multiscale pore structures and intricate physical properties.Digital rock physics technology has become increasingly influential in the study of subsurface rock propert...Subsurface rocks,as complex porous media,exhibit multiscale pore structures and intricate physical properties.Digital rock physics technology has become increasingly influential in the study of subsurface rock properties.Given the multiscale characteristics of rock pore structures,direct three-dimensional imaging at sub-micrometer and nanometer scales is typically infeasible.This study introduces a method for reconstructing porous media using multidimensional data,which combines one-dimensional pore structure parameters with two-dimensional images to reconstruct three-dimensional models.The pore network model(PNM)is stochastically reconstructed using one-dimensional parameters,and a generative adversarial network(GAN)is utilized to equip the PNM with pore morphologies derived from two-dimensional images.The digital rocks generated by this method possess excellent controllability.Using Berea sandstone and Grosmont carbonate samples,we performed digital rock reconstructions based on PNM extracted by the maximum ball algorithm and compared them with stochastically reconstructed PNM.Pore structure parameters,permeability,and formation factors were calculated.The results show that the generated samples exhibit good consistency with real samples in terms of pore morphology,pore structure,and physical properties.Furthermore,our method effectively supplements the micropores not captured in CT images,demonstrating its potential in multiscale carbonate samples.Thus,the proposed reconstruction method is promising for advancing porous media property research.展开更多
Firstly,based on the data of air quality and the meteorological data in Baoding City from 2017 to 2021,the correlations of meteorological elements and pollutants with O_(3)concentration were explored to determine the ...Firstly,based on the data of air quality and the meteorological data in Baoding City from 2017 to 2021,the correlations of meteorological elements and pollutants with O_(3)concentration were explored to determine the forecast factors of forecast models.Secondly,the O_(3)-8h concentration in Baoding City in 2021 was predicted based on the constructed models of multiple linear regression(MLR),backward propagation neural network(BPNN),and auto regressive integrated moving average(ARIMA),and the predicted values were compared with the observed values to test their prediction effects.The results show that overall,the MLR,BPNN and ARIMA models were able to forecast the changing trend of O_(3)-8h concentration in Baoding in 2021,but the BPNN model gave better forecast results than the ARIMA and MLR models,especially for the prediction of the high values of O_(3)-8h concentration,and the correlation coefficients between the predicted values and the observed values were all higher than 0.9 during June-September.The mean error(ME),mean absolute error(MAE),and root mean square error(RMSE)of the predicted values and the observed values of daily O_(3)-8h concentration based on the BPNN model were 0.45,19.11 and 24.41μg/m 3,respectively,which were significantly better than those of the MLR and ARIMA models.The prediction effects of the MLR,BPNN and ARIMA models were the best at the pollution level,followed by the excellent level,and it was the worst at the good level.In comparison,the prediction effect of BPNN model was better than that of the MLR and ARIMA models as a whole,especially for the pollution and excellent levels.The TS scores of the BPNN model were all above 66%,and the PC values were above 86%.The BPNN model can forecast the changing trend of O_(3)concentration more accurately,and has a good practical application value,but at the same time,the predicted high values of O_(3)concentration should be appropriately increased according to error characteristics of the model.展开更多
Disintegrated dolomite slope and tunnel disasters occur frequently due to poor water stability of disintegrated dolomite,primarily in a form of seepage failure.For engineering purposes,it is critical to determine the ...Disintegrated dolomite slope and tunnel disasters occur frequently due to poor water stability of disintegrated dolomite,primarily in a form of seepage failure.For engineering purposes,it is critical to determine the seepage properties of disintegrated dolomite within the strata.However,conventional experimental methods are time-consuming and expensive and may not be effective in investigating seepage characteristics due to the heterogeneity of disintegrated dolomite.In this study,pore network model(PNM)was established by the computerized tomography(CT)scanning technology to characterize the pores.Meanwhile,the seepage and coefficient of permeability under different inlet stress conditions based on the accurate pore model were realized by linking the commercial image processing software Avizo with the commercial multi-physics modeling package Comsol.The results show that the porosities of severely and completely disintegrated dolomites are 29.17% and 45.37%,respectively.The grade of pore development increases with disintegration grade,which facilitates seepage failure.Severely and completely disintegrated dolomites have the coefficients of permeability of 9.67×10^(-7) m/s and 1.61×10^(-6) m/s,respectively.Under conventional conditions,severely and completely disintegrated dolomites undergo seepage failure above a pressure difference of 6×10^(3) Pa and 5×10^(3) Pa,respectively.These results are consistent with both in situ water pressure tests in the borehole and laboratory tests with the constant-head method,demonstrating that CT scanning is an effective method for observing fractures and pores in disintegrated dolomite for seepage evaluation.展开更多
Underwater imaging is frequently influenced by factors such as illumination,scattering,and refraction,which can result in low image contrast and blurriness.Moreover,the presence of numerous small,overlapping targets r...Underwater imaging is frequently influenced by factors such as illumination,scattering,and refraction,which can result in low image contrast and blurriness.Moreover,the presence of numerous small,overlapping targets reduces detection accuracy.To address these challenges,first,green channel images are preprocessed to rectify color bias while improving contrast and clarity.Se-cond,the YOLO-DBS network that employs deformable convolution is proposed to enhance feature learning from underwater blurry images.The ECA attention mechanism is also introduced to strengthen feature focus.Moreover,a bidirectional feature pyramid net-work is utilized for efficient multilayer feature fusion while removing nodes that contribute minimally to detection performance.In addition,the SIoU loss function that considers factors such as angular error and distance deviation is incorporated into the network.Validation on the RUOD dataset demonstrates that YOLO-DBS achieves approximately 3.1%improvement in mAP@0.5 compared with YOLOv8n and surpasses YOLOv9-tiny by 1.3%.YOLO-DBS reduces parameter count by 32%relative to YOLOv8n,thereby demonstrating superior performance in real-time detection on underwater observation platforms.展开更多
基金supported by the National Natural Science Foundation of China(Grant Nos.12202339 and 12172273)Xi’an Jiaotong University Tang Scholar.
文摘Current hyperelastic constitutive models of hydrogels face difficulties in capturing the stress-strain behaviors of hydrogels under extremely large deformation because the effect of non-affine deformation of the polymer network inside is ambiguous.In this work,we construct periodic random network(PRN)models for the effective polymer network in hydrogels and investigate the non-affine deformation of polymer chains intrinsically originates from the structural randomness from bottom up.The non-affine deformation in PRN models is manifested as the actual stretch of polymer chains randomly deviated from the chain stretch predicted by affine assumption,and quantified by a non-affine ratio of each polymer chain.It is found that the non-affine ratios of polymer chains are closely related to bulk deformation state,chain orientation,and initial chain elongation.By fitting the non-affine ratio of polymer chains in all PRN models,we propose a non-affine constitutive model for the hydrogel polymer network based on micro-sphere model.The stress-strain curves of the proposed constitutive models under uniaxial tension condition agree with the simulation results of different PRN models of hydrogels very well.
基金supported by the National Natural Science Foundation of China Basic Science Center Program for“Multiscale Problems in Nonlinear Mechanics”(Grant No.11988102)the National Natural Science Foundation of China(Grant Nos.92252203,12102439,and 12425207)+1 种基金the Chinese Academy of Sciences Project for Young Scientists in Basic Research(Grant No.YSBR-087)the Strategic Priority Research Program of Chinese Academy of Sciences(Grant No.XDB0620102).
文摘The empirical models for wavenumber-frequency spectra of wall pressure are broadly used in the fast prediction of aerodynamic and hydrodynamic noise.However,it needs to fit the parameter using massive data and is only used for limited cases.In this letter,we propose Kolmogorov-Arnold networks(KAN)base models for wavenumber-frequency spectra of pressure fluctuations under turbulent boundary layers.The results are compared with DNS results.In turbulent channel flows,it is found that the KAN base model leads to a smooth wavenumber-frequency spectrum with sparse samples.In the turbulent flow over an axisymmetric body of revolution,the KAN base model captures the wavenumber-frequency spectra near the convective peak.
基金supported by the National Key Research and Development Program of China(Grant No.2022YFC3080200)the National Natural Science Foundation of China(Grant No.42022053)the China Postdoctoral Science Foundation(Grant No.2023M731264).
文摘Natural slopes usually display complicated exposed rock surfaces that are characterized by complex and substantial terrain undulation and ubiquitous undesirable phenomena such as vegetation cover and rockfalls.This study presents a systematic outcrop research of fracture pattern variations in a complicated rock slope,and the qualitative and quantitative study of the complex phenomena impact on threedimensional(3D)discrete fracture network(DFN)modeling.As the studies of the outcrop fracture pattern have been so far focused on local variations,thus,we put forward a statistical analysis of global variations.The entire outcrop is partitioned into several subzones,and the subzone-scale variability of fracture geometric properties is analyzed(including the orientation,the density,and the trace length).The results reveal significant variations in fracture characteristics(such as the concentrative degree,the average orientation,the density,and the trace length)among different subzones.Moreover,the density of fracture sets,which is approximately parallel to the slope surface,exhibits a notably higher value compared to other fracture sets across all subzones.To improve the accuracy of the DFN modeling,the effects of three common phenomena resulting from vegetation and rockfalls are qualitatively analyzed and the corresponding quantitative data processing solutions are proposed.Subsequently,the 3D fracture geometric parameters are determined for different areas of the high-steep rock slope in terms of the subzone dimensions.The results show significant variations in the same set of 3D fracture parameters across different regions with density differing by up to tenfold and mean trace length exhibiting differences of 3e4 times.The study results present precise geological structural information,improve modeling accuracy,and provide practical solutions for addressing complex outcrop issues.
基金supported by National Natural Science Foundation of China(Grant No.42172159)Science Foundation of China University of Petroleum,Beijing(Grant No.2462023XKBH002).
文摘Multiphase flow in low permeability porous media is involved in numerous energy and environmental applications.However,a complete description of this process is challenging due to the limited modeling scale and the effects of complex pore structures and wettability.To address this issue,based on the digital rock of low permeability sandstone,a direct numerical simulation is performed considering the interphase drag and boundary slip to clarify the microscopic water-oil displacement process.In addition,a dual-porosity pore network model(PNM)is constructed to obtain the water-oil relative permeability of the sample.The displacement efficiency as a recovery process is assessed under different wetting and pore structure properties.Results show that microscopic displacement mechanisms explain the corresponding macroscopic relative permeability.The injected water breaks through the outlet earlier with a large mass flow,while thick oil films exist in rough hydrophobic surfaces and poorly connected pores.The variation of water-oil relative permeability is significant,and residual oil saturation is high in the oil-wet system.The flooding is extensive,and the residual oil is trapped in complex pore networks for hydrophilic pore surfaces;thus,water relative permeability is lower in the water-wet system.While the displacement efficiency is the worst in mixed-wetting systems for poor water connectivity.Microporosity negatively correlates with invading oil volume fraction due to strong capillary resistance,and a large microporosity corresponds to low residual oil saturation.This work provides insights into the water-oil flow from different modeling perspectives and helps to optimize the development plan for enhanced recovery.
基金This work was supported by the National Natural Science Foundation of China(52204206,52274246)the Open Fund Project Funded by State Key Laboratory of Gas Disaster Detecting,Preventing and Emergency Controlling(2021SKLFF03)the Natural Science Foundation of Chongqing(cstc2021jcyj-msxmX1149).
文摘Coalbed methane(CBM)recovery is attracting global attention due to its huge reserve and low carbon burning benefits for the environment.Fully understanding the complex structure of coal and its transport properties is crucial for CBM development.This study describes the implementation of mercury intrusion and μ-CT techniques for quantitative analysis of 3D pore structure in two anthracite coals.It shows that the porosity is 7.04%-8.47%and 10.88%-12.11%,and the pore connectivity is 0.5422-0.6852 and 0.7948-0.9186 for coal samples 1 and 2,respectively.The fractal dimension and pore geometric tortuosity were calculated based on the data obtained from 3D pore structure.The results show that the pore structure of sample 2 is more complex and developed,with lower tortuosity,indicating the higher fluid deliverability of pore system in sample 2.The tortuosity in three-direction is significantly different,indicating that the pore structure of the studied coals has significant anisotropy.The equivalent pore network model(PNM)was extracted,and the anisotropic permeability was estimated by PNM gas flow simulation.The results show that the anisotropy of permeability is consistent with the slice surface porosity distribution in 3D pore structure.The permeability in the horizontal direction is much greater than that in the vertical direction,indicating that the dominant transportation channel is along the horizontal direction of the studied coals.The research results achieve the visualization of the 3D complex structure of coal and fully capture and quantify pore size,connectivity,curvature,permeability,and its anisotropic characteristics at micron-scale resolution.This provides a prerequisite for the study of mass transfer behaviors and associated transport mechanisms in real pore structures.
基金sponsored by the General Program of the National Natural Science Foundation of China(Grant Nos.52079129 and 52209148)the Hubei Provincial General Fund,China(Grant No.2023AFB567)。
文摘Analyzing rock mass seepage using the discrete fracture network(DFN)flow model poses challenges when dealing with complex fracture networks.This paper presents a novel DFN flow model that incorporates the actual connections of large-scale fractures.Notably,this model efficiently manages over 20,000 fractures without necessitating adjustments to the DFN geometry.All geometric analyses,such as identifying connected fractures,dividing the two-dimensional domain into closed loops,triangulating arbitrary loops,and refining triangular elements,are fully automated.The analysis processes are comprehensively introduced,and core algorithms,along with their pseudo-codes,are outlined and explained to assist readers in their programming endeavors.The accuracy of geometric analyses is validated through topological graphs representing the connection relationships between fractures.In practical application,the proposed model is employed to assess the water-sealing effectiveness of an underground storage cavern project.The analysis results indicate that the existing design scheme can effectively prevent the stored oil from leaking in the presence of both dense and sparse fractures.Furthermore,following extensive modification and optimization,the scale and precision of model computation suggest that the proposed model and developed codes can meet the requirements of engineering applications.
基金This project is sponsored by National Natural Science Foundation of China, No. 40574030.
文摘Based on the percolation network model characterizing reservoir rock＇s pore structure and fluid characteristics, this paper qualitatively studies the effects of pore size, pore shape, pore connectivity, and the amount of micropores on the I - Sw curve using numerical modeling. The effects of formation water salinity on the electrical resistivity of the rock are discussed. Then the relative magnitudes of the different influencing factors are discussed. The effects of the different factors on the I - Sw curve are analyzed by fitting simulation results. The results show that the connectivity of the void spaces and the amount of micropores have a large effect on the I - S, curve, while the other factors have little effect. The formation water salinity has a large effect on the absolute resistivity values. The non-Archie phenomenon is prevalent, which is remarkable in rocks with low permeability.
基金National Natural Science Foundation of China(11971211,12171388).
文摘Complex network models are frequently employed for simulating and studyingdiverse real-world complex systems.Among these models,scale-free networks typically exhibit greater fragility to malicious attacks.Consequently,enhancing the robustness of scale-free networks has become a pressing issue.To address this problem,this paper proposes a Multi-Granularity Integration Algorithm(MGIA),which aims to improve the robustness of scale-free networks while keeping the initial degree of each node unchanged,ensuring network connectivity and avoiding the generation of multiple edges.The algorithm generates a multi-granularity structure from the initial network to be optimized,then uses different optimization strategies to optimize the networks at various granular layers in this structure,and finally realizes the information exchange between different granular layers,thereby further enhancing the optimization effect.We propose new network refresh,crossover,and mutation operators to ensure that the optimized network satisfies the given constraints.Meanwhile,we propose new network similarity and network dissimilarity evaluation metrics to improve the effectiveness of the optimization operators in the algorithm.In the experiments,the MGIA enhances the robustness of the scale-free network by 67.6%.This improvement is approximately 17.2%higher than the optimization effects achieved by eight currently existing complex network robustness optimization algorithms.
基金supported by the Hebei Provincial Key Research and Development Project(21372803D)。
文摘[Objective]To construct an Escherichia coli mutant strain that accumulates pyruvate by genetic modification guided by the genome-scale metabolic network model.[Methods]Using a genome-scale metabolic network model as a guide,we simulated pyruvate production of E.coli,screened key genes in metabolic pathways,and developed gene editing procedures accordingly.We knocked out the acetate kinase gene ackA,phosphate acetyltransferase gene pta,alcohol dehydrogenase adhE,glycogen synthase gene glgA,glycogen phosphorylase gene glgP,phosphoribosyl pyrophosphate(PRPP)synthase gene prs,ribose 1,5-bisphosphate phosphokinase gene phnN,and transporter encoding gene proP.Furthermore,we knocked in the transporter encoding gene ompC,flavonoid toxin gene fldA,and D-serine ammonia lyase gene dsdA.[Results]A shake flask process with the genetically edited mutant strain MG1655-6-2 under anaerobic conditions produced pyruvate at a titer of 10.46 g/L and a yield of 0.69 g/g.Metabolomic analysis revealed a significant increase in the pyruvate level in the fermentation broth,accompanied by notable decreases in the levels of certain related metabolic byproducts.Through 5 L fed-batch fermentation and an adaptive laboratory evolution,the strain finally achieved a pyruvate titer of 45.86 g/L.[Conclusion]This study illustrated the efficacy of a gene editing strategy predicted by a genome-scale metabolic network model in enhancing pyruvate accumulation in E.coli under anaerobic conditions and provided novel insights for microbial metabolic engineering.
基金The National Key R&D Program,Grant/Award Number:2023YFC2907203National Natural Science Foundation of China,Grant/Award Numbers:52374121,52074121。
文摘The flow characteristics of coalbed methane(CBM)are influenced by the coal rock fracture network,which serves as the primary gas transport channel.This has a significant effect on the permeability performance of coal reservoirs.In any case,the traditional techniques of coal rock fracture observation are unable to precisely define the flow of CBM.In this study,coal samples were subjected to an in situ loading scanning test in order to create a pore network model(PNM)and determine the pore and fracture dynamic evolution law of the samples in the loading path.On this basis,the structural characteristic parameters of the samples were extracted from the PNM and the impact on the permeability performance of CBM was assessed.The findings demonstrate that the coal samples'internal porosity increases by 2.039%under uniaxial loading,the average throat pore radius increases by 205.5 to 36.1μm,and the loading has an impact on the distribution and morphology of the pores in the coal rock.The PNM was loaded into the finite element program COMSOL for seepage modeling,and the M3 stage showed isolated pore connectivity to produce microscopic fissures,which could serve as seepage channels.In order to confirm the viability of the PNM and COMSOL docking technology,the streamline distribution law of pressure and velocity fields during the coal sample loading process was examined.The absolute permeability of the coal samples was also obtained in order for comparison with the measured results.The macroscopic CBM flow mechanism in complex lowpermeability coal rocks can be revealed through three-dimensional reconstruction of the microscopic fracture structure and seepage simulation.This study lays the groundwork for the fine description and evaluation of coal reservoirs as well as the precise prediction of gas production in CBM wells.
基金support of State Grid Corporation of China Project:Research on key tech-nologies of automatic generation of typical power grid operation modes and automatic calculation of section stability limits(5100-202355420A-3-2-ZN).
文摘Ensuring stable frequency and voltage has recently become increasingly challenging for modern power systems. This is primarily due to the fluctuating and intermittent nature of renewable energy sources and the uncertain electricity demand. To address these issues, this study proposes a load resource management(LRM) method to cope with the sudden power disturbances. The LRM method supports primary frequency and voltage regulation, and its integration with network dynamics minimizes the established disutility function caused by load participation. For better control performance, a non-dominated sorting genetic algorithm-Ⅱ(NSGA-Ⅱ)-based gain-tuning procedure was utilized for LRM, aiming to enhance the frequency/voltage nadir, reduce the frequency/voltage steady-state error, and minimize the total load control efforts. To validate the effectiveness of the proposed approach, comparative experiments were conducted with three load–resource management technologies for primary regulation auxiliary services in MATLAB/Simulink. Compared to the conventional optimal load control or using LRM alone, the improved NSGA-Ⅱ-based LRM demonstrates superior performance. It achieves better frequency response, voltage transients, and steady-state responses, while also considering disutility.
基金supported by the National Natural Science Foundation of China(No.52005378)the opening project fund of Materials Service Safety Assessment Facilities(No.MSAF-2021-107).
文摘The press-fit connector is a typical plug-and-play solderless connection,and it is widely used in signal transmission in fields such as communication and automotive devices.This paper focuses on inverse designing and optimization of geometric structure,as well as insertion-withdrawal forces of press-fit connector using artificial neural network(ANN)-assisted optimization method.The ANN model is established to approximate the relationship between geometric parameters and insertion-withdrawal forces,of which hyper-parameters of neural network are optimized to improve model performance.Two numerical methods are proposed for inverse designing structural parameters(Model-I)and multi-objective optimization of insertion-withdrawal forces(Model-II)of press-fit connector.In Model-I,a method for inverse designing structure parameters is established,of which an ANN model is coupled with single-objective optimization algorithm.The objective function is established,the inverse problem is solved,and effectiveness is verified.In Model-II,a multi-objective optimization method is proposed,of which an ANN model is coupled with genetic algorithm.The Pareto solution sets of insertion-withdrawal forces are obtained,and results are analyzed.The established ANN-coupled numerical optimization methods are beneficial for improving the design efficiency,and enhancing the connection reliability of the press-fit connector.
基金support of the National Natural Science Foundation of China(Grant Nos.42377179,U22A20595,12202463).
文摘Accurately characterizing the pore structure of Tamusu mudstone and simulating fluid flow within it are crucial for assessing underground disposal of high-level radioactive waste(HLW)in this formation.Modeling mudstone presents challenges due to its multiscale pore structure and the necessity that accounts for the effects of high clay content.A method for constructing a dual-scale pore network model(PNM)for the Tamusu mudstone,which considers the hydrological expansion of clays,was proposed.This model integrates N2 adsorption data with focused ion beam/scanning electron microscopy(FIB/SEM)images and labels pores based on clay content.Simulations of single-phase flow were conducted to validate the proposed model.Additionally,the influences of cell number,connectivity,slip effects,and clay minerals on permeability were examined.The findings indicate that a configuration of 45×45×45 cells adequately represents the model.The permeability of the Tamusu mudstone,about 1020 m^(2),aligns with the experimental values.During the simulation,Knudsen diffusion is considered.Factors such as increased roughness,tortuosity,clay content,and water film thickness decrease the permeability,whereas increased connectivity enhances permeability.In the model,numerical coordination numbers 2 and 3 are deemed suitable for the Tamusu mudstone.The proposed model is effective as a tool for constructing and simulating fluid flow in the Tamusu mudstone.
基金supported by the Research and Innovation Fund for Graduate Students of Southwest Petroleum University(No.2022KYCX027)supported by the National Natural Science Foundation for Youth Grant(No.41902157).
文摘Studying immiscible fluid displacement patterns can provide a better understanding of displacement processes within heterogeneous porous media,thereby helping improving oil recovery and optimizing geological CO_(2) sequestration.As the injection rate of water displacing oil increases and the displacement pattern transits from capillary fingering to viscous fingering,there is a broad crossover zone between the two that can adversely affect the oil displacement efficiency.While previous studies have utilized phase diagrams to investigate the influence of the viscosity ratio and wettability of the crossover zone,fewer have studied the impact of rock heterogeneity.In this study,we created pore network models with varying degrees of heterogeneity to simulate water flooding at different injection rates.Our model quantifies capillary and viscous fingering characteristics while investigating porous media heterogeneity's role in the crossover zone.Analysis of simulation results reveals that a higher characteristic front flow rate within the crossover zone leads to earlier breakthrough and reduced displacement efficiency.Increased heterogeneity in the porous media raises injection-site pressure,lowers water saturation,and elevates the characteristic front flow rate,thereby expanding the extent of crossover zone.
基金the National Natural Science Foundation of China (Nos. 42302143, 42172159)China Geological Survey Project (No. DD20211350)support from the G. Albert Shoemaker endowment
文摘Clayey-silt natural gas hydrate reservoirs in the South China Sea exhibit loose and unconsolidated structures, heterogeneous pore structures, high clay mineral contents, and strong hydrophilicity. These characteristics complicate the gas-water two-phase flow process in porous media following hydrate decomposition, posing challenges for efficient development. This study examines the transport response of clayey-silt reservoir samples from the Shenhu area using gas-water two-phase flow experiments and CT scanning to explore changes in pore structure, gas-water distribution, and relative permeability under varying flow conditions. The results indicate that pore heterogeneity significantly influences flow characteristics. Gas preferentially displaces water in larger pores, forming fracture-like pores, which serve as preferential flow channels for gas migration. The preferential flow channels enhance gas-phase permeability up to 19 times that of the water phase when fluid pressures exceed total stresses. However,small pores retain liquid, leading to a high residual water saturation of 0.561. CT imaging reveals that these hydro-fractures improve gas permeability but also confine gas flow to specific channels. Pore network analysis shows that gas injection expands the pore-throat network, enhancing connectivity and forming fracture-like pores. Residual water remains trapped in smaller pores and throats, while structural changes, including new fractures, improve gas flow pathways and overall connectivity. Relative permeability curves demonstrate a narrow gas-water cocurrent-flow zone, a right-shifted iso-permeability point and high reservoir capillary pressure, indicating a strong "water-blocking" effect. The findings suggest that optimizing reservoir stimulation techniques to enhance fracture formation, reduce residual water saturation, and improve gas flow capacity is critical for efficient hydrate reservoir development.
文摘Software defect prediction plays a critical role in software development and quality assurance processes. Effective defect prediction enables testers to accurately prioritize testing efforts and enhance defect detection efficiency. Additionally, this technology provides developers with a means to quickly identify errors, thereby improving software robustness and overall quality. However, current research in software defect prediction often faces challenges, such as relying on a single data source or failing to adequately account for the characteristics of multiple coexisting data sources. This approach may overlook the differences and potential value of various data sources, affecting the accuracy and generalization performance of prediction results. To address this issue, this study proposes a multivariate heterogeneous hybrid deep learning algorithm for defect prediction (DP-MHHDL). Initially, Abstract Syntax Tree (AST), Code Dependency Network (CDN), and code static quality metrics are extracted from source code files and used as inputs to ensure data diversity. Subsequently, for the three types of heterogeneous data, the study employs a graph convolutional network optimization model based on adjacency and spatial topologies, a Convolutional Neural Network-Bidirectional Long Short-Term Memory (CNN-BiLSTM) hybrid neural network model, and a TabNet model to extract data features. These features are then concatenated and processed through a fully connected neural network for defect prediction. Finally, the proposed framework is evaluated using ten promise defect repository projects, and performance is assessed with three metrics: F1, Area under the curve (AUC), and Matthews correlation coefficient (MCC). The experimental results demonstrate that the proposed algorithm outperforms existing methods, offering a novel solution for software defect prediction.
基金supported by the Shandong Provincial Natural Science Foundation(ZR2024MD116)National Natural Science Foundation of China(Grant Nos.42174143,42004098)Technology Innovation Leading Program of Shaanxi(No.2024 ZC-YYDP-27).
文摘Subsurface rocks,as complex porous media,exhibit multiscale pore structures and intricate physical properties.Digital rock physics technology has become increasingly influential in the study of subsurface rock properties.Given the multiscale characteristics of rock pore structures,direct three-dimensional imaging at sub-micrometer and nanometer scales is typically infeasible.This study introduces a method for reconstructing porous media using multidimensional data,which combines one-dimensional pore structure parameters with two-dimensional images to reconstruct three-dimensional models.The pore network model(PNM)is stochastically reconstructed using one-dimensional parameters,and a generative adversarial network(GAN)is utilized to equip the PNM with pore morphologies derived from two-dimensional images.The digital rocks generated by this method possess excellent controllability.Using Berea sandstone and Grosmont carbonate samples,we performed digital rock reconstructions based on PNM extracted by the maximum ball algorithm and compared them with stochastically reconstructed PNM.Pore structure parameters,permeability,and formation factors were calculated.The results show that the generated samples exhibit good consistency with real samples in terms of pore morphology,pore structure,and physical properties.Furthermore,our method effectively supplements the micropores not captured in CT images,demonstrating its potential in multiscale carbonate samples.Thus,the proposed reconstruction method is promising for advancing porous media property research.
基金the Project of the Key Open Laboratory of Atmospheric Detection,China Meteorological Administration(2023KLAS02M)the Second Batch of Science and Technology Project of China Meteorological Administration("Jiebangguashuai"):the Research and Development of Short-term and Near-term Warning Products for Severe Convective Weather in Beijing-Tianjin-Hebei Region(CMAJBGS202307).
文摘Firstly,based on the data of air quality and the meteorological data in Baoding City from 2017 to 2021,the correlations of meteorological elements and pollutants with O_(3)concentration were explored to determine the forecast factors of forecast models.Secondly,the O_(3)-8h concentration in Baoding City in 2021 was predicted based on the constructed models of multiple linear regression(MLR),backward propagation neural network(BPNN),and auto regressive integrated moving average(ARIMA),and the predicted values were compared with the observed values to test their prediction effects.The results show that overall,the MLR,BPNN and ARIMA models were able to forecast the changing trend of O_(3)-8h concentration in Baoding in 2021,but the BPNN model gave better forecast results than the ARIMA and MLR models,especially for the prediction of the high values of O_(3)-8h concentration,and the correlation coefficients between the predicted values and the observed values were all higher than 0.9 during June-September.The mean error(ME),mean absolute error(MAE),and root mean square error(RMSE)of the predicted values and the observed values of daily O_(3)-8h concentration based on the BPNN model were 0.45,19.11 and 24.41μg/m 3,respectively,which were significantly better than those of the MLR and ARIMA models.The prediction effects of the MLR,BPNN and ARIMA models were the best at the pollution level,followed by the excellent level,and it was the worst at the good level.In comparison,the prediction effect of BPNN model was better than that of the MLR and ARIMA models as a whole,especially for the pollution and excellent levels.The TS scores of the BPNN model were all above 66%,and the PC values were above 86%.The BPNN model can forecast the changing trend of O_(3)concentration more accurately,and has a good practical application value,but at the same time,the predicted high values of O_(3)concentration should be appropriately increased according to error characteristics of the model.
基金supported by the National Natural Science Foundation of China(Grant No.42162026)the Basic Research Program in Yunnan Province,China(Grant No.202401AT070328)the Young Talents Project of“Xingdian Talent Support Program"in Yunnan Province,China(Grant No.YNWR-QNBJ-2020-019).
文摘Disintegrated dolomite slope and tunnel disasters occur frequently due to poor water stability of disintegrated dolomite,primarily in a form of seepage failure.For engineering purposes,it is critical to determine the seepage properties of disintegrated dolomite within the strata.However,conventional experimental methods are time-consuming and expensive and may not be effective in investigating seepage characteristics due to the heterogeneity of disintegrated dolomite.In this study,pore network model(PNM)was established by the computerized tomography(CT)scanning technology to characterize the pores.Meanwhile,the seepage and coefficient of permeability under different inlet stress conditions based on the accurate pore model were realized by linking the commercial image processing software Avizo with the commercial multi-physics modeling package Comsol.The results show that the porosities of severely and completely disintegrated dolomites are 29.17% and 45.37%,respectively.The grade of pore development increases with disintegration grade,which facilitates seepage failure.Severely and completely disintegrated dolomites have the coefficients of permeability of 9.67×10^(-7) m/s and 1.61×10^(-6) m/s,respectively.Under conventional conditions,severely and completely disintegrated dolomites undergo seepage failure above a pressure difference of 6×10^(3) Pa and 5×10^(3) Pa,respectively.These results are consistent with both in situ water pressure tests in the borehole and laboratory tests with the constant-head method,demonstrating that CT scanning is an effective method for observing fractures and pores in disintegrated dolomite for seepage evaluation.
基金funded by the Jilin City Science and Technology Innovation Development Plan Project(No.20240302014)the Jilin Provincial Department of Educa-tion Science and Technology Research Project(No.JJKH 20250879KJ)the Jilin Province Science and Tech-nology Development Plan Project(No.YDZJ202401640 ZYTS).
文摘Underwater imaging is frequently influenced by factors such as illumination,scattering,and refraction,which can result in low image contrast and blurriness.Moreover,the presence of numerous small,overlapping targets reduces detection accuracy.To address these challenges,first,green channel images are preprocessed to rectify color bias while improving contrast and clarity.Se-cond,the YOLO-DBS network that employs deformable convolution is proposed to enhance feature learning from underwater blurry images.The ECA attention mechanism is also introduced to strengthen feature focus.Moreover,a bidirectional feature pyramid net-work is utilized for efficient multilayer feature fusion while removing nodes that contribute minimally to detection performance.In addition,the SIoU loss function that considers factors such as angular error and distance deviation is incorporated into the network.Validation on the RUOD dataset demonstrates that YOLO-DBS achieves approximately 3.1%improvement in mAP@0.5 compared with YOLOv8n and surpasses YOLOv9-tiny by 1.3%.YOLO-DBS reduces parameter count by 32%relative to YOLOv8n,thereby demonstrating superior performance in real-time detection on underwater observation platforms.
