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
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
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
Ulcerative colitis, an inflammatory bowel disease, is a chronic inflammatory disorder that results in ulcers of the colon and rectum without known etiology. Ulcerative colitis causes a huge public health care burden p...Ulcerative colitis, an inflammatory bowel disease, is a chronic inflammatory disorder that results in ulcers of the colon and rectum without known etiology. Ulcerative colitis causes a huge public health care burden particularly in developed countries. Many studies suggest that ulcerative colitis results from an abnormal immune response against components of cornrnensal rnicrobiota in genetically susceptible individuals. However, understanding of the disease mechanisms at cellular and molecular levels remains largely elusive. In this paper, a network model is developed based on our previous study and computer simulations are perforrned using an agent-based network modeling to elucidate the dynamics of immune response in ulcerative colitis progression. Our modeling study identifies several important positive feedback loops as a driving force for ulcerative colitis initiation and progression. The results demonstrate that although immune response in ulcerative colitis patients is dominated by anti-inflarnrnatory/regulatory cells such as alternatively activated rnacrophages and type II natural killer T cells, proinflarnrnatory cells including classically activated rnacrophages, T helper 1 and T helper 17 cells, and their secreted cytokines tumor necrosis factor-α, interleukin-12, interleukin-23, interleukin-17 and interferon-γ remain at certain levels （lower than those in Crohn＇s disease, another inflammatory bowel disease）. Long-terrn exposure to these proinflarnrnatory components, causes rnucosal tissue damage persistently, leading to ulcerative colitis. Our simulation results are qualitatively in agreement with clinical and laboratory measurements, offering novel insight into the disease mechanisms.展开更多
Chronic obstructive pulmonary disease(COPD) is a chronic inflammatory disorder characterized by airflow obstruction and progressive damage of lung tissues. As currently more than 3 billion people use biomass fuel for ...Chronic obstructive pulmonary disease(COPD) is a chronic inflammatory disorder characterized by airflow obstruction and progressive damage of lung tissues. As currently more than 3 billion people use biomass fuel for cooking and heating worldwide, exposure to biomass smoke(BS) is recognized as a significant risk factor for COPD. Recent clinical data have shown that BS-COPD patients have a Th2-type inflammatory profile significantly different from that in COPD induced by cigarette smoke. As COPD is essentially proinflammatory,however, the mechanism underlying this Th2-type anti-inflammatory profile remains elusive.In this work, a network model is applied to study BS-induced inflammatory dynamics. The network model involves several positive feedback loops, activations of which are responsible for different mechanisms by which clinical phenotypes of COPD are produced. Our modeling study in this work has identified a subset of BS-COPD patients with a mixed M1-and Th2-type inflammatory profile. The model’s prediction is in good agreement with clinical experiments and our in silico knockout simulations have demonstrated several important network components that play an important role in the disease. Our modeling study provides novel insight into BS-COPD progression, offering a rationale for targeted therapy and personalized medicine for treatment of the disease in future.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
This study at the Esmeralda Mine,part of the El Teniente Division of CODELCO,investigates optimizing hydraulic fracturing(HF)holes’spatial distribution to improve rock material production in one of the world's la...This study at the Esmeralda Mine,part of the El Teniente Division of CODELCO,investigates optimizing hydraulic fracturing(HF)holes’spatial distribution to improve rock material production in one of the world's largest copper-molybdenum deposits.Utilizing diverse data sources,including borehole,oriented borehole,and photogrammetry data,along with hang-up frequency and hydrofracturing details,we applied discrete fracture network(DFN)modeling to analyze in-situ block size distribution and fragmentation.These results are based on 12,000 realizations of discrete fracture network(DFN)models using R-Dis-Frag computer pacakge at real cave volumes of 200 m200 m200 m,with varying parameters,which significantly enhances their reliability.The incorporation of DFN modeling and geostatistical simulation allows for capturing the interaction berween several spatial variables and explaining the variations observed in the production results at the draw points.Keyfindings of spatio-statistical analysis highlight the significance of volumetric fracture intensity(P32)and extraction column height in reducing hang-up events and enhancing fragmentation efficiency.The study integrates HF-induced and natural fracture intensities,revealing that higher P32 values and higher draw columns correlate with fewer hang-ups and better fragmentation.We recommend non-regular HF patterns for high P32 zones to improve operational efficiency.This research provides insights into optimizing mining operations,acknowledging the limitations of HF propagation efficacy and paving the way for further exploration into the interplay between hydraulic fracturing and natural discontinuities.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
Titanium dioxide （TiO2） nanoparticles were prepared by sol gel route. The preparation parameters were optimized in the removal of 4-nitropbenol （4-NP）. All catalysts were analyzed by X-ray diffraction （XRD） and ...Titanium dioxide （TiO2） nanoparticles were prepared by sol gel route. The preparation parameters were optimized in the removal of 4-nitropbenol （4-NP）. All catalysts were analyzed by X-ray diffraction （XRD） and scanning electron microscopy （SEM）. An artificial neural network model （ANN） was developed to predict the photocatalytic removal of 4-NP in the presence of TiOz nanoparticles prepared under desired conditions. The comparison between the predicted results by designed ANN model and the experimental data proved that modeling of the removal process of 4-NP using artificial neural network was a precise method to predict the extent of 4-NP removal under different conditions.展开更多
A new approach referred as“the network modeling method”was developed by the authors to analyze the behaviors of marine structures.In this paper the method is briefly described and applied to predict the loads acting...A new approach referred as“the network modeling method”was developed by the authors to analyze the behaviors of marine structures.In this paper the method is briefly described and applied to predict the loads acting on the connectors between the two modules of the Scientific Research and Demonstration Platform(SRDP),which was deployed in a complicated wave environment near islands and reefs in South China Sea.Based on this method,the response amplitude operators(RAOs)of the connector loads of the SRDP in regular waves,and the time variations of the connector loads of the SRDP in an on-site measured random sea state are predicted and presented.The significant stresses at 20 spots of the local connection structure induced by the connector loads in the sea state are further calculated.The comparisons between the predicted and the on-site measured stresses confirm that the network modeling method is feasible to some extent and especially useful for design of the connectors’arrangement,estimation of the connector loads and the related structural safety of a multi-module floating structure in early design stage.展开更多
Owing to the increasing worldwide demand for natural gas,the development of a large submerged combustion vaporizer is required.Its burner is equipped with a water spray nozzle to reduce nitrogen oxides,and a practi-ca...Owing to the increasing worldwide demand for natural gas,the development of a large submerged combustion vaporizer is required.Its burner is equipped with a water spray nozzle to reduce nitrogen oxides,and a practi-cal simulation method is required for the optimal design.The non-adiabatic flamelet approach can predict the combustion emissions and is useful for reducing simulation costs.However,as the number of control variables increases,the database requires larger memory and cannot be dealt with by general computers.In this study,an artificial neural network(ANN)model based on a five-dimensional flamelet database,which includes the effects of heat loss and vapor concentration by sprayed water evaporation,is developed.Furthermore,large eddy sim-ulations(LESs)for turbulent combustion fields with and without water spray are conducted employing flamelet generated manifold(FGM)approach with this ANN model,and the validity is investigated.For comparison,a lab-scale burner equipped with a water spray nozzle is manufactured,and combustion experiments with and without water spray are conducted.The results show that CO,NO,temperature,and reaction rate of progress variable predicted by the present ANN model are in good agreement with those of a five-dimensional flamelet database.In the condition without water spray,the flame behavior predicted by the LES employing the FGM/ANN ap-proach is in good agreement with that employing the conventional FGM approach,while indicating much lower memory,although there appeared some quantitative discrepancies in the temperature against the experiment probably partially because of the insufficiency of the FGM approach for the present complex flame structure.In the condition with water spray,the LES employing the FGM/ANN approach is able to capture the effect of the water spray on the flame behavior in the experiment,such that the water spray decreases the temperature,which causes the decrease in NO but increase in CO.展开更多
In this research, a method called ANNMG is presented to integrate Artificial Neural Networks and Geostatistics for optimum mineral reserve evaluation. The word ANNMG simply means Artificial Neural Network Model integr...In this research, a method called ANNMG is presented to integrate Artificial Neural Networks and Geostatistics for optimum mineral reserve evaluation. The word ANNMG simply means Artificial Neural Network Model integrated with Geostatiscs, In this procedure, the Artificial Neural Network was trained, tested and validated using assay values obtained from exploratory drillholes. Next, the validated model was used to generalize mineral grades at known and unknown sampled locations inside the drilling region respectively. Finally, the reproduced and generalized assay values were combined and fed to geostatistics in order to develop a geological 3D block model. The regression analysis revealed that the predicted sample grades were in close proximity to the actual sample grades, The generalized grades from the ANNMG show that this process could be used to complement exploration activities thereby reducing drilling requirement. It could also be an effective mineral reserve evaluation method that could oroduce optimum block model for mine design.展开更多
Alzheimer’s disease is a primary age-related neurodegenerative disorder that can result in impaired cognitive and memory functions.Although connections between changes in brain networks of Alzheimer’s disease patien...Alzheimer’s disease is a primary age-related neurodegenerative disorder that can result in impaired cognitive and memory functions.Although connections between changes in brain networks of Alzheimer’s disease patients have been established,the mechanisms that drive these alterations remain incompletely understood.This study,which was conducted in 2018 at Northeastern University in China,included data from 97 participants of the Alzheimer’s Disease Neuroimaging Initiative(ADNI)dataset covering genetics,imaging,and clinical data.All participants were divided into two groups:normal control(n=52;20 males and 32 females;mean age 73.90±4.72 years)and Alzheimer’s disease(n=45,23 males and 22 females;mean age 74.85±5.66).To uncover the wiring mechanisms that shaped changes in the topology of human brain networks of Alzheimer’s disease patients,we proposed a local naive Bayes brain network model based on graph theory.Our results showed that the proposed model provided an excellent fit to observe networks in all properties examined,including clustering coefficient,modularity,characteristic path length,network efficiency,betweenness,and degree distribution compared with empirical methods.This proposed model simulated the wiring changes in human brain networks between controls and Alzheimer’s disease patients.Our results demonstrate its utility in understanding relationships between brain tissue structure and cognitive or behavioral functions.The ADNI was performed in accordance with the Good Clinical Practice guidelines,US 21 CFR Part 50-Protection of Human Subjects,and Part 56-Institutional Review Boards(IRBs)/Research Good Clinical Practice guidelines Institutional Review Boards(IRBs)/Research Ethics Boards(REBs).展开更多
Warm rotary draw bending provides a feasible method to form the large-diameter thin-walled（LDTW）TC4 bent tubes, which are widely used in the pneumatic system of aircrafts. An accurate prediction of flow behavior of ...Warm rotary draw bending provides a feasible method to form the large-diameter thin-walled（LDTW）TC4 bent tubes, which are widely used in the pneumatic system of aircrafts. An accurate prediction of flow behavior of TC4 tubes considering the couple effects of temperature,strain rate and strain is critical for understanding the deformation behavior of metals and optimizing the processing parameters in warm rotary draw bending of TC4 tubes. In this study, isothermal compression tests of TC4 tube alloy were performed from 573 to 873 K with an interval of 100 K and strain rates of 0.001, 0.010 and0.100 s^（-1）. The prediction of flow behavior was done using two constitutive models, namely modified Arrhenius model and artificial neural network（ANN） model. The predictions of these constitutive models were compared using statistical measures like correlation coefficient（R）, average absolute relative error（AARE） and its variation with the deformation parameters（temperature, strain rate and strain）. Analysis of statistical measures reveals that the two models show high predicted accuracy in terms of R and AARE. Comparatively speaking, the ANN model presents higher predicted accuracy than the modified Arrhenius model. In addition, the predicted accuracy of ANN model presents high stability at the whole deformation parameter ranges, whereas the predictability of the modified Arrhenius model has some fluctuation at different deformation conditions. It presents higher predicted accuracy at temperatures of 573-773 K, strain rates of 0.010-0.100 s^（-1）and strain of 0.04-0.32, while low accuracy at temperature of 873 K, strain rates of 0.001 s^（-1）and strain of 0.36-0.48.Thus, the application of modified Arrhenius model is limited by its relatively low predicted accuracy at some deformation conditions, while the ANN model presents very high predicted accuracy at all deformation conditions,which can be used to study the compression behavior of TC4 tube at the temperature range of 573-873 K and the strain rate of 0.001-0.100 s^（-1）. It can provide guideline for the design of processing parameters in warm rotary draw bending of LDTW TC4 tubes.展开更多
Hydrogen-enriched compressed natural gas(HCNG)has great potential for renewable energy and hydrogen utilization.However,injecting hydrogen into the natural gas network will change original fluid dynamics and complicat...Hydrogen-enriched compressed natural gas(HCNG)has great potential for renewable energy and hydrogen utilization.However,injecting hydrogen into the natural gas network will change original fluid dynamics and complicate compressed gas's physical properties,threatening operational safety of the electricity-HCNG-integrated energy system(E-HCNG-IES).To resolve such problem,this paper investigates effect of HCNG on gas network dynamics and presents an improved HCNG network model,which embodies the influence of blending hydrogen on the pressure drop equation and line pack equation.In addition,an optimal dispatch model for the E-HCNG-IES,considering the“production-storage-blending-transportation-utilization”link of the HCNG supply chain,is also proposed.The dispatch model is converted into a mixed-integer second-order conic programming(MISOCP)problem using the second-order cone(SOC)relaxation and piecewise linearization techniques.An iterative algorithm is proposed based on the convex-concave procedure and bound-tightening method to obtain a tight solution.Finally,the proposed methodology is evaluated through two E-HCNGIES numerical testbeds with different hydrogen volume fractions.Detailed operation analysis reveals that E-HCNG-IES can benefit from economic and environmental improvement with increased hydrogen volume fraction,despite declining energy delivery capacityand line pack flexibility.展开更多
The voltage source converter based multi-terminal high-voltage direct current(VSC-MTDC)system has attracted much attention because it can achieve the interconnection between AC grids.However,the initial phases and sho...The voltage source converter based multi-terminal high-voltage direct current(VSC-MTDC)system has attracted much attention because it can achieve the interconnection between AC grids.However,the initial phases and short-circuit ratios(SCRs)of the interconnected AC grids cause the steady-state phases(SSPs)of AC ports in the VSC-MTDC system to be different.This can lead to the issues such as mismatches in multiple converter reference frame systems,potentially causing inaccuracies in stability analysis when this phenomenon is disregarded.To address the aforementioned issues,a multi-port network model of the VSC-MTDC system,which considers the SSPs of the AC grids and AC ports,is derived by multiplying the port models of different subsystems(SSs).The proposed multi-port network model can accurately describe the transmission characteristics between the input and output ports of the system.Additionally,this model facilitates accurate analysis of the system stability.Furthermore,it identifies the key factors affecting the system stability.Ultimately,the accuracy of the proposed multi-port network model and the analysis of key factors are verified by time-domain simulations.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
Modeling of network traffic is a fundamental building block of computer science. Measurements of network traffic demonstrate that self-similarity is one of the basic properties of the network traffic possess at large ...Modeling of network traffic is a fundamental building block of computer science. Measurements of network traffic demonstrate that self-similarity is one of the basic properties of the network traffic possess at large time-scale. This paper investigates the change of non-stationary self-similarity of network traffic over time,and proposes a method of combining the discrete wavelet transform (DWT) and Schwarz information criterion (SIC) to detect change points of self-similarity in network traffic. The traffic is segmented into pieces around changing points with homogenous characteristics for the Hurst parameter,named local Hurst parameter,and then each piece of network traffic is modeled using fractional Gaussian noise (FGN) model with the local Hurst parameter. The presented experimental performance on data set from the Internet Traffic Archive (ITA) demonstrates that the method is more accurate in describing the non-stationary self-similarity of network traffic.展开更多
The number of common neighbor between nodes is applied to the modeling of resting-state brain function network in order to analyze the effect of anatomical distance on the modeling of resting-state brain function netw...The number of common neighbor between nodes is applied to the modeling of resting-state brain function network in order to analyze the effect of anatomical distance on the modeling of resting-state brain function network. Three models based on anatomical distance, the number of common neighbor, or anatomical distance and the number of common neighbor are designed. Basing on residuals creates the evaluation criteria for selecting the optimal brain function model network in each class model. The model is selected to simulate the human real brain function network by comparison with real data functional magnetic resonance imaging(f MRI)network. Finally, the result shows that the best model only is based on anatomical distance.展开更多
Unconventional reservoirs are normally characterized by dual porous media, which has both multi-scalepore and fracture structures, such as low permeability or tight oil reservoirs. The seepage characteristicsof such r...Unconventional reservoirs are normally characterized by dual porous media, which has both multi-scalepore and fracture structures, such as low permeability or tight oil reservoirs. The seepage characteristicsof such reservoirs is mainly determined by micro-fractures, but conventional laboratory experimentalmethods are difficult to measure it, which is attribute to the dynamic cracking of these micro-fractures.The emerging digital core technology in recent years can solve this problem by developing an accuratepore network model and a rational simulation approach. In this study, a novel pore-fracture dualnetwork model was established based on percolation theory. Fluid flow in the pore of two scales, microfracture and matrix pore, were considered, also with the impact of micro-fracture opening and closingduring flow. Some seepage characteristic parameters, such as fluid saturations, capillary pressure, relative permeabilities, displacement efficiency in different flow stage, can be predicted by proposedcalculating method. Through these work, seepage characteristics of dual porous media can be achieved.展开更多
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 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 the National Natural Science Foundation of China (No.21273209)
文摘Ulcerative colitis, an inflammatory bowel disease, is a chronic inflammatory disorder that results in ulcers of the colon and rectum without known etiology. Ulcerative colitis causes a huge public health care burden particularly in developed countries. Many studies suggest that ulcerative colitis results from an abnormal immune response against components of cornrnensal rnicrobiota in genetically susceptible individuals. However, understanding of the disease mechanisms at cellular and molecular levels remains largely elusive. In this paper, a network model is developed based on our previous study and computer simulations are perforrned using an agent-based network modeling to elucidate the dynamics of immune response in ulcerative colitis progression. Our modeling study identifies several important positive feedback loops as a driving force for ulcerative colitis initiation and progression. The results demonstrate that although immune response in ulcerative colitis patients is dominated by anti-inflarnrnatory/regulatory cells such as alternatively activated rnacrophages and type II natural killer T cells, proinflarnrnatory cells including classically activated rnacrophages, T helper 1 and T helper 17 cells, and their secreted cytokines tumor necrosis factor-α, interleukin-12, interleukin-23, interleukin-17 and interferon-γ remain at certain levels （lower than those in Crohn＇s disease, another inflammatory bowel disease）. Long-terrn exposure to these proinflarnrnatory components, causes rnucosal tissue damage persistently, leading to ulcerative colitis. Our simulation results are qualitatively in agreement with clinical and laboratory measurements, offering novel insight into the disease mechanisms.
基金This work was supported by the National Natural Science Foundation of China(No.21273209).
文摘Chronic obstructive pulmonary disease(COPD) is a chronic inflammatory disorder characterized by airflow obstruction and progressive damage of lung tissues. As currently more than 3 billion people use biomass fuel for cooking and heating worldwide, exposure to biomass smoke(BS) is recognized as a significant risk factor for COPD. Recent clinical data have shown that BS-COPD patients have a Th2-type inflammatory profile significantly different from that in COPD induced by cigarette smoke. As COPD is essentially proinflammatory,however, the mechanism underlying this Th2-type anti-inflammatory profile remains elusive.In this work, a network model is applied to study BS-induced inflammatory dynamics. The network model involves several positive feedback loops, activations of which are responsible for different mechanisms by which clinical phenotypes of COPD are produced. Our modeling study in this work has identified a subset of BS-COPD patients with a mixed M1-and Th2-type inflammatory profile. The model’s prediction is in good agreement with clinical experiments and our in silico knockout simulations have demonstrated several important network components that play an important role in the disease. Our modeling study provides novel insight into BS-COPD progression, offering a rationale for targeted therapy and personalized medicine for treatment of the disease in future.
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the funding of the Agencia Nacional de Investigación y 761 Desarrollo (ANID), through grant project of Fondecyt Iniciacion No. 11221093Basal Grants Center for Modeling ACE210010 and FB210005
文摘This study at the Esmeralda Mine,part of the El Teniente Division of CODELCO,investigates optimizing hydraulic fracturing(HF)holes’spatial distribution to improve rock material production in one of the world's largest copper-molybdenum deposits.Utilizing diverse data sources,including borehole,oriented borehole,and photogrammetry data,along with hang-up frequency and hydrofracturing details,we applied discrete fracture network(DFN)modeling to analyze in-situ block size distribution and fragmentation.These results are based on 12,000 realizations of discrete fracture network(DFN)models using R-Dis-Frag computer pacakge at real cave volumes of 200 m200 m200 m,with varying parameters,which significantly enhances their reliability.The incorporation of DFN modeling and geostatistical simulation allows for capturing the interaction berween several spatial variables and explaining the variations observed in the production results at the draw points.Keyfindings of spatio-statistical analysis highlight the significance of volumetric fracture intensity(P32)and extraction column height in reducing hang-up events and enhancing fragmentation efficiency.The study integrates HF-induced and natural fracture intensities,revealing that higher P32 values and higher draw columns correlate with fewer hang-ups and better fragmentation.We recommend non-regular HF patterns for high P32 zones to improve operational efficiency.This research provides insights into optimizing mining operations,acknowledging the limitations of HF propagation efficacy and paving the way for further exploration into the interplay between hydraulic fracturing and natural discontinuities.
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.
文摘Titanium dioxide （TiO2） nanoparticles were prepared by sol gel route. The preparation parameters were optimized in the removal of 4-nitropbenol （4-NP）. All catalysts were analyzed by X-ray diffraction （XRD） and scanning electron microscopy （SEM）. An artificial neural network model （ANN） was developed to predict the photocatalytic removal of 4-NP in the presence of TiOz nanoparticles prepared under desired conditions. The comparison between the predicted results by designed ANN model and the experimental data proved that modeling of the removal process of 4-NP using artificial neural network was a precise method to predict the extent of 4-NP removal under different conditions.
基金supported by the National Natural Science Foundation of China(Grant Nos.11702088,11902084)supported by the Ministry of Industry and Information Technology(Grant No.[2016]22)the Ministry of Science and Technology(Grant No.2013CB36100).
文摘A new approach referred as“the network modeling method”was developed by the authors to analyze the behaviors of marine structures.In this paper the method is briefly described and applied to predict the loads acting on the connectors between the two modules of the Scientific Research and Demonstration Platform(SRDP),which was deployed in a complicated wave environment near islands and reefs in South China Sea.Based on this method,the response amplitude operators(RAOs)of the connector loads of the SRDP in regular waves,and the time variations of the connector loads of the SRDP in an on-site measured random sea state are predicted and presented.The significant stresses at 20 spots of the local connection structure induced by the connector loads in the sea state are further calculated.The comparisons between the predicted and the on-site measured stresses confirm that the network modeling method is feasible to some extent and especially useful for design of the connectors’arrangement,estimation of the connector loads and the related structural safety of a multi-module floating structure in early design stage.
基金The temperature measurements and PIA were supported by Prof.M.Nishioka of University of Tsukuba and Prof.K.Nishino of Yokohama National University,respectively.This work was partially supported by MEXT as"Program for Promoting Researches on the Supercomputer Fu-gaku"(Digital Twins of Real World’s Clean Energy Systems with Inte-grated Utilization of Super-simulation and AI).
文摘Owing to the increasing worldwide demand for natural gas,the development of a large submerged combustion vaporizer is required.Its burner is equipped with a water spray nozzle to reduce nitrogen oxides,and a practi-cal simulation method is required for the optimal design.The non-adiabatic flamelet approach can predict the combustion emissions and is useful for reducing simulation costs.However,as the number of control variables increases,the database requires larger memory and cannot be dealt with by general computers.In this study,an artificial neural network(ANN)model based on a five-dimensional flamelet database,which includes the effects of heat loss and vapor concentration by sprayed water evaporation,is developed.Furthermore,large eddy sim-ulations(LESs)for turbulent combustion fields with and without water spray are conducted employing flamelet generated manifold(FGM)approach with this ANN model,and the validity is investigated.For comparison,a lab-scale burner equipped with a water spray nozzle is manufactured,and combustion experiments with and without water spray are conducted.The results show that CO,NO,temperature,and reaction rate of progress variable predicted by the present ANN model are in good agreement with those of a five-dimensional flamelet database.In the condition without water spray,the flame behavior predicted by the LES employing the FGM/ANN ap-proach is in good agreement with that employing the conventional FGM approach,while indicating much lower memory,although there appeared some quantitative discrepancies in the temperature against the experiment probably partially because of the insufficiency of the FGM approach for the present complex flame structure.In the condition with water spray,the LES employing the FGM/ANN approach is able to capture the effect of the water spray on the flame behavior in the experiment,such that the water spray decreases the temperature,which causes the decrease in NO but increase in CO.
基金the management of Sierra Rutile Company for providing the drillhole dataset used in this studythe Japanese Ministry of Education Science and Technology (MEXT) Scholarship for academic funding
文摘In this research, a method called ANNMG is presented to integrate Artificial Neural Networks and Geostatistics for optimum mineral reserve evaluation. The word ANNMG simply means Artificial Neural Network Model integrated with Geostatiscs, In this procedure, the Artificial Neural Network was trained, tested and validated using assay values obtained from exploratory drillholes. Next, the validated model was used to generalize mineral grades at known and unknown sampled locations inside the drilling region respectively. Finally, the reproduced and generalized assay values were combined and fed to geostatistics in order to develop a geological 3D block model. The regression analysis revealed that the predicted sample grades were in close proximity to the actual sample grades, The generalized grades from the ANNMG show that this process could be used to complement exploration activities thereby reducing drilling requirement. It could also be an effective mineral reserve evaluation method that could oroduce optimum block model for mine design.
基金Fundamental Research Funds for the Central Universities in China,No.N161608001 and No.N171903002
文摘Alzheimer’s disease is a primary age-related neurodegenerative disorder that can result in impaired cognitive and memory functions.Although connections between changes in brain networks of Alzheimer’s disease patients have been established,the mechanisms that drive these alterations remain incompletely understood.This study,which was conducted in 2018 at Northeastern University in China,included data from 97 participants of the Alzheimer’s Disease Neuroimaging Initiative(ADNI)dataset covering genetics,imaging,and clinical data.All participants were divided into two groups:normal control(n=52;20 males and 32 females;mean age 73.90±4.72 years)and Alzheimer’s disease(n=45,23 males and 22 females;mean age 74.85±5.66).To uncover the wiring mechanisms that shaped changes in the topology of human brain networks of Alzheimer’s disease patients,we proposed a local naive Bayes brain network model based on graph theory.Our results showed that the proposed model provided an excellent fit to observe networks in all properties examined,including clustering coefficient,modularity,characteristic path length,network efficiency,betweenness,and degree distribution compared with empirical methods.This proposed model simulated the wiring changes in human brain networks between controls and Alzheimer’s disease patients.Our results demonstrate its utility in understanding relationships between brain tissue structure and cognitive or behavioral functions.The ADNI was performed in accordance with the Good Clinical Practice guidelines,US 21 CFR Part 50-Protection of Human Subjects,and Part 56-Institutional Review Boards(IRBs)/Research Good Clinical Practice guidelines Institutional Review Boards(IRBs)/Research Ethics Boards(REBs).
基金financially supported by the National Natural Science Foundation of China(Nos.51275415 and50905144)the Natural Science Basic Research Plan in Shanxi Province(No.2011JQ6004)the Program of the Ministry of Education of China for Introducing Talents of Discipline to Universities(No.B08040)
文摘Warm rotary draw bending provides a feasible method to form the large-diameter thin-walled（LDTW）TC4 bent tubes, which are widely used in the pneumatic system of aircrafts. An accurate prediction of flow behavior of TC4 tubes considering the couple effects of temperature,strain rate and strain is critical for understanding the deformation behavior of metals and optimizing the processing parameters in warm rotary draw bending of TC4 tubes. In this study, isothermal compression tests of TC4 tube alloy were performed from 573 to 873 K with an interval of 100 K and strain rates of 0.001, 0.010 and0.100 s^（-1）. The prediction of flow behavior was done using two constitutive models, namely modified Arrhenius model and artificial neural network（ANN） model. The predictions of these constitutive models were compared using statistical measures like correlation coefficient（R）, average absolute relative error（AARE） and its variation with the deformation parameters（temperature, strain rate and strain）. Analysis of statistical measures reveals that the two models show high predicted accuracy in terms of R and AARE. Comparatively speaking, the ANN model presents higher predicted accuracy than the modified Arrhenius model. In addition, the predicted accuracy of ANN model presents high stability at the whole deformation parameter ranges, whereas the predictability of the modified Arrhenius model has some fluctuation at different deformation conditions. It presents higher predicted accuracy at temperatures of 573-773 K, strain rates of 0.010-0.100 s^（-1）and strain of 0.04-0.32, while low accuracy at temperature of 873 K, strain rates of 0.001 s^（-1）and strain of 0.36-0.48.Thus, the application of modified Arrhenius model is limited by its relatively low predicted accuracy at some deformation conditions, while the ANN model presents very high predicted accuracy at all deformation conditions,which can be used to study the compression behavior of TC4 tube at the temperature range of 573-873 K and the strain rate of 0.001-0.100 s^（-1）. It can provide guideline for the design of processing parameters in warm rotary draw bending of LDTW TC4 tubes.
基金supported in part by the Science and Technology Project of State Grid Corporation of China(No.5100-202119574A-0-5-SF)。
文摘Hydrogen-enriched compressed natural gas(HCNG)has great potential for renewable energy and hydrogen utilization.However,injecting hydrogen into the natural gas network will change original fluid dynamics and complicate compressed gas's physical properties,threatening operational safety of the electricity-HCNG-integrated energy system(E-HCNG-IES).To resolve such problem,this paper investigates effect of HCNG on gas network dynamics and presents an improved HCNG network model,which embodies the influence of blending hydrogen on the pressure drop equation and line pack equation.In addition,an optimal dispatch model for the E-HCNG-IES,considering the“production-storage-blending-transportation-utilization”link of the HCNG supply chain,is also proposed.The dispatch model is converted into a mixed-integer second-order conic programming(MISOCP)problem using the second-order cone(SOC)relaxation and piecewise linearization techniques.An iterative algorithm is proposed based on the convex-concave procedure and bound-tightening method to obtain a tight solution.Finally,the proposed methodology is evaluated through two E-HCNGIES numerical testbeds with different hydrogen volume fractions.Detailed operation analysis reveals that E-HCNG-IES can benefit from economic and environmental improvement with increased hydrogen volume fraction,despite declining energy delivery capacityand line pack flexibility.
基金supported by the National Natural Science Key Foundation of China(No.51937001)in part by the Fundamental Research Funds for the Central Universities(No.2023CDJXY-029)。
文摘The voltage source converter based multi-terminal high-voltage direct current(VSC-MTDC)system has attracted much attention because it can achieve the interconnection between AC grids.However,the initial phases and short-circuit ratios(SCRs)of the interconnected AC grids cause the steady-state phases(SSPs)of AC ports in the VSC-MTDC system to be different.This can lead to the issues such as mismatches in multiple converter reference frame systems,potentially causing inaccuracies in stability analysis when this phenomenon is disregarded.To address the aforementioned issues,a multi-port network model of the VSC-MTDC system,which considers the SSPs of the AC grids and AC ports,is derived by multiplying the port models of different subsystems(SSs).The proposed multi-port network model can accurately describe the transmission characteristics between the input and output ports of the system.Additionally,this model facilitates accurate analysis of the system stability.Furthermore,it identifies the key factors affecting the system stability.Ultimately,the accuracy of the proposed multi-port network model and the analysis of key factors are verified by time-domain simulations.
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the National High Technology Research and Development Program (863) of China(Nos. 2005AA145110 and 2006AA01Z436)the Natural Science Foundation of Shanghai of China(No. 05ZR14083)the Pudong New Area Technology Innovation Public Service Platform of China(No. PDPT2005-04)
文摘Modeling of network traffic is a fundamental building block of computer science. Measurements of network traffic demonstrate that self-similarity is one of the basic properties of the network traffic possess at large time-scale. This paper investigates the change of non-stationary self-similarity of network traffic over time,and proposes a method of combining the discrete wavelet transform (DWT) and Schwarz information criterion (SIC) to detect change points of self-similarity in network traffic. The traffic is segmented into pieces around changing points with homogenous characteristics for the Hurst parameter,named local Hurst parameter,and then each piece of network traffic is modeled using fractional Gaussian noise (FGN) model with the local Hurst parameter. The presented experimental performance on data set from the Internet Traffic Archive (ITA) demonstrates that the method is more accurate in describing the non-stationary self-similarity of network traffic.
基金the National Natural Science Foundation of China(Nos.6117013661373101+3 种基金61472270 and61402318)the Natural Science Foundation of Shanxi(No.2014021022-5)the Special/Youth Foundation of Taiyuan University of Technology(No.2012L014)the Youth Team Fund of Taiyuan University of Technology(Nos.2013T047 and 2013T048)
文摘The number of common neighbor between nodes is applied to the modeling of resting-state brain function network in order to analyze the effect of anatomical distance on the modeling of resting-state brain function network. Three models based on anatomical distance, the number of common neighbor, or anatomical distance and the number of common neighbor are designed. Basing on residuals creates the evaluation criteria for selecting the optimal brain function model network in each class model. The model is selected to simulate the human real brain function network by comparison with real data functional magnetic resonance imaging(f MRI)network. Finally, the result shows that the best model only is based on anatomical distance.
基金The writers greatly appreciate the financial support of the Major Special Project of PetroChina Co Ltd.(2017E-0406)the National Science and Technology Major Project during the 13th Five-year Plan Period(2016ZX05010-00504).
文摘Unconventional reservoirs are normally characterized by dual porous media, which has both multi-scalepore and fracture structures, such as low permeability or tight oil reservoirs. The seepage characteristicsof such reservoirs is mainly determined by micro-fractures, but conventional laboratory experimentalmethods are difficult to measure it, which is attribute to the dynamic cracking of these micro-fractures.The emerging digital core technology in recent years can solve this problem by developing an accuratepore network model and a rational simulation approach. In this study, a novel pore-fracture dualnetwork model was established based on percolation theory. Fluid flow in the pore of two scales, microfracture and matrix pore, were considered, also with the impact of micro-fracture opening and closingduring flow. Some seepage characteristic parameters, such as fluid saturations, capillary pressure, relative permeabilities, displacement efficiency in different flow stage, can be predicted by proposedcalculating method. Through these work, seepage characteristics of dual porous media can be achieved.
