To fundamentally alleviate the excavation chamber clogging during slurry tunnel boring machine(TBM)advancing in hard rock,large-diameter short screw conveyor was adopted to slurry TBM of Qingdao Jiaozhou Bay Second Un...To fundamentally alleviate the excavation chamber clogging during slurry tunnel boring machine(TBM)advancing in hard rock,large-diameter short screw conveyor was adopted to slurry TBM of Qingdao Jiaozhou Bay Second Undersea Tunnel.To evaluate the discharging performance of short screw conveyor in different cases,the full-scale transient slurry-rock two-phase model for a short screw conveyor actively discharging rocks was established using computational fluid dynamics-discrete element method(CFD-DEM)coupling approach.In the fluid domain of coupling model,the sliding mesh technology was utilized to describe the rotations of the atmospheric composite cutterhead and the short screw conveyor.In the particle domain of coupling model,the dynamic particle factories were established to produce rock particles with the rotation of the cutterhead.And the accuracy and reliability of the CFD-DEM simulation results were validated via the field test and model test.Furthermore,a comprehensive parameter analysis was conducted to examine the effects of TBM operating parameters,the geometric design of screw conveyor and the size of rocks on the discharging performance of short screw conveyor.Accordingly,a reasonable rotational speed of screw conveyor was suggested and applied to Jiaozhou Bay Second Undersea Tunnel project.The findings in this paper could provide valuable references for addressing the excavation chamber clogging during ultra-large-diameter slurry TBM tunneling in hard rock for similar future.展开更多
Microfiltration membrane technology has been widely used in various industries for solid-liquid separation. However, pore clogging remains a persistent challenge. This study employs (CFD) and discrete element method (...Microfiltration membrane technology has been widely used in various industries for solid-liquid separation. However, pore clogging remains a persistent challenge. This study employs (CFD) and discrete element method (DEM) models to enhance our understanding of microfiltration membrane clogging. The models were validated by comparing them to experimental data, demonstrating reasonable consistency. Subsequently, a parametric study was conducted on a cross-flow model, exploring the influence of key parameters on clogging. Findings show that clogging is a complex phenomenon affected by various factors. The mean inlet velocity and transmembrane flux were found to directly impact clogging, while the confinement ratio and cosine of the membrane pore entrance angle had an inverse relationship with it. Two clog types were identified: internal (inside the pore) and external (arching at the pore entrance), with the confinement ratio determining the type. This study introduced a dimensionless number as a quantitative clogging indicator based on transmembrane flux, Reynolds number, filtration time, entrance angle cosine, and confinement ratio. While this hypothesis held true in simulations, future studies should explore variations in clogging indicators, and improved modeling of clogging characteristics. Calibration between numerical and physical times and consideration of particle volume fraction will enhance understanding.展开更多
A nozzle clogging online forecasting model based on hydrodynamics engineering was developed, in which the actual flow rate was calculated from the mold width, thickness, and casting speed. There is a linear relationsh...A nozzle clogging online forecasting model based on hydrodynamics engineering was developed, in which the actual flow rate was calculated from the mold width, thickness, and casting speed. There is a linear relationship between the theoretical flow rate and the slide gate opening ratio as the molten steel level, argon flow rate, and the top slag weight are kept constant, and the relationship can be obtained by regression of the data collected at the beginning of the first heat in each casting sequence when the nozzle clogging does not occur. Then, during the casting, the theoretical flow rate can be calculated at intervals of one second. Comparing the theoretical flow rate with the actual flow rate, the online nozzle clogging ratio can be obtained at intervals of one second. The computer model based on the conception of the nozzle clogging ratio can display the degree of the nozzle clogging intuitively.展开更多
The SEN clogging is a serious problem for continuous casting operation and steel quality.The kinetics mathematic model of SEN clogging during steel continuous casting is discussed.The fluid flow and inclusions motion ...The SEN clogging is a serious problem for continuous casting operation and steel quality.The kinetics mathematic model of SEN clogging during steel continuous casting is discussed.The fluid flow and inclusions motion are calculated by means of mathematic model.Effects of diameters of inclusions,roughness of nozzle,diameter of nozzle and casting speed on the entrapment probability are calculated and evaluated.The result shows that inclusions are more easily to attach to the nozzle by the following condition：smaller inclusion size,larger roughness of the wall and the smaller bulk velocity in the nozzle.展开更多
The utilization of stone columns has emerged as a popular ground improvement strategy,whereas the drainage performance can be adversely hampered by clogging effect.Despite the ample progress of calculation methods for...The utilization of stone columns has emerged as a popular ground improvement strategy,whereas the drainage performance can be adversely hampered by clogging effect.Despite the ample progress of calculation methods for the consolidation of stone column-improved ground,theoretical investigations into the clogging effect have not been thoroughly explored.Furthermore,it is imperative to involve the column consolidation deformation to mitigate computational error on the consolidation of composite ground with high replacement ratios.In this context,an analytical model accounting for the initial clogging and coupled time and depth-dependent clogging of stone columns is established.Then,the resulting governing equations and analytical solutions are obtained under a new flow continuity relationship to incorporate column consolidation deformation.The accuracy and reliability of the proposed model are illustrated by degradation analysis and case studies with good agreements.Subsequently,the computed results of the current study are juxtaposed against the existing models,and an in-depth assessment of the impacts of several crucial parameters on the consolidation behavior is conducted.The results reveal that ignoring column consolidation deformation leads to an overestimate of the consolidation rate,with maximum error reaching up to 16%as the replacement ratio increases.Furthermore,the initial clogging also has a significant influence on the consolidation performance.Additionally,the increment of depth and time-clogging factors a and b will induce a noticeable retardation of the consolidation process,particularly in the later stage.展开更多
A physical model with mercury as analog was developed to investigate the influences of electromagnetic stirring（EMS） on flow field in slab continuous casting when the submerged entry nozzle（SEN） was clogged with d...A physical model with mercury as analog was developed to investigate the influences of electromagnetic stirring（EMS） on flow field in slab continuous casting when the submerged entry nozzle（SEN） was clogged with different clogging rates（0,10% ,25% ,and 50% ）. The flow field in mold under different EMS currents（0, 40 A, and 60 A） was measured by an ultrasonic Doppler velocimeter. The results proved that the flow field in the mold was a typical double roll structure under non-clogging SEN. As the SEN clogging rate increased, the flow field structure was transformed from a double roll to asymmetry flow. When the clogging rate reached 50%, the up circulation disappeared on the clogged side. The zone under the meniscus near the narrow face was a non-flowing area. EMS could correct bias flow caused by SEN clogging and improve the symmetry of the flow field during SEN clogging.展开更多
Combining with the indoor clogging tests of loose foundation in Tibet,the permeability clogging process of loose foundation was simulated based on particle flow method. Under the constant head of 2.2m, numerical micro...Combining with the indoor clogging tests of loose foundation in Tibet,the permeability clogging process of loose foundation was simulated based on particle flow method. Under the constant head of 2.2m, numerical micro-simulation was made in three cases,which was not adding any clogging materials,or adding the clogging materials with the diameter between 0.075-0.500 mm and 0.5-1 mm. The dynamic changes of fluid velocity,permeability coefficient,porosity and loss amount were recorded in the numerical simulation. The results have shown that fluid velocity and permeability coefficient decreased rapidly,when adding the clogging materials with the diameter between 0.5 mm and 1.0 mm. With seepage stability,fluid velocity value was very low. By using computer simulation in the two cases,we got that both cases induced clogging effect. Clogging effect was due to one of the interval of particles rather than one size,which could be seen in the phenomenon of the second case. To some extent,numerical method is useful in the study of clogging problems,which gave the same result obtained in laboratory test and simulation test. These data provided basis and reference for further study of clogging problems,and also provided a new method to study the micro-scale permeability clogging mechanism.展开更多
<div style="text-align:justify;"> Due to the poor anti-clogging performance of the common drip irrigation emitters, this paper designed a new bionic flow channel in the emitter based on the shape of sh...<div style="text-align:justify;"> Due to the poor anti-clogging performance of the common drip irrigation emitters, this paper designed a new bionic flow channel in the emitter based on the shape of shark dorsal fin. After preliminary structural design, the computational fluid dynamics (CFD) simulation showed that the bionic emitter exhibited superior anti-clogging performance and reasonable hydraulic performance. The passage rate of particles of the bionic emitter in simulation reached 96.3% which was 37.6% higher than 70% of traditional emitter, and the discharge exponent reached 0.4995 which was close to traditional emitter. Physical experiments were consistent with the CFD results, which confirmed the correctness of simulation. After a short cycle anti-clogging performance experiment, the bionic emitter still maintained 96.09% of the initial flow rate. </div>展开更多
Clogging is a major geohazards risk in mechanized tunnelling through cohesive soils.Clay clogging results from the high adhesion between the clay and metal.Based on the water film theory and Reynolds fluid equation,th...Clogging is a major geohazards risk in mechanized tunnelling through cohesive soils.Clay clogging results from the high adhesion between the clay and metal.Based on the water film theory and Reynolds fluid equation,the interfacial adhesion between metal and soil is simplified in this study as viscous hydrodynamic behavior between planes.Considering the influence of capillary force and the viscous force of water film at the interface between metal and soil,a theoretical calculation model of interfacial adhesion between metal and soil is established.The influence of water film thickness and separation rate on the interfacial adhesion between metal and soil is qualitatively analyzed.Then,the adhesion stress between the clay and the metal surface was tested with a pullout test and the influence of moisture content,pullout rates and types of clay minerals on the adhesion stress was analyzed.Finally,the calculation model of adhesion force was compared with the experimental results.The calculation model of soil adhesion stress established in this paper can quantitatively describe the relationship between soil adhesion force and moisture content and can also qualitatively reveal the influence mechanism of soil moisture content on adhesion stress.展开更多
We propose an integrated method of data-driven and mechanism models for well logging formation evaluation,explicitly focusing on predicting reservoir parameters,such as porosity and water saturation.Accurately interpr...We propose an integrated method of data-driven and mechanism models for well logging formation evaluation,explicitly focusing on predicting reservoir parameters,such as porosity and water saturation.Accurately interpreting these parameters is crucial for effectively exploring and developing oil and gas.However,with the increasing complexity of geological conditions in this industry,there is a growing demand for improved accuracy in reservoir parameter prediction,leading to higher costs associated with manual interpretation.The conventional logging interpretation methods rely on empirical relationships between logging data and reservoir parameters,which suffer from low interpretation efficiency,intense subjectivity,and suitability for ideal conditions.The application of artificial intelligence in the interpretation of logging data provides a new solution to the problems existing in traditional methods.It is expected to improve the accuracy and efficiency of the interpretation.If large and high-quality datasets exist,data-driven models can reveal relationships of arbitrary complexity.Nevertheless,constructing sufficiently large logging datasets with reliable labels remains challenging,making it difficult to apply data-driven models effectively in logging data interpretation.Furthermore,data-driven models often act as“black boxes”without explaining their predictions or ensuring compliance with primary physical constraints.This paper proposes a machine learning method with strong physical constraints by integrating mechanism and data-driven models.Prior knowledge of logging data interpretation is embedded into machine learning regarding network structure,loss function,and optimization algorithm.We employ the Physically Informed Auto-Encoder(PIAE)to predict porosity and water saturation,which can be trained without labeled reservoir parameters using self-supervised learning techniques.This approach effectively achieves automated interpretation and facilitates generalization across diverse datasets.展开更多
To improve the accuracy and generalization of well logging curve reconstruction,this paper proposes an artificial intelligence large language model“Gaia”and conducts model evaluation experiments.By fine-tuning the p...To improve the accuracy and generalization of well logging curve reconstruction,this paper proposes an artificial intelligence large language model“Gaia”and conducts model evaluation experiments.By fine-tuning the pre-trained large language model,the Gaia significantly improved its ability in extracting sequential patterns and spatial features from well-log curves.Leveraging the adapter method for fine-tuning,this model required training only about 1/70 of its original parameters,greatly improving training efficiency.Comparative experiments,ablation experiments,and generalization experiments were designed and conducted using well-log data from 250 wells.In the comparative experiment,the Gaia model was benchmarked against cutting-edge small deep learning models and conventional large language models,demonstrating that the Gaia model reduced the mean absolute error(MAE)by at least 20%.In the ablation experiments,the synergistic effect of the Gaia model's multiple components was validated,with its MAE being at least 30%lower than that of single-component models.In the generalization experiments,the superior performance of the Gaia model in blind-well predictions was further confirmed.Compared to traditional models,the Gaia model is significantly superior in accuracy and generalization for logging curve reconstruction,fully showcasing the potential of large language models in the field of well-logging.This provides a new approach for future intelligent logging data processing.展开更多
Well logging technology has accumulated a large amount of historical data through four generations of technological development,which forms the basis of well logging big data and digital assets.However,the value of th...Well logging technology has accumulated a large amount of historical data through four generations of technological development,which forms the basis of well logging big data and digital assets.However,the value of these data has not been well stored,managed and mined.With the development of cloud computing technology,it provides a rare development opportunity for logging big data private cloud.The traditional petrophysical evaluation and interpretation model has encountered great challenges in the face of new evaluation objects.The solution research of logging big data distributed storage,processing and learning functions integrated in logging big data private cloud has not been carried out yet.To establish a distributed logging big-data private cloud platform centered on a unifi ed learning model,which achieves the distributed storage and processing of logging big data and facilitates the learning of novel knowledge patterns via the unifi ed logging learning model integrating physical simulation and data models in a large-scale functional space,thus resolving the geo-engineering evaluation problem of geothermal fi elds.Based on the research idea of“logging big data cloud platform-unifi ed logging learning model-large function space-knowledge learning&discovery-application”,the theoretical foundation of unified learning model,cloud platform architecture,data storage and learning algorithm,arithmetic power allocation and platform monitoring,platform stability,data security,etc.have been carried on analysis.The designed logging big data cloud platform realizes parallel distributed storage and processing of data and learning algorithms.The feasibility of constructing a well logging big data cloud platform based on a unifi ed learning model of physics and data is analyzed in terms of the structure,ecology,management and security of the cloud platform.The case study shows that the logging big data cloud platform has obvious technical advantages over traditional logging evaluation methods in terms of knowledge discovery method,data software and results sharing,accuracy,speed and complexity.展开更多
We propose a novel workflow for fast forward modeling of well logs in axially symmetric 2D models of the nearwellbore environment.The approach integrates the finite element method with deep residual neural networks to...We propose a novel workflow for fast forward modeling of well logs in axially symmetric 2D models of the nearwellbore environment.The approach integrates the finite element method with deep residual neural networks to achieve exceptional computational efficiency and accuracy.The workflow is demonstrated through the modeling of wireline electromagnetic propagation resistivity logs,where the measured responses exhibit a highly nonlinear relationship with formation properties.The motivation for this research is the need for advanced modeling al-gorithms that are fast enough for use in modern quantitative interpretation tools,where thousands of simulations may be required in iterative inversion processes.The proposed algorithm achieves a remarkable enhancement in performance,being up to 3000 times faster than the finite element method alone when utilizing a GPU.While still ensuring high accuracy,this makes it well-suited for practical applications when reliable payzone assessment is needed in complex environmental scenarios.Furthermore,the algorithm’s efficiency positions it as a promising tool for stochastic Bayesian inversion,facilitating reliable uncertainty quantification in subsurface property estimation.展开更多
A cased well model consists of a coaxial tank and casing,which houses coaxially installed transmitting and receiving coils.The transmitting coil is excited by the current produced by the transmitting circuit,and trans...A cased well model consists of a coaxial tank and casing,which houses coaxially installed transmitting and receiving coils.The transmitting coil is excited by the current produced by the transmitting circuit,and transient electromagnetic responses occur in the casing,including direct coupling and casing responses.As the range between the transmitting and receiving coils increases,direct coupling responses decay rapidly,are less than the casing response at 0.3 m,and disappear at 0.7 m.By contrast,a casing response increases rapidly and then declines slowly after reaching a peak and changes little within a specifi c range.The peak decreases slowly with range.The continuous addition of water to the tank causes slight changes in transient electromagnetic responses,so the diff erence which are subtracted from the response without water is used.Moreover,the diff erences at the time of rapid increase in response and the time of rapid decrease in response are large,forming a peak and a trough.Given that the conductivity of water in a full tank changes after the addition of salt,the diff erence in the peak is linear with the increase in the conductivity of water.This result provides an experimental basis for the design of a transient electromagnetic logging instrument that measures the conductivity of formation in cased well.展开更多
Global forest cover is undergoing significant transformations due to anthropogenic activities and natural disturbances,profoundly impacting hydrological processes.However,the inherent spatial heterogeneity within wate...Global forest cover is undergoing significant transformations due to anthropogenic activities and natural disturbances,profoundly impacting hydrological processes.However,the inherent spatial heterogeneity within watersheds leads to varied hydrological responses across spatiotemporal scales,challenging comprehensive assessment of logging impacts at the watershed scale.Here,we developed multiple forest logging scenarios using the soil and water assessment tool(SWAT)model for the Le'an River watershed,a 5,837 km2 subtropical watershed in China,to quantify the hydrological effects of forest logging across different spatiotemporal scales.Our results demonstrate that increasing forest logging ratios from 1.54% to 9.25% consistently enhanced ecohydrological sensitivity.However,sensitivity varied across spatiotemporal scales,with the rainy season(15.30%-15.81%)showing higher sensitivity than annual(11.56%-12.07%)and dry season(3.38%-5.57%)periods.Additionally,the ecohydrological sensitivity of logging varied significantly across the watershed,with midstream areas exhibiting the highest sensitivity(13.13%-13.25%),followed by downstream(11.87%-11.98%)and upstream regions(9.96%-10.05%).Furthermore,the whole watershed exhibited greater hydrological resilience to logging compared to upstream areas,with attenuated runoff changes due to scale effects.Scale effects were more pronounced during dry seasons((-8.13 to -42.13)×10^(4) m^(3)·month^(-1))than in the rainy season((-11.11 to -26.65)×10^(4) m^(3)·month^(-1)).These findings advance understanding of logging impacts on hydrology across different spatiotemporal scales in subtropical regions,providing valuable insights for forest management under increasing anthropogenic activities and climate change.展开更多
Business processes described by formal or semi-formal models are realized via information systems.Event logs generated from these systems are probably not consistent with the existing models due to insufficient design...Business processes described by formal or semi-formal models are realized via information systems.Event logs generated from these systems are probably not consistent with the existing models due to insufficient design of the information system or the system upgrade.By comparing an existing process model with event logs,we can detect inconsistencies called deviations,verify and extend the business process model,and accordingly improve the business process.In this paper,some abnormal activities in business processes are formally defined based on Petri nets.An efficient approach to detect deviations between the process model and event logs is proposed.Then,business process models are revised when abnormal activities exist.A clinical process in a healthcare information system is used as a case study to illustrate our work.Experimental results show the effectiveness and efficiency of the proposed approach.展开更多
AIM:To investigate the efficiency of Cox proportional hazard model in detecting prognostic factors for gastric cancer.METHODS:We used the log-normal regression model to evaluate prognostic factors in gastric cancer an...AIM:To investigate the efficiency of Cox proportional hazard model in detecting prognostic factors for gastric cancer.METHODS:We used the log-normal regression model to evaluate prognostic factors in gastric cancer and compared it with the Cox model.Three thousand and eighteen gastric cancer patients who received a gastrectomy between 1980 and 2004 were retrospectively evaluated.Clinic-pathological factors were included in a log-normal model as well as Cox model.The akaike information criterion (AIC) was employed to compare the efficiency of both models.Univariate analysis indicated that age at diagnosis,past history,cancer location,distant metastasis status,surgical curative degree,combined other organ resection,Borrmann type,Lauren's classification,pT stage,total dissected nodes and pN stage were prognostic factors in both log-normal and Cox models.RESULTS:In the final multivariate model,age at diagnosis,past history,surgical curative degree,Borrmann type,Lauren's classification,pT stage,and pN stage were significant prognostic factors in both log-normal and Cox models.However,cancer location,distant metastasis status,and histology types were found to be significant prognostic factors in log-normal results alone.According to AIC,the log-normal model performed better than the Cox proportional hazard model (AIC value:2534.72 vs 1693.56).CONCLUSION:It is suggested that the log-normal regression model can be a useful statistical model to evaluate prognostic factors instead of the Cox proportional hazard model.展开更多
The numerical solution of Green’s function for the potential in 2-D arbitrary in-homogeneous media with axial symmetry has been given by use of efficient half-analytical, half-numerical hybrid method. Then the loggin...The numerical solution of Green’s function for the potential in 2-D arbitrary in-homogeneous media with axial symmetry has been given by use of efficient half-analytical, half-numerical hybrid method. Then the logging responses of various kinds of the DC resistivity log with axisymmetric excitation have been obtained by using surface integral equation method to match the boundary conditions on the electrodes of the logging sonde. Comparing the results with that obtained by other methods, one can see good precision and efficiency of the given method. Some applications of the numerical modeling have been also discussed.展开更多
This paper introduces a new spontaneous potential log model for the case in which formation resistivity is not piecewise constant. The spontaneous potential satisfies an elliptic boundary value problem with jump condi...This paper introduces a new spontaneous potential log model for the case in which formation resistivity is not piecewise constant. The spontaneous potential satisfies an elliptic boundary value problem with jump conditions on the interfaces. It has beer/ shown that the elliptic interface problem has a unique weak solution. Furthermore, a jump condition capturing finite difference scheme is proposed and applied to solve such elliptic problems. Numerical results show validity and effectiveness of the proposed method.展开更多
基金supported by the Fundamental Research Funds for the Central Universities(Grant No.2023YJS053)the National Natural Science Foundation of China(Grant No.52278386).
文摘To fundamentally alleviate the excavation chamber clogging during slurry tunnel boring machine(TBM)advancing in hard rock,large-diameter short screw conveyor was adopted to slurry TBM of Qingdao Jiaozhou Bay Second Undersea Tunnel.To evaluate the discharging performance of short screw conveyor in different cases,the full-scale transient slurry-rock two-phase model for a short screw conveyor actively discharging rocks was established using computational fluid dynamics-discrete element method(CFD-DEM)coupling approach.In the fluid domain of coupling model,the sliding mesh technology was utilized to describe the rotations of the atmospheric composite cutterhead and the short screw conveyor.In the particle domain of coupling model,the dynamic particle factories were established to produce rock particles with the rotation of the cutterhead.And the accuracy and reliability of the CFD-DEM simulation results were validated via the field test and model test.Furthermore,a comprehensive parameter analysis was conducted to examine the effects of TBM operating parameters,the geometric design of screw conveyor and the size of rocks on the discharging performance of short screw conveyor.Accordingly,a reasonable rotational speed of screw conveyor was suggested and applied to Jiaozhou Bay Second Undersea Tunnel project.The findings in this paper could provide valuable references for addressing the excavation chamber clogging during ultra-large-diameter slurry TBM tunneling in hard rock for similar future.
文摘Microfiltration membrane technology has been widely used in various industries for solid-liquid separation. However, pore clogging remains a persistent challenge. This study employs (CFD) and discrete element method (DEM) models to enhance our understanding of microfiltration membrane clogging. The models were validated by comparing them to experimental data, demonstrating reasonable consistency. Subsequently, a parametric study was conducted on a cross-flow model, exploring the influence of key parameters on clogging. Findings show that clogging is a complex phenomenon affected by various factors. The mean inlet velocity and transmembrane flux were found to directly impact clogging, while the confinement ratio and cosine of the membrane pore entrance angle had an inverse relationship with it. Two clog types were identified: internal (inside the pore) and external (arching at the pore entrance), with the confinement ratio determining the type. This study introduced a dimensionless number as a quantitative clogging indicator based on transmembrane flux, Reynolds number, filtration time, entrance angle cosine, and confinement ratio. While this hypothesis held true in simulations, future studies should explore variations in clogging indicators, and improved modeling of clogging characteristics. Calibration between numerical and physical times and consideration of particle volume fraction will enhance understanding.
基金financially supported by the State EconomicTrade Commission of China (No.OIBK-098-02-07)
文摘A nozzle clogging online forecasting model based on hydrodynamics engineering was developed, in which the actual flow rate was calculated from the mold width, thickness, and casting speed. There is a linear relationship between the theoretical flow rate and the slide gate opening ratio as the molten steel level, argon flow rate, and the top slag weight are kept constant, and the relationship can be obtained by regression of the data collected at the beginning of the first heat in each casting sequence when the nozzle clogging does not occur. Then, during the casting, the theoretical flow rate can be calculated at intervals of one second. Comparing the theoretical flow rate with the actual flow rate, the online nozzle clogging ratio can be obtained at intervals of one second. The computer model based on the conception of the nozzle clogging ratio can display the degree of the nozzle clogging intuitively.
基金Item Sponsored by National Natural Science Foundation of China and Shanghai Baoshan Steel Group(50674013)
文摘The SEN clogging is a serious problem for continuous casting operation and steel quality.The kinetics mathematic model of SEN clogging during steel continuous casting is discussed.The fluid flow and inclusions motion are calculated by means of mathematic model.Effects of diameters of inclusions,roughness of nozzle,diameter of nozzle and casting speed on the entrapment probability are calculated and evaluated.The result shows that inclusions are more easily to attach to the nozzle by the following condition：smaller inclusion size,larger roughness of the wall and the smaller bulk velocity in the nozzle.
基金funding support from the National Natural Science Foundation of China(Grant Nos.52178373 and 51878657).
文摘The utilization of stone columns has emerged as a popular ground improvement strategy,whereas the drainage performance can be adversely hampered by clogging effect.Despite the ample progress of calculation methods for the consolidation of stone column-improved ground,theoretical investigations into the clogging effect have not been thoroughly explored.Furthermore,it is imperative to involve the column consolidation deformation to mitigate computational error on the consolidation of composite ground with high replacement ratios.In this context,an analytical model accounting for the initial clogging and coupled time and depth-dependent clogging of stone columns is established.Then,the resulting governing equations and analytical solutions are obtained under a new flow continuity relationship to incorporate column consolidation deformation.The accuracy and reliability of the proposed model are illustrated by degradation analysis and case studies with good agreements.Subsequently,the computed results of the current study are juxtaposed against the existing models,and an in-depth assessment of the impacts of several crucial parameters on the consolidation behavior is conducted.The results reveal that ignoring column consolidation deformation leads to an overestimate of the consolidation rate,with maximum error reaching up to 16%as the replacement ratio increases.Furthermore,the initial clogging also has a significant influence on the consolidation performance.Additionally,the increment of depth and time-clogging factors a and b will induce a noticeable retardation of the consolidation process,particularly in the later stage.
文摘A physical model with mercury as analog was developed to investigate the influences of electromagnetic stirring（EMS） on flow field in slab continuous casting when the submerged entry nozzle（SEN） was clogged with different clogging rates（0,10% ,25% ,and 50% ）. The flow field in mold under different EMS currents（0, 40 A, and 60 A） was measured by an ultrasonic Doppler velocimeter. The results proved that the flow field in the mold was a typical double roll structure under non-clogging SEN. As the SEN clogging rate increased, the flow field structure was transformed from a double roll to asymmetry flow. When the clogging rate reached 50%, the up circulation disappeared on the clogged side. The zone under the meniscus near the narrow face was a non-flowing area. EMS could correct bias flow caused by SEN clogging and improve the symmetry of the flow field during SEN clogging.
基金supported by the National Natural Science Foundation of P. R. China,clogging effects on affects and mechanism of the permeability of loose medium of altitude reservoirs (No. 41072197)
文摘Combining with the indoor clogging tests of loose foundation in Tibet,the permeability clogging process of loose foundation was simulated based on particle flow method. Under the constant head of 2.2m, numerical micro-simulation was made in three cases,which was not adding any clogging materials,or adding the clogging materials with the diameter between 0.075-0.500 mm and 0.5-1 mm. The dynamic changes of fluid velocity,permeability coefficient,porosity and loss amount were recorded in the numerical simulation. The results have shown that fluid velocity and permeability coefficient decreased rapidly,when adding the clogging materials with the diameter between 0.5 mm and 1.0 mm. With seepage stability,fluid velocity value was very low. By using computer simulation in the two cases,we got that both cases induced clogging effect. Clogging effect was due to one of the interval of particles rather than one size,which could be seen in the phenomenon of the second case. To some extent,numerical method is useful in the study of clogging problems,which gave the same result obtained in laboratory test and simulation test. These data provided basis and reference for further study of clogging problems,and also provided a new method to study the micro-scale permeability clogging mechanism.
文摘<div style="text-align:justify;"> Due to the poor anti-clogging performance of the common drip irrigation emitters, this paper designed a new bionic flow channel in the emitter based on the shape of shark dorsal fin. After preliminary structural design, the computational fluid dynamics (CFD) simulation showed that the bionic emitter exhibited superior anti-clogging performance and reasonable hydraulic performance. The passage rate of particles of the bionic emitter in simulation reached 96.3% which was 37.6% higher than 70% of traditional emitter, and the discharge exponent reached 0.4995 which was close to traditional emitter. Physical experiments were consistent with the CFD results, which confirmed the correctness of simulation. After a short cycle anti-clogging performance experiment, the bionic emitter still maintained 96.09% of the initial flow rate. </div>
基金financially supported by the National Natural Science Foundation of China(Grant No.52078428)the Sichuan Outstanding Young Science and Technology Talent Project(Grant No.2020JDJQ0032).
文摘Clogging is a major geohazards risk in mechanized tunnelling through cohesive soils.Clay clogging results from the high adhesion between the clay and metal.Based on the water film theory and Reynolds fluid equation,the interfacial adhesion between metal and soil is simplified in this study as viscous hydrodynamic behavior between planes.Considering the influence of capillary force and the viscous force of water film at the interface between metal and soil,a theoretical calculation model of interfacial adhesion between metal and soil is established.The influence of water film thickness and separation rate on the interfacial adhesion between metal and soil is qualitatively analyzed.Then,the adhesion stress between the clay and the metal surface was tested with a pullout test and the influence of moisture content,pullout rates and types of clay minerals on the adhesion stress was analyzed.Finally,the calculation model of adhesion force was compared with the experimental results.The calculation model of soil adhesion stress established in this paper can quantitatively describe the relationship between soil adhesion force and moisture content and can also qualitatively reveal the influence mechanism of soil moisture content on adhesion stress.
基金supported by National Key Research and Development Program (2019YFA0708301)National Natural Science Foundation of China (51974337)+2 种基金the Strategic Cooperation Projects of CNPC and CUPB (ZLZX2020-03)Science and Technology Innovation Fund of CNPC (2021DQ02-0403)Open Fund of Petroleum Exploration and Development Research Institute of CNPC (2022-KFKT-09)
文摘We propose an integrated method of data-driven and mechanism models for well logging formation evaluation,explicitly focusing on predicting reservoir parameters,such as porosity and water saturation.Accurately interpreting these parameters is crucial for effectively exploring and developing oil and gas.However,with the increasing complexity of geological conditions in this industry,there is a growing demand for improved accuracy in reservoir parameter prediction,leading to higher costs associated with manual interpretation.The conventional logging interpretation methods rely on empirical relationships between logging data and reservoir parameters,which suffer from low interpretation efficiency,intense subjectivity,and suitability for ideal conditions.The application of artificial intelligence in the interpretation of logging data provides a new solution to the problems existing in traditional methods.It is expected to improve the accuracy and efficiency of the interpretation.If large and high-quality datasets exist,data-driven models can reveal relationships of arbitrary complexity.Nevertheless,constructing sufficiently large logging datasets with reliable labels remains challenging,making it difficult to apply data-driven models effectively in logging data interpretation.Furthermore,data-driven models often act as“black boxes”without explaining their predictions or ensuring compliance with primary physical constraints.This paper proposes a machine learning method with strong physical constraints by integrating mechanism and data-driven models.Prior knowledge of logging data interpretation is embedded into machine learning regarding network structure,loss function,and optimization algorithm.We employ the Physically Informed Auto-Encoder(PIAE)to predict porosity and water saturation,which can be trained without labeled reservoir parameters using self-supervised learning techniques.This approach effectively achieves automated interpretation and facilitates generalization across diverse datasets.
基金Supported by the National Natural Science Foundation of China(52288101)National Key R&D Program of China(2024YFF1500600)。
文摘To improve the accuracy and generalization of well logging curve reconstruction,this paper proposes an artificial intelligence large language model“Gaia”and conducts model evaluation experiments.By fine-tuning the pre-trained large language model,the Gaia significantly improved its ability in extracting sequential patterns and spatial features from well-log curves.Leveraging the adapter method for fine-tuning,this model required training only about 1/70 of its original parameters,greatly improving training efficiency.Comparative experiments,ablation experiments,and generalization experiments were designed and conducted using well-log data from 250 wells.In the comparative experiment,the Gaia model was benchmarked against cutting-edge small deep learning models and conventional large language models,demonstrating that the Gaia model reduced the mean absolute error(MAE)by at least 20%.In the ablation experiments,the synergistic effect of the Gaia model's multiple components was validated,with its MAE being at least 30%lower than that of single-component models.In the generalization experiments,the superior performance of the Gaia model in blind-well predictions was further confirmed.Compared to traditional models,the Gaia model is significantly superior in accuracy and generalization for logging curve reconstruction,fully showcasing the potential of large language models in the field of well-logging.This provides a new approach for future intelligent logging data processing.
基金supported By Grant (PLN2022-14) of State Key Laboratory of Oil and Gas Reservoir Geology and Exploitation (Southwest Petroleum University)。
文摘Well logging technology has accumulated a large amount of historical data through four generations of technological development,which forms the basis of well logging big data and digital assets.However,the value of these data has not been well stored,managed and mined.With the development of cloud computing technology,it provides a rare development opportunity for logging big data private cloud.The traditional petrophysical evaluation and interpretation model has encountered great challenges in the face of new evaluation objects.The solution research of logging big data distributed storage,processing and learning functions integrated in logging big data private cloud has not been carried out yet.To establish a distributed logging big-data private cloud platform centered on a unifi ed learning model,which achieves the distributed storage and processing of logging big data and facilitates the learning of novel knowledge patterns via the unifi ed logging learning model integrating physical simulation and data models in a large-scale functional space,thus resolving the geo-engineering evaluation problem of geothermal fi elds.Based on the research idea of“logging big data cloud platform-unifi ed logging learning model-large function space-knowledge learning&discovery-application”,the theoretical foundation of unified learning model,cloud platform architecture,data storage and learning algorithm,arithmetic power allocation and platform monitoring,platform stability,data security,etc.have been carried on analysis.The designed logging big data cloud platform realizes parallel distributed storage and processing of data and learning algorithms.The feasibility of constructing a well logging big data cloud platform based on a unifi ed learning model of physics and data is analyzed in terms of the structure,ecology,management and security of the cloud platform.The case study shows that the logging big data cloud platform has obvious technical advantages over traditional logging evaluation methods in terms of knowledge discovery method,data software and results sharing,accuracy,speed and complexity.
基金financially supported by the Russian federal research project No.FWZZ-2022-0026“Innovative aspects of electro-dynamics in problems of exploration and oilfield geophysics”.
文摘We propose a novel workflow for fast forward modeling of well logs in axially symmetric 2D models of the nearwellbore environment.The approach integrates the finite element method with deep residual neural networks to achieve exceptional computational efficiency and accuracy.The workflow is demonstrated through the modeling of wireline electromagnetic propagation resistivity logs,where the measured responses exhibit a highly nonlinear relationship with formation properties.The motivation for this research is the need for advanced modeling al-gorithms that are fast enough for use in modern quantitative interpretation tools,where thousands of simulations may be required in iterative inversion processes.The proposed algorithm achieves a remarkable enhancement in performance,being up to 3000 times faster than the finite element method alone when utilizing a GPU.While still ensuring high accuracy,this makes it well-suited for practical applications when reliable payzone assessment is needed in complex environmental scenarios.Furthermore,the algorithm’s efficiency positions it as a promising tool for stochastic Bayesian inversion,facilitating reliable uncertainty quantification in subsurface property estimation.
基金supported by the National Natural Science Foundation of China (grant nos. 42074137)。
文摘A cased well model consists of a coaxial tank and casing,which houses coaxially installed transmitting and receiving coils.The transmitting coil is excited by the current produced by the transmitting circuit,and transient electromagnetic responses occur in the casing,including direct coupling and casing responses.As the range between the transmitting and receiving coils increases,direct coupling responses decay rapidly,are less than the casing response at 0.3 m,and disappear at 0.7 m.By contrast,a casing response increases rapidly and then declines slowly after reaching a peak and changes little within a specifi c range.The peak decreases slowly with range.The continuous addition of water to the tank causes slight changes in transient electromagnetic responses,so the diff erence which are subtracted from the response without water is used.Moreover,the diff erences at the time of rapid increase in response and the time of rapid decrease in response are large,forming a peak and a trough.Given that the conductivity of water in a full tank changes after the addition of salt,the diff erence in the peak is linear with the increase in the conductivity of water.This result provides an experimental basis for the design of a transient electromagnetic logging instrument that measures the conductivity of formation in cased well.
基金supported by the National Natural Science Foundation of China(No.31660234).
文摘Global forest cover is undergoing significant transformations due to anthropogenic activities and natural disturbances,profoundly impacting hydrological processes.However,the inherent spatial heterogeneity within watersheds leads to varied hydrological responses across spatiotemporal scales,challenging comprehensive assessment of logging impacts at the watershed scale.Here,we developed multiple forest logging scenarios using the soil and water assessment tool(SWAT)model for the Le'an River watershed,a 5,837 km2 subtropical watershed in China,to quantify the hydrological effects of forest logging across different spatiotemporal scales.Our results demonstrate that increasing forest logging ratios from 1.54% to 9.25% consistently enhanced ecohydrological sensitivity.However,sensitivity varied across spatiotemporal scales,with the rainy season(15.30%-15.81%)showing higher sensitivity than annual(11.56%-12.07%)and dry season(3.38%-5.57%)periods.Additionally,the ecohydrological sensitivity of logging varied significantly across the watershed,with midstream areas exhibiting the highest sensitivity(13.13%-13.25%),followed by downstream(11.87%-11.98%)and upstream regions(9.96%-10.05%).Furthermore,the whole watershed exhibited greater hydrological resilience to logging compared to upstream areas,with attenuated runoff changes due to scale effects.Scale effects were more pronounced during dry seasons((-8.13 to -42.13)×10^(4) m^(3)·month^(-1))than in the rainy season((-11.11 to -26.65)×10^(4) m^(3)·month^(-1)).These findings advance understanding of logging impacts on hydrology across different spatiotemporal scales in subtropical regions,providing valuable insights for forest management under increasing anthropogenic activities and climate change.
基金supported by the National Natural Science Foundation of China(61170078,61472228,61903229,61902222)the “Taishan Scholar” Construction Project of Shandong Province,China,the Natural Science Foundation of Shandong Province(ZR2018MF001)+1 种基金the Scientific Research Foundation of Shandong University of Science and Technology for Recruited Talents(2017RCJJ044)the Key Research and Development Program of Shandong Province(2018GGX101011)
文摘Business processes described by formal or semi-formal models are realized via information systems.Event logs generated from these systems are probably not consistent with the existing models due to insufficient design of the information system or the system upgrade.By comparing an existing process model with event logs,we can detect inconsistencies called deviations,verify and extend the business process model,and accordingly improve the business process.In this paper,some abnormal activities in business processes are formally defined based on Petri nets.An efficient approach to detect deviations between the process model and event logs is proposed.Then,business process models are revised when abnormal activities exist.A clinical process in a healthcare information system is used as a case study to illustrate our work.Experimental results show the effectiveness and efficiency of the proposed approach.
基金Supported by the Gastric Cancer Laboratory and Pathology Department of Chinese Medical University,Shenyang,Chinathe Science and Technology Program of Shenyang,No. 1081232-1-00
文摘AIM:To investigate the efficiency of Cox proportional hazard model in detecting prognostic factors for gastric cancer.METHODS:We used the log-normal regression model to evaluate prognostic factors in gastric cancer and compared it with the Cox model.Three thousand and eighteen gastric cancer patients who received a gastrectomy between 1980 and 2004 were retrospectively evaluated.Clinic-pathological factors were included in a log-normal model as well as Cox model.The akaike information criterion (AIC) was employed to compare the efficiency of both models.Univariate analysis indicated that age at diagnosis,past history,cancer location,distant metastasis status,surgical curative degree,combined other organ resection,Borrmann type,Lauren's classification,pT stage,total dissected nodes and pN stage were prognostic factors in both log-normal and Cox models.RESULTS:In the final multivariate model,age at diagnosis,past history,surgical curative degree,Borrmann type,Lauren's classification,pT stage,and pN stage were significant prognostic factors in both log-normal and Cox models.However,cancer location,distant metastasis status,and histology types were found to be significant prognostic factors in log-normal results alone.According to AIC,the log-normal model performed better than the Cox proportional hazard model (AIC value:2534.72 vs 1693.56).CONCLUSION:It is suggested that the log-normal regression model can be a useful statistical model to evaluate prognostic factors instead of the Cox proportional hazard model.
基金Supported by the National Natural Science FoundatiOn of China
文摘The numerical solution of Green’s function for the potential in 2-D arbitrary in-homogeneous media with axial symmetry has been given by use of efficient half-analytical, half-numerical hybrid method. Then the logging responses of various kinds of the DC resistivity log with axisymmetric excitation have been obtained by using surface integral equation method to match the boundary conditions on the electrodes of the logging sonde. Comparing the results with that obtained by other methods, one can see good precision and efficiency of the given method. Some applications of the numerical modeling have been also discussed.
基金supported by the National Natural Science Foundation of China (No. 10431030)the Shanghai Natural Science Foundation (No. 08ZR1401100)
文摘This paper introduces a new spontaneous potential log model for the case in which formation resistivity is not piecewise constant. The spontaneous potential satisfies an elliptic boundary value problem with jump conditions on the interfaces. It has beer/ shown that the elliptic interface problem has a unique weak solution. Furthermore, a jump condition capturing finite difference scheme is proposed and applied to solve such elliptic problems. Numerical results show validity and effectiveness of the proposed method.
