A 2D micro-mechanical model was proposed to study the compressive failure of Uni Directional（UD） carbon/epoxy composite. Considering the initial imperfection and strength distribution of the fiber, the plasticity an...A 2D micro-mechanical model was proposed to study the compressive failure of Uni Directional（UD） carbon/epoxy composite. Considering the initial imperfection and strength distribution of the fiber, the plasticity and ductile damage of the matrix, the failure of T300/914 UD composite under longitudinal compression and in-plane combined loads was simulated by this model. Simulation results show that the longitudinal compressive failure of the UD composite is caused by the plastic yielding of the matrix in kink band, and the fiber initial imperfection is the main reason for it. Under in-plane combined loads, the stress state of the matrix in kink band is changed, which affects the longitudinal compressive failure modes and strength of UD composite.The failure envelope of r_1–s_（12） and r_1–r_2 are obtained by the micro-mechanical model. Meanwhile,the compressive failure mechanism of the UD composite is analyzed. Numerical results agree well with the experimental data, which verifies the validity of the micro-mechanical model.展开更多
A new unified macro- and micro-mechanics failure analysis method for composite structures was developed in order to take the effects of composite micro structure into consideration. In this method, the macro stress di...A new unified macro- and micro-mechanics failure analysis method for composite structures was developed in order to take the effects of composite micro structure into consideration. In this method, the macro stress distribution of composite structure was calculated by commercial finite element analysis software. According to the macro stress distribution, the damage point was searched and the micro-stress distribution was calculated by reformulated finite-volume direct averaging micromechanics （FVDAM）, which was a multi-scale finite element method for composite. The micro structure failure modes were estimated with the failure strength of constituents. A unidirectional composite plate with a circular hole in the center under two kinds of loads was analyzed with the traditional macro-mechanical failure analysis method and the unified macro- and micro-mechanics failure analysis method. The results obtained by the two methods are consistent, which show this new method＇s accuracy and efficiency.展开更多
The warp yarns and weft yarns of plain woven fabric which,being the principal axes of material of fabric,are orthogonal in the original configuration,but are obliquely crossed in the deformed configuration in general....The warp yarns and weft yarns of plain woven fabric which,being the principal axes of material of fabric,are orthogonal in the original configuration,but are obliquely crossed in the deformed configuration in general.The orthotropic constitutive model is unsuitable for fabric.In the oblique principal axes system the relations between loaded stress vectors and stress tensor are investigated,the stress fields of micro-weaving structures of fabric due to pure shear are carefully studied and,finally,a nonlinear micro-mechanical model for plain woven fabric is proposed.This model can accurately describe the nonlinear mechanical behavior of fabric observed m experimeuts.Under the assumption of small deformation and linearity of mechanical properties of fabric the model will degenerate into the existing linear model.展开更多
Warp yarns and weft yarns of plain woven fabric are the principal axes of mate- rial of fabric. They are orthogonal in their original con?guration, but are obliquely crisscross in deformed con?guration in general. I...Warp yarns and weft yarns of plain woven fabric are the principal axes of mate- rial of fabric. They are orthogonal in their original con?guration, but are obliquely crisscross in deformed con?guration in general. In this paper the expressions of incremental components of strain tensor are derived, the non-linear model of woven fabric is linearized physically and its geometric non-linearity survives. The convenience of determining the total deformation is shown by the choice of the coordinate system of the principal axes of the material, with the convergence of the incremental methods illustrated by examples. This incremental model furnishes a basis for numerical simulations of fabric draping and wrinkling based on the micro-mechanical model of fabric.展开更多
To predict the Elasto-Plastic Behaviors(EPBs)of aligned inclusions reinforced composites,this paper develops an interpolative Mori-Tanaka/Double-Inclusion(MT-DI)homogenization model with the secant formulation,and giv...To predict the Elasto-Plastic Behaviors(EPBs)of aligned inclusions reinforced composites,this paper develops an interpolative Mori-Tanaka/Double-Inclusion(MT-DI)homogenization model with the secant formulation,and gives the numerical implementation algorithms of the developed MT-DI model with the secant formulation.The Finite Element(FE)homogenization method is implemented to provide the "exact" EPBs of the composites and thus validate the MT-DI model with the secant formulation.The MT-DI model with the 2 nd-order secant formulation is validated to provide the more accurate predictions,while the MT-DI model with the 1 st-order secant formulation always gives the stiffer predictions.The results show that using the macro-stress and macrostrain as the inputs,the MT-DI model with the secant formulation gives the identical predictions.The predictions of the MT-DI model with the secant formulation vary between those of the MT and DI models with the secant formulation.Meanwhile,the MT-DI model with the secant formulation does not predict the accurate EPBs for the phases of the composites.展开更多
In recent years, the micro-deformation mechanisms of the tension/compression behavior for nickel-base single-crystal superalloys have been studied extensively and general agreements have been derived. Based on these r...In recent years, the micro-deformation mechanisms of the tension/compression behavior for nickel-base single-crystal superalloys have been studied extensively and general agreements have been derived. Based on these researches, a new model called united tension/compression asymmetry micro-mechanical model (UTCAM) has been proposed, which can effectively estimate the initial yield strength of nickel-base single-crystal (SC) superalloys under different loading directions. Considering the combined effects of octahedral slip system and cubic slip system, slip control factor is introduced in the UTCAM to determine the type of the open slip system of nickel-base single-crystal superalloys during deformation, thus making this model cover a rather wide range of application. Furthermore, the UTCAM is applied to hot tension and compression tests of three typical nickel-base SC superalloys (PWA1480-593 ℃, RENE N4-760 ℃ and DD407-760 ℃). The predicted initial yield strengths of the nickel-base SC superalloys are in good agreement with the experimental results, and the UTCAM proves to be effective.展开更多
With the application of 2.5D Woven Variable Thickness Composites(2.5DWVTC)in aviation and other fields,the issue of strength failure in this composite type has become a focal point.First,a three-step modeling approach...With the application of 2.5D Woven Variable Thickness Composites(2.5DWVTC)in aviation and other fields,the issue of strength failure in this composite type has become a focal point.First,a three-step modeling approach is proposed to rapidly construct full-scale meso-finite element models for Outer Reduction Yarn Woven Composites(ORYWC)and Inner Reduction Yarn Woven Composites(IRYWC).Then,six independent damage variables are identified:yarn fiber tension/compression,yarn matrix tension/compression,and resin matrix tension/compression.These variables are utilized to establish the constitutive equation of woven composites,considering the coupling effects of microscopic damage.Finally,combined with the Hashin failure criterion and von Mises failure criterion,the strength prediction model is implemented in ANSYS using APDL language to simulate the strength failure process of 2.5DWVTC.The results show that the predicted stiffness and strength values of various parts of ORYWC and IRYWC are in good agreement with the relevant test results.展开更多
The mechanism of SC-CO_(2)-brine-rock interaction(SCBRI) and its effect on the mechanical properties of shale are crucial for shale oil development and CO_(2)sequestration. To clarify the influence of SCBRI on the mic...The mechanism of SC-CO_(2)-brine-rock interaction(SCBRI) and its effect on the mechanical properties of shale are crucial for shale oil development and CO_(2)sequestration. To clarify the influence of SCBRI on the micromechanics of shale, the lamina and matrix of shale were saturated with SC-CO_(2)-brine for 2, 4, 6,and 8 days, respectively. The micro-scratch technique was then used to measure the localized fracture toughness before and after SC-CO_(2)-brine saturation. Combining the micro-scratch results with SEM-QEMSCAN-EDS analysis, the differences in mineral composition and mechanical properties of lamina(primarily composed of carbonate minerals) and matrix(primarily composed of clay minerals) were studied. The QEMSCAN analysis and micro-scratch results indicate distinct mineralogical compositions and mechanical properties between the lamina and the matrix. The results showed that:(1) SCBRI leads to the decrease in carbonate mineral content and the significant increase in matrix porosity and laminar cracks. In addition, the damage degree increased at saturation for 6 days.(2) SCBRI weakens the mechanical properties of shale. The scratch depth of laminar and matrix increased by 34.38% and 1.02%, and the fracture toughness decreased by 34.38% and 13.11%. It showed a trend of first increasing and then decreasing.(3) SCBRI enhances the plastic deformation behavior of shale, and the plastic index of lamina and matrix increases by 18.75% and 21.58%, respectively. These results are of great significance for evaluating the mechanical properties of shale oil and gas extraction by CO_(2).展开更多
BACKGROUND Rebleeding after recovery from esophagogastric variceal bleeding(EGVB)is a severe complication that is associated with high rates of both incidence and mortality.Despite its clinical importance,recognized p...BACKGROUND Rebleeding after recovery from esophagogastric variceal bleeding(EGVB)is a severe complication that is associated with high rates of both incidence and mortality.Despite its clinical importance,recognized prognostic models that can effectively predict esophagogastric variceal rebleeding in patients with liver cirrhosis are lacking.AIM To construct and externally validate a reliable prognostic model for predicting the occurrence of esophagogastric variceal rebleeding.METHODS This study included 477 EGVB patients across 2 cohorts:The derivation cohort(n=322)and the validation cohort(n=155).The primary outcome was rebleeding events within 1 year.The least absolute shrinkage and selection operator was applied for predictor selection,and multivariate Cox regression analysis was used to construct the prognostic model.Internal validation was performed with bootstrap resampling.We assessed the discrimination,calibration and accuracy of the model,and performed patient risk stratification.RESULTS Six predictors,including albumin and aspartate aminotransferase concentrations,white blood cell count,and the presence of ascites,portal vein thrombosis,and bleeding signs,were selected for the rebleeding event prediction following endoscopic treatment(REPET)model.In predicting rebleeding within 1 year,the REPET model ex-hibited a concordance index of 0.775 and a Brier score of 0.143 in the derivation cohort,alongside 0.862 and 0.127 in the validation cohort.Furthermore,the REPET model revealed a significant difference in rebleeding rates(P<0.01)between low-risk patients and intermediate-to high-risk patients in both cohorts.CONCLUSION We constructed and validated a new prognostic model for variceal rebleeding with excellent predictive per-formance,which will improve the clinical management of rebleeding in EGVB patients.展开更多
This study was aimed to prepare landslide susceptibility maps for the Pithoragarh district in Uttarakhand,India,using advanced ensemble models that combined Radial Basis Function Networks(RBFN)with three ensemble lear...This study was aimed to prepare landslide susceptibility maps for the Pithoragarh district in Uttarakhand,India,using advanced ensemble models that combined Radial Basis Function Networks(RBFN)with three ensemble learning techniques:DAGGING(DG),MULTIBOOST(MB),and ADABOOST(AB).This combination resulted in three distinct ensemble models:DG-RBFN,MB-RBFN,and AB-RBFN.Additionally,a traditional weighted method,Information Value(IV),and a benchmark machine learning(ML)model,Multilayer Perceptron Neural Network(MLP),were employed for comparison and validation.The models were developed using ten landslide conditioning factors,which included slope,aspect,elevation,curvature,land cover,geomorphology,overburden depth,lithology,distance to rivers and distance to roads.These factors were instrumental in predicting the output variable,which was the probability of landslide occurrence.Statistical analysis of the models’performance indicated that the DG-RBFN model,with an Area Under ROC Curve(AUC)of 0.931,outperformed the other models.The AB-RBFN model achieved an AUC of 0.929,the MB-RBFN model had an AUC of 0.913,and the MLP model recorded an AUC of 0.926.These results suggest that the advanced ensemble ML model DG-RBFN was more accurate than traditional statistical model,single MLP model,and other ensemble models in preparing trustworthy landslide susceptibility maps,thereby enhancing land use planning and decision-making.展开更多
The micro-mechanical response of asphalt mixtures was studied using the discrete element method. The discrete element sample of stone mastic asphalt was generated first and the vehicle load was applied to the sample. ...The micro-mechanical response of asphalt mixtures was studied using the discrete element method. The discrete element sample of stone mastic asphalt was generated first and the vehicle load was applied to the sample. A user-written program was coded with the FISH language in PFC3 D to extract the contact forces within the sample and the displacements of the particles. Then, the contact forces within the whole sample, in asphalt mastic, in coarse aggregates and between asphalt mastic and coarse aggregates were investigated. Finally, the movement of the particles in the sample was analyzed. The sample was divided into 15 areas and a figure was drawn to show how the balls move in each area according to the displacements of the balls in each area. The displacements of asphalt mastic balls and coarse aggregates were also analyzed. The experimental results explain how the asphalt mixture bears vehicle load and the potential reasons why the rutting forms from a micro-mechanical view.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
Conducting predictability studies is essential for tracing the source of forecast errors,which not only leads to the improvement of observation and forecasting systems,but also enhances the understanding of weather an...Conducting predictability studies is essential for tracing the source of forecast errors,which not only leads to the improvement of observation and forecasting systems,but also enhances the understanding of weather and climate phenomena.In the past few decades,dynamical numerical models have been the primary tools for predictability studies,achieving significant progress.Nowadays,with the advances in artificial intelligence(AI)techniques and accumulations of vast meteorological data,modeling weather and climate events using modern data-driven approaches is becoming trendy,where FourCastNet,Pangu-Weather,and GraphCast are successful pioneers.In this perspective article,we suggest AI models should not be limited to forecasting but be expanded to predictability studies,leveraging AI's advantages of high efficiency and self-contained optimization modules.To this end,we first remark that AI models should possess high simulation capability with fine spatiotemporal resolution for two kinds of predictability studies.AI models with high simulation capabilities comparable to numerical models can be considered to provide solutions to partial differential equations in a data-driven way.Then,we highlight several specific predictability issues with well-determined nonlinear optimization formulizations,which can be well-studied using AI models,holding significant scientific value.In addition,we advocate for the incorporation of AI models into the synergistic cycle of the cognition–observation–model paradigm.Comprehensive predictability studies have the potential to transform“big data”to“big and better data”and shift the focus from“AI for forecasts”to“AI for science”,ultimately advancing the development of the atmospheric and oceanic sciences.展开更多
Developing sensorless techniques for estimating battery expansion is essential for effective mechanical state monitoring,improving the accuracy of digital twin simulation and abnormality detection.Therefore,this paper...Developing sensorless techniques for estimating battery expansion is essential for effective mechanical state monitoring,improving the accuracy of digital twin simulation and abnormality detection.Therefore,this paper presents a data-driven approach to expansion estimation using electromechanical coupled models with machine learning.The proposed method integrates reduced-order impedance models with data-driven mechanical models,coupling the electrochemical and mechanical states through the state of charge(SOC)and mechanical pressure within a state estimation framework.The coupling relationship was established through experimental insights into pressure-related impedance parameters and the nonlinear mechanical behavior with SOC and pressure.The data-driven model was interpreted by introducing a novel swelling coefficient defined by component stiffnesses to capture the nonlinear mechanical behavior across various mechanical constraints.Sensitivity analysis of the impedance model shows that updating model parameters with pressure can reduce the mean absolute error of simulated voltage by 20 mV and SOC estimation error by 2%.The results demonstrate the model's estimation capabilities,achieving a root mean square error of less than 1 kPa when the maximum expansion force is from 30 kPa to 120 kPa,outperforming calibrated stiffness models and other machine learning techniques.The model's robustness and generalizability are further supported by its effective handling of SOC estimation and pressure measurement errors.This work highlights the importance of the proposed framework in enhancing state estimation and fault diagnosis for lithium-ion batteries.展开更多
As a key node of modern transportation network,the informationization management of road tunnels is crucial to ensure the operation safety and traffic efficiency.However,the existing tunnel vehicle modeling methods ge...As a key node of modern transportation network,the informationization management of road tunnels is crucial to ensure the operation safety and traffic efficiency.However,the existing tunnel vehicle modeling methods generally have problems such as insufficient 3D scene description capability and low dynamic update efficiency,which are difficult to meet the demand of real-time accurate management.For this reason,this paper proposes a vehicle twin modeling method for road tunnels.This approach starts from the actual management needs,and supports multi-level dynamic modeling from vehicle type,size to color by constructing a vehicle model library that can be flexibly invoked;at the same time,semantic constraint rules with geometric layout,behavioral attributes,and spatial relationships are designed to ensure that the virtual model matches with the real model with a high degree of similarity;ultimately,the prototype system is constructed and the case region is selected for the case study,and the dynamic vehicle status in the tunnel is realized by integrating real-time monitoring data with semantic constraints for precise virtual-real mapping.Finally,the prototype system is constructed and case experiments are conducted in selected case areas,which are combined with real-time monitoring data to realize dynamic updating and three-dimensional visualization of vehicle states in tunnels.The experiments show that the proposed method can run smoothly with an average rendering efficiency of 17.70 ms while guaranteeing the modeling accuracy(composite similarity of 0.867),which significantly improves the real-time and intuitive tunnel management.The research results provide reliable technical support for intelligent operation and emergency response of road tunnels,and offer new ideas for digital twin modeling of complex scenes.展开更多
Large language models(LLMs)have undergone significant expansion and have been increasingly integrated across various domains.Notably,in the realm of robot task planning,LLMs harness their advanced reasoning and langua...Large language models(LLMs)have undergone significant expansion and have been increasingly integrated across various domains.Notably,in the realm of robot task planning,LLMs harness their advanced reasoning and language comprehension capabilities to formulate precise and efficient action plans based on natural language instructions.However,for embodied tasks,where robots interact with complex environments,textonly LLMs often face challenges due to a lack of compatibility with robotic visual perception.This study provides a comprehensive overview of the emerging integration of LLMs and multimodal LLMs into various robotic tasks.Additionally,we propose a framework that utilizes multimodal GPT-4V to enhance embodied task planning through the combination of natural language instructions and robot visual perceptions.Our results,based on diverse datasets,indicate that GPT-4V effectively enhances robot performance in embodied tasks.This extensive survey and evaluation of LLMs and multimodal LLMs across a variety of robotic tasks enriches the understanding of LLM-centric embodied intelligence and provides forward-looking insights towards bridging the gap in Human-Robot-Environment interaction.展开更多
To examine the similarities and differences in the evolution of cavity,wetting and dynamics of a highspeed,oblique water-entry projectile with different positive angles of attack,a comparative analysis has been conduc...To examine the similarities and differences in the evolution of cavity,wetting and dynamics of a highspeed,oblique water-entry projectile with different positive angles of attack,a comparative analysis has been conducted based on the numerical results of two mathematical models,the rigid-body model and fluid-structure interaction model.In addition,the applicable scope of the above two methods,and the structural response characteristics of the projectile have also been investigated.Our results demonstrate that:(1) The impact loads and angular motion of the projectile of the rigid-body method are more likely to exhibit periodic variations due to the periodic tail slap,its range of positive angles of attack is about α<2°.(2) When the projectile undergone significant wetting,a strong coupling effect is observed among wetting,structural deformation,and projectile motion.With the applied projectile shape,it is observed that,when the projectile bends,the final wetting position is that of Part B(cylinder of body).With the occu rrence of this phenomenon,the projectile ballistics beco me completely unstable.(3) The force exerted on the lower surface of the projectile induced by wetting is the primary reason of the destabilization of the projectile traj ectory and structu ral deformation failure.Bending deformation is most likely to appear at the junction of Part C(cone of body) and Part D(tail).The safe angles of attack of the projectile stability are found to be about α≤2°.展开更多
文摘A 2D micro-mechanical model was proposed to study the compressive failure of Uni Directional（UD） carbon/epoxy composite. Considering the initial imperfection and strength distribution of the fiber, the plasticity and ductile damage of the matrix, the failure of T300/914 UD composite under longitudinal compression and in-plane combined loads was simulated by this model. Simulation results show that the longitudinal compressive failure of the UD composite is caused by the plastic yielding of the matrix in kink band, and the fiber initial imperfection is the main reason for it. Under in-plane combined loads, the stress state of the matrix in kink band is changed, which affects the longitudinal compressive failure modes and strength of UD composite.The failure envelope of r_1–s_（12） and r_1–r_2 are obtained by the micro-mechanical model. Meanwhile,the compressive failure mechanism of the UD composite is analyzed. Numerical results agree well with the experimental data, which verifies the validity of the micro-mechanical model.
基金co-supported by National Basic Research Program of China, National Natural Science Foundation of China(No. 51075204)Aeronautical Science Foundation of China (No.2009ZB52028, No. 2012ZB52026)+1 种基金Research Fund for the Doctoral Program of Higher Education of China (No. 20070287039)NUAA Research Funding (No. NZ2012106)
文摘A new unified macro- and micro-mechanics failure analysis method for composite structures was developed in order to take the effects of composite micro structure into consideration. In this method, the macro stress distribution of composite structure was calculated by commercial finite element analysis software. According to the macro stress distribution, the damage point was searched and the micro-stress distribution was calculated by reformulated finite-volume direct averaging micromechanics （FVDAM）, which was a multi-scale finite element method for composite. The micro structure failure modes were estimated with the failure strength of constituents. A unidirectional composite plate with a circular hole in the center under two kinds of loads was analyzed with the traditional macro-mechanical failure analysis method and the unified macro- and micro-mechanics failure analysis method. The results obtained by the two methods are consistent, which show this new method＇s accuracy and efficiency.
基金Project supported by the National Natural Science Foundation of China (No.10272079).
文摘The warp yarns and weft yarns of plain woven fabric which,being the principal axes of material of fabric,are orthogonal in the original configuration,but are obliquely crossed in the deformed configuration in general.The orthotropic constitutive model is unsuitable for fabric.In the oblique principal axes system the relations between loaded stress vectors and stress tensor are investigated,the stress fields of micro-weaving structures of fabric due to pure shear are carefully studied and,finally,a nonlinear micro-mechanical model for plain woven fabric is proposed.This model can accurately describe the nonlinear mechanical behavior of fabric observed m experimeuts.Under the assumption of small deformation and linearity of mechanical properties of fabric the model will degenerate into the existing linear model.
基金Project supported by the National Natural Science Foundation of China (No. 10272079).
文摘Warp yarns and weft yarns of plain woven fabric are the principal axes of mate- rial of fabric. They are orthogonal in their original con?guration, but are obliquely crisscross in deformed con?guration in general. In this paper the expressions of incremental components of strain tensor are derived, the non-linear model of woven fabric is linearized physically and its geometric non-linearity survives. The convenience of determining the total deformation is shown by the choice of the coordinate system of the principal axes of the material, with the convergence of the incremental methods illustrated by examples. This incremental model furnishes a basis for numerical simulations of fabric draping and wrinkling based on the micro-mechanical model of fabric.
基金the financial support from the Natural Science Basic Research Program of Shaanxi(No.2019JQ857)the Doctoral Scientific Research Foundation of Xi’an Polytechnic University(No.BS201863)+1 种基金the China Textile Industry Federation Science and Technology Guidance Project(No.2019002)the Tianyuan Open Fund Project(No.tywl2019-14)。
文摘To predict the Elasto-Plastic Behaviors(EPBs)of aligned inclusions reinforced composites,this paper develops an interpolative Mori-Tanaka/Double-Inclusion(MT-DI)homogenization model with the secant formulation,and gives the numerical implementation algorithms of the developed MT-DI model with the secant formulation.The Finite Element(FE)homogenization method is implemented to provide the "exact" EPBs of the composites and thus validate the MT-DI model with the secant formulation.The MT-DI model with the 2 nd-order secant formulation is validated to provide the more accurate predictions,while the MT-DI model with the 1 st-order secant formulation always gives the stiffer predictions.The results show that using the macro-stress and macrostrain as the inputs,the MT-DI model with the secant formulation gives the identical predictions.The predictions of the MT-DI model with the secant formulation vary between those of the MT and DI models with the secant formulation.Meanwhile,the MT-DI model with the secant formulation does not predict the accurate EPBs for the phases of the composites.
基金supported by National Natural Science Foundation of China(51205190)the Fundamental Research Funds for the Central Universities(No.NS2016026)+1 种基金the Aeronautical Power Science Fund Project(6141B090317)the Innovation Fund of Jiangsu Province,China(KYLX-0304).
文摘In recent years, the micro-deformation mechanisms of the tension/compression behavior for nickel-base single-crystal superalloys have been studied extensively and general agreements have been derived. Based on these researches, a new model called united tension/compression asymmetry micro-mechanical model (UTCAM) has been proposed, which can effectively estimate the initial yield strength of nickel-base single-crystal (SC) superalloys under different loading directions. Considering the combined effects of octahedral slip system and cubic slip system, slip control factor is introduced in the UTCAM to determine the type of the open slip system of nickel-base single-crystal superalloys during deformation, thus making this model cover a rather wide range of application. Furthermore, the UTCAM is applied to hot tension and compression tests of three typical nickel-base SC superalloys (PWA1480-593 ℃, RENE N4-760 ℃ and DD407-760 ℃). The predicted initial yield strengths of the nickel-base SC superalloys are in good agreement with the experimental results, and the UTCAM proves to be effective.
基金supported by National Science and Technology Major Project,China(No.2017-IV-0007-0044)National Natural Science Foundation of China(No.52175142),National Natural Science Foundation of China(No.52305170)Natural Science Foundation of Sichuan Province,China(No.2022NSFSC1885)。
文摘With the application of 2.5D Woven Variable Thickness Composites(2.5DWVTC)in aviation and other fields,the issue of strength failure in this composite type has become a focal point.First,a three-step modeling approach is proposed to rapidly construct full-scale meso-finite element models for Outer Reduction Yarn Woven Composites(ORYWC)and Inner Reduction Yarn Woven Composites(IRYWC).Then,six independent damage variables are identified:yarn fiber tension/compression,yarn matrix tension/compression,and resin matrix tension/compression.These variables are utilized to establish the constitutive equation of woven composites,considering the coupling effects of microscopic damage.Finally,combined with the Hashin failure criterion and von Mises failure criterion,the strength prediction model is implemented in ANSYS using APDL language to simulate the strength failure process of 2.5DWVTC.The results show that the predicted stiffness and strength values of various parts of ORYWC and IRYWC are in good agreement with the relevant test results.
基金supported by the National Natural Science Foundation of China (52374014)the Fundamental Research Funds for the Central Universities (2024ZKPYSB03)。
文摘The mechanism of SC-CO_(2)-brine-rock interaction(SCBRI) and its effect on the mechanical properties of shale are crucial for shale oil development and CO_(2)sequestration. To clarify the influence of SCBRI on the micromechanics of shale, the lamina and matrix of shale were saturated with SC-CO_(2)-brine for 2, 4, 6,and 8 days, respectively. The micro-scratch technique was then used to measure the localized fracture toughness before and after SC-CO_(2)-brine saturation. Combining the micro-scratch results with SEM-QEMSCAN-EDS analysis, the differences in mineral composition and mechanical properties of lamina(primarily composed of carbonate minerals) and matrix(primarily composed of clay minerals) were studied. The QEMSCAN analysis and micro-scratch results indicate distinct mineralogical compositions and mechanical properties between the lamina and the matrix. The results showed that:(1) SCBRI leads to the decrease in carbonate mineral content and the significant increase in matrix porosity and laminar cracks. In addition, the damage degree increased at saturation for 6 days.(2) SCBRI weakens the mechanical properties of shale. The scratch depth of laminar and matrix increased by 34.38% and 1.02%, and the fracture toughness decreased by 34.38% and 13.11%. It showed a trend of first increasing and then decreasing.(3) SCBRI enhances the plastic deformation behavior of shale, and the plastic index of lamina and matrix increases by 18.75% and 21.58%, respectively. These results are of great significance for evaluating the mechanical properties of shale oil and gas extraction by CO_(2).
基金Supported by National Natural Science Foundation of China,No.81874390 and No.81573948Shanghai Natural Science Foundation,No.21ZR1464100+1 种基金Science and Technology Innovation Action Plan of Shanghai Science and Technology Commission,No.22S11901700the Shanghai Key Specialty of Traditional Chinese Clinical Medicine,No.shslczdzk01201.
文摘BACKGROUND Rebleeding after recovery from esophagogastric variceal bleeding(EGVB)is a severe complication that is associated with high rates of both incidence and mortality.Despite its clinical importance,recognized prognostic models that can effectively predict esophagogastric variceal rebleeding in patients with liver cirrhosis are lacking.AIM To construct and externally validate a reliable prognostic model for predicting the occurrence of esophagogastric variceal rebleeding.METHODS This study included 477 EGVB patients across 2 cohorts:The derivation cohort(n=322)and the validation cohort(n=155).The primary outcome was rebleeding events within 1 year.The least absolute shrinkage and selection operator was applied for predictor selection,and multivariate Cox regression analysis was used to construct the prognostic model.Internal validation was performed with bootstrap resampling.We assessed the discrimination,calibration and accuracy of the model,and performed patient risk stratification.RESULTS Six predictors,including albumin and aspartate aminotransferase concentrations,white blood cell count,and the presence of ascites,portal vein thrombosis,and bleeding signs,were selected for the rebleeding event prediction following endoscopic treatment(REPET)model.In predicting rebleeding within 1 year,the REPET model ex-hibited a concordance index of 0.775 and a Brier score of 0.143 in the derivation cohort,alongside 0.862 and 0.127 in the validation cohort.Furthermore,the REPET model revealed a significant difference in rebleeding rates(P<0.01)between low-risk patients and intermediate-to high-risk patients in both cohorts.CONCLUSION We constructed and validated a new prognostic model for variceal rebleeding with excellent predictive per-formance,which will improve the clinical management of rebleeding in EGVB patients.
基金the University of Transport Technology under the project entitled“Application of Machine Learning Algorithms in Landslide Susceptibility Mapping in Mountainous Areas”with grant number DTTD2022-16.
文摘This study was aimed to prepare landslide susceptibility maps for the Pithoragarh district in Uttarakhand,India,using advanced ensemble models that combined Radial Basis Function Networks(RBFN)with three ensemble learning techniques:DAGGING(DG),MULTIBOOST(MB),and ADABOOST(AB).This combination resulted in three distinct ensemble models:DG-RBFN,MB-RBFN,and AB-RBFN.Additionally,a traditional weighted method,Information Value(IV),and a benchmark machine learning(ML)model,Multilayer Perceptron Neural Network(MLP),were employed for comparison and validation.The models were developed using ten landslide conditioning factors,which included slope,aspect,elevation,curvature,land cover,geomorphology,overburden depth,lithology,distance to rivers and distance to roads.These factors were instrumental in predicting the output variable,which was the probability of landslide occurrence.Statistical analysis of the models’performance indicated that the DG-RBFN model,with an Area Under ROC Curve(AUC)of 0.931,outperformed the other models.The AB-RBFN model achieved an AUC of 0.929,the MB-RBFN model had an AUC of 0.913,and the MLP model recorded an AUC of 0.926.These results suggest that the advanced ensemble ML model DG-RBFN was more accurate than traditional statistical model,single MLP model,and other ensemble models in preparing trustworthy landslide susceptibility maps,thereby enhancing land use planning and decision-making.
基金Funded by the National Natural Science Foundation of China(Nos.51108237 and 51178112)
文摘The micro-mechanical response of asphalt mixtures was studied using the discrete element method. The discrete element sample of stone mastic asphalt was generated first and the vehicle load was applied to the sample. A user-written program was coded with the FISH language in PFC3 D to extract the contact forces within the sample and the displacements of the particles. Then, the contact forces within the whole sample, in asphalt mastic, in coarse aggregates and between asphalt mastic and coarse aggregates were investigated. Finally, the movement of the particles in the sample was analyzed. The sample was divided into 15 areas and a figure was drawn to show how the balls move in each area according to the displacements of the balls in each area. The displacements of asphalt mastic balls and coarse aggregates were also analyzed. The experimental results explain how the asphalt mixture bears vehicle load and the potential reasons why the rutting forms from a micro-mechanical view.
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in part supported by the National Natural Science Foundation of China(Grant Nos.42288101,42405147 and 42475054)in part by the China National Postdoctoral Program for Innovative Talents(Grant No.BX20230071)。
文摘Conducting predictability studies is essential for tracing the source of forecast errors,which not only leads to the improvement of observation and forecasting systems,but also enhances the understanding of weather and climate phenomena.In the past few decades,dynamical numerical models have been the primary tools for predictability studies,achieving significant progress.Nowadays,with the advances in artificial intelligence(AI)techniques and accumulations of vast meteorological data,modeling weather and climate events using modern data-driven approaches is becoming trendy,where FourCastNet,Pangu-Weather,and GraphCast are successful pioneers.In this perspective article,we suggest AI models should not be limited to forecasting but be expanded to predictability studies,leveraging AI's advantages of high efficiency and self-contained optimization modules.To this end,we first remark that AI models should possess high simulation capability with fine spatiotemporal resolution for two kinds of predictability studies.AI models with high simulation capabilities comparable to numerical models can be considered to provide solutions to partial differential equations in a data-driven way.Then,we highlight several specific predictability issues with well-determined nonlinear optimization formulizations,which can be well-studied using AI models,holding significant scientific value.In addition,we advocate for the incorporation of AI models into the synergistic cycle of the cognition–observation–model paradigm.Comprehensive predictability studies have the potential to transform“big data”to“big and better data”and shift the focus from“AI for forecasts”to“AI for science”,ultimately advancing the development of the atmospheric and oceanic sciences.
基金Fund supported this work for Excellent Youth Scholars of China(Grant No.52222708)the National Natural Science Foundation of China(Grant No.51977007)+1 种基金Part of this work is supported by the research project“SPEED”(03XP0585)at RWTH Aachen Universityfunded by the German Federal Ministry of Education and Research(BMBF)。
文摘Developing sensorless techniques for estimating battery expansion is essential for effective mechanical state monitoring,improving the accuracy of digital twin simulation and abnormality detection.Therefore,this paper presents a data-driven approach to expansion estimation using electromechanical coupled models with machine learning.The proposed method integrates reduced-order impedance models with data-driven mechanical models,coupling the electrochemical and mechanical states through the state of charge(SOC)and mechanical pressure within a state estimation framework.The coupling relationship was established through experimental insights into pressure-related impedance parameters and the nonlinear mechanical behavior with SOC and pressure.The data-driven model was interpreted by introducing a novel swelling coefficient defined by component stiffnesses to capture the nonlinear mechanical behavior across various mechanical constraints.Sensitivity analysis of the impedance model shows that updating model parameters with pressure can reduce the mean absolute error of simulated voltage by 20 mV and SOC estimation error by 2%.The results demonstrate the model's estimation capabilities,achieving a root mean square error of less than 1 kPa when the maximum expansion force is from 30 kPa to 120 kPa,outperforming calibrated stiffness models and other machine learning techniques.The model's robustness and generalizability are further supported by its effective handling of SOC estimation and pressure measurement errors.This work highlights the importance of the proposed framework in enhancing state estimation and fault diagnosis for lithium-ion batteries.
基金National Natural Science Foundation of China(Nos.42301473,42271424,42171397)Chinese Postdoctoral Innovation Talents Support Program(No.BX20230299)+2 种基金China Postdoctoral Science Foundation(No.2023M742884)Natural Science Foundation of Sichuan Province(Nos.24NSFSC2264,2025ZNSFSC0322)Key Research and Development Project of Sichuan Province(No.24ZDYF0633).
文摘As a key node of modern transportation network,the informationization management of road tunnels is crucial to ensure the operation safety and traffic efficiency.However,the existing tunnel vehicle modeling methods generally have problems such as insufficient 3D scene description capability and low dynamic update efficiency,which are difficult to meet the demand of real-time accurate management.For this reason,this paper proposes a vehicle twin modeling method for road tunnels.This approach starts from the actual management needs,and supports multi-level dynamic modeling from vehicle type,size to color by constructing a vehicle model library that can be flexibly invoked;at the same time,semantic constraint rules with geometric layout,behavioral attributes,and spatial relationships are designed to ensure that the virtual model matches with the real model with a high degree of similarity;ultimately,the prototype system is constructed and the case region is selected for the case study,and the dynamic vehicle status in the tunnel is realized by integrating real-time monitoring data with semantic constraints for precise virtual-real mapping.Finally,the prototype system is constructed and case experiments are conducted in selected case areas,which are combined with real-time monitoring data to realize dynamic updating and three-dimensional visualization of vehicle states in tunnels.The experiments show that the proposed method can run smoothly with an average rendering efficiency of 17.70 ms while guaranteeing the modeling accuracy(composite similarity of 0.867),which significantly improves the real-time and intuitive tunnel management.The research results provide reliable technical support for intelligent operation and emergency response of road tunnels,and offer new ideas for digital twin modeling of complex scenes.
基金supported by National Natural Science Foundation of China(62376219 and 62006194)Foundational Research Project in Specialized Discipline(Grant No.G2024WD0146)Faculty Construction Project(Grant No.24GH0201148).
文摘Large language models(LLMs)have undergone significant expansion and have been increasingly integrated across various domains.Notably,in the realm of robot task planning,LLMs harness their advanced reasoning and language comprehension capabilities to formulate precise and efficient action plans based on natural language instructions.However,for embodied tasks,where robots interact with complex environments,textonly LLMs often face challenges due to a lack of compatibility with robotic visual perception.This study provides a comprehensive overview of the emerging integration of LLMs and multimodal LLMs into various robotic tasks.Additionally,we propose a framework that utilizes multimodal GPT-4V to enhance embodied task planning through the combination of natural language instructions and robot visual perceptions.Our results,based on diverse datasets,indicate that GPT-4V effectively enhances robot performance in embodied tasks.This extensive survey and evaluation of LLMs and multimodal LLMs across a variety of robotic tasks enriches the understanding of LLM-centric embodied intelligence and provides forward-looking insights towards bridging the gap in Human-Robot-Environment interaction.
基金supported by the Postgraduate Research&Practice Innovation Program of Jiangsu Province(Grant No.KYCX24_0714).
文摘To examine the similarities and differences in the evolution of cavity,wetting and dynamics of a highspeed,oblique water-entry projectile with different positive angles of attack,a comparative analysis has been conducted based on the numerical results of two mathematical models,the rigid-body model and fluid-structure interaction model.In addition,the applicable scope of the above two methods,and the structural response characteristics of the projectile have also been investigated.Our results demonstrate that:(1) The impact loads and angular motion of the projectile of the rigid-body method are more likely to exhibit periodic variations due to the periodic tail slap,its range of positive angles of attack is about α<2°.(2) When the projectile undergone significant wetting,a strong coupling effect is observed among wetting,structural deformation,and projectile motion.With the applied projectile shape,it is observed that,when the projectile bends,the final wetting position is that of Part B(cylinder of body).With the occu rrence of this phenomenon,the projectile ballistics beco me completely unstable.(3) The force exerted on the lower surface of the projectile induced by wetting is the primary reason of the destabilization of the projectile traj ectory and structu ral deformation failure.Bending deformation is most likely to appear at the junction of Part C(cone of body) and Part D(tail).The safe angles of attack of the projectile stability are found to be about α≤2°.
