The triangular zone cracks in 2101 duplex stainless steel produced by the vertical continuous caster have troubled company A for a long time. To simulate the temperature and thermal stress distributions in the solidif...The triangular zone cracks in 2101 duplex stainless steel produced by the vertical continuous caster have troubled company A for a long time. To simulate the temperature and thermal stress distributions in the solidification process of 2101 duplex stainless steel produced by the vertical continuous caster, a two-dimensional viscoelastic-plastic thermomechanically coupled finite element model was established by the secondary development of the commercial nonlinear finite element analysis software MSC Marc. The results show that the thermal stress on the surface reaches a maximum at the exit of the mould, and the highest thermal stresses at the centre of the wide face and the narrow face are 75 and 115 MPa, respectively. Meanwhile, the internal temperature of slab is still higher than the solidus temperature, resulting in no thermal stress. The slab shows different high-temperature strengths and suffers from different stresses at different positions; thus, the risk of cracking also varies. At a location of 6-8 m from the meniscus, the temperature of the triangular zone is 1270-1360℃ and the corresponding permissible high-temperature strength is about 10-30 MPa, while the thermal stress at this time is 60 MPa, which is higher than the high-temperature strength. As a result, triangular zone cracks form easily.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
The shear pin of the friction pendulum bearing(FPB)can be made of 40Cr steel.In conceptual design,the optimal cut-off point of the shear pin is predetermined,guiding the design of bridges isolated by FPBs to maximize ...The shear pin of the friction pendulum bearing(FPB)can be made of 40Cr steel.In conceptual design,the optimal cut-off point of the shear pin is predetermined,guiding the design of bridges isolated by FPBs to maximize their isolation performance.Current researches on the shear pins are mainly based on linear elastic models,neglecting their plasticity,damage,and fracture mechanical properties.To accurately predict its cutoff behavior,the elastic-plastic degradationmodel of 40Cr steel is indeed calibrated.For this purpose,the Ramberg-Osgoodmodel,the Bao-Wierzbicki damage initiation criterion,and the linear damage evolution criterion were selected to develop the elastic-plastic degradation model of 40Cr.Subsequently,parameter calibration of this model was performed through uniaxial tensile tests on two sets of six smooth,round bars with different diameters.Following this,finite element simulations were conducted for the pure shear test of grade 10.9 high-strength bolts made of 40Cr steel,aiming to verify the elasticplastic degradation model.The results showed that the failure modes and force-displacement curves simulated by the finite element method were in good agreement with the test results.Moreover,the error between the primary characteristic parameters(initial stiffness,peak load,fracture displacement,and absorbed energy)obtained by finite element calculation and the test values was within 15%.These results demonstrated that the calibration elastic-plastic degradation model of 40Cr steel can predict the cutoff of the shear pin.展开更多
The effects of temperature and Re content on the mechanical properties,dislocation morphology,and deformation mechanism of γ-γ′phases nickel-based single crystal superalloys are investigated by using the molecular ...The effects of temperature and Re content on the mechanical properties,dislocation morphology,and deformation mechanism of γ-γ′phases nickel-based single crystal superalloys are investigated by using the molecular dynamics method through the model of γ-γ′phases containing hole defect.The addition of Re makes the dislocation distribution tend towards the γ phase.The higher the Re content,the earlier theγphase yields,while the γ′phase yields later.Dislocation bends under the combined action of the applied force and the resistance of the Re atoms to form a bend point.The Re atoms are located at the bend points and strengthen the alloy by fixing the dislocation and preventing it from cutting the γ′phase.Dislocations nucleate first in the γ phase,causing theγphase to deform plastically before the γ′phase.As the strain increases,the dislocation length first remains unchanged,then increases rapidly,and finally fluctuates and changes.The dislocation lengths in the γ phase are larger than those in the γ′phase at different temperatures.The dislocation length shows a decreasing tendency with the increase of the temperature.Temperature can affect movement of the dislocation,and superalloys have different plastic deformation mechanisms at low,medium and high temperatures.展开更多
A new viscoelastic-plastic （VEP） constitutive model for sea ice dynamics was developed based on continuum mechanics. This model consists of four components： Kelvin-Vogit viscoelastic model, Mohr-Coulomb yielding cr...A new viscoelastic-plastic （VEP） constitutive model for sea ice dynamics was developed based on continuum mechanics. This model consists of four components： Kelvin-Vogit viscoelastic model, Mohr-Coulomb yielding criterion, associated normality flow rule for plastic rehololgy, and hydrostatic pressure. The numerical simulations for ice motion in an idealized rectangular basin were made using smoothed particle hydrodynamics （SPH） method, and compared with the analytical solution as well as those based on the modified viscous plastic（VP） model and static ice jam theory. These simulations show that the new VEP model can simulate ice dynamics accurately. The new constitutive model was further applied to simulate ice dynamics of the Bohai Sea and compared with the traditional VP, and modified VP models. The results of the VEP model are compared better with the satellite remote images, and the simulated ice conditions in the JZ20-2 oil platform area were more reasonable.展开更多
The model of rigid and elastic-plastic motion and strain in intraplate blocks is established in the paper. The unique of strain parameters and minimum root-mean-square error of velocity residual of blocks are tested i...The model of rigid and elastic-plastic motion and strain in intraplate blocks is established in the paper. The unique of strain parameters and minimum root-mean-square error of velocity residual of blocks are tested in the model. Based on the velocity fields in Chinese mainland and its peripheral areas, the strain parameters of 8 blocks are estimated and their strain status analyzed. The estimated strain status of each block is well consistent with those derived by the methods of geology and geophysics. The principal direction of collision force from India plate to Eurasia plate estimated from the azimuth of principal compressive strain of Himalaya block might be N7.1°E.展开更多
The adhesion of single asperity contacting with a rigid flat is investigated. The microcontact model of the deformable asperity is established utilizing fractal geometry, which makes the resuRed adhesion model to rela...The adhesion of single asperity contacting with a rigid flat is investigated. The microcontact model of the deformable asperity is established utilizing fractal geometry, which makes the resuRed adhesion model to relate with the surface characteristics that the asperity belongs to. The Dugdale approximation is utilized to consider the adhesive interaction within and outside the contact area. Then the model for solving the elastic-plastic adhesion of single asperity is presented by combing the Maugis-Dugdale（MD） model. To illustrate the necessity of considering the plastic deformation in microcontact, simulations of the relationship between the adhesive contact load and the interference of the asperity are performed. The result shows that the presented model is more suitable for the solution of the elastic-plastic microcontact of spherical asperity due to intermolecular adhesive interactions.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
Due to the different microstructures caused by the heat source effect,welding joints exhibit significant differences in mechanical properties compared to the base material.Precise characterization of the constitutive ...Due to the different microstructures caused by the heat source effect,welding joints exhibit significant differences in mechanical properties compared to the base material.Precise characterization of the constitutive characteristics of the welded joint requires a large number of repetitive experiments,which are costly,inefficient,and have limited accuracy improvements.This paper proposes an integrated experimental-simulation-based inverse calibration method,which establishes a calibration optimization problem based on the corresponding constitutive model and a finite element calculation model built by the distribution of hardness in the weldment.Using the global tensile force-displacement curve of the MIG-welded 6005A-T6 aluminum alloy specimen and the experimental data of local deformation with time change obtained from DIC(Digital Image Correlation),the parameters involved in the constitutive models are optimized accordingly.This method can directly obtain the constitutive characteristics of the weldment under conditions of limited experiments and insufficient data.Additionally,the adaptability of the constitutive model to the calibration method and the influence of optimization results are discussed and analyzed.The results indicate that the global force-displacement response of the non-saturated Ramberg-Osgood(R-O)model is in the best agreement with that of the experimental data,and the energy error is only 2.62%,followed by the MPL model,while the saturation-based Voce model shows the largest simulation error in terms of the presented object.Furthermore,the simulation results of R-O,Voce,and MPL models in the local area are far superior to traditional fitting methods.展开更多
On the basis of elastic-plastic damage model of cement consolidated soil,the authors took organic contents into reasonable damage variable evolution equation in order to seek relation between the organic contents and ...On the basis of elastic-plastic damage model of cement consolidated soil,the authors took organic contents into reasonable damage variable evolution equation in order to seek relation between the organic contents and parameters in the equation,and established the elastic-plastic damage model of cement consolidated soil considering organic contents.The results show that the parameters change correspondingly with difference of the organic contents.The higher the organic contents are,the less the valves of the parameters such as elastic modulus(E),material parameters(K,n) and damage evolution parameter(ε) become,but the larger strain damage threshold value(εd) of the sample is.Meanwhile,the calculation results obtained from established model are compared with the test data in the condition of common indoors test,which is testified with reliability.展开更多
Traditional methods focus on the ultimate bending moment of glulam beams and the fracture failure of materials with defects,which usually depends on empirical parameters.There is no systematic theoretical method to pr...Traditional methods focus on the ultimate bending moment of glulam beams and the fracture failure of materials with defects,which usually depends on empirical parameters.There is no systematic theoretical method to predict the stiffness and shear distribution of glulam beams in elastic-plastic stage,and consequently,the failure of such glulam beams cannot be predicted effectively.To address these issues,an analytical method considering material nonlinearity was proposed for glulam beams,and the calculating equations of deflection and shear stress distribution for different failure modes were established.The proposed method was verified by experiments and numerical models under the corresponding conditions.Results showed that the theoretical calculations were in good agreement with experimental and numerical results,indicating that the equations proposed in this paper were reliable and accurate for such glulam beams with wood material in the elastic-plastic stage ignoring the influence of mechanic properties in radial and tangential directions of wood.Furthermore,the experimental results reported by the previous studies indicated that the method was applicable and could be used as a theoretical reference for predicting the failure of glulam beams.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
文摘The triangular zone cracks in 2101 duplex stainless steel produced by the vertical continuous caster have troubled company A for a long time. To simulate the temperature and thermal stress distributions in the solidification process of 2101 duplex stainless steel produced by the vertical continuous caster, a two-dimensional viscoelastic-plastic thermomechanically coupled finite element model was established by the secondary development of the commercial nonlinear finite element analysis software MSC Marc. The results show that the thermal stress on the surface reaches a maximum at the exit of the mould, and the highest thermal stresses at the centre of the wide face and the narrow face are 75 and 115 MPa, respectively. Meanwhile, the internal temperature of slab is still higher than the solidus temperature, resulting in no thermal stress. The slab shows different high-temperature strengths and suffers from different stresses at different positions; thus, the risk of cracking also varies. At a location of 6-8 m from the meniscus, the temperature of the triangular zone is 1270-1360℃ and the corresponding permissible high-temperature strength is about 10-30 MPa, while the thermal stress at this time is 60 MPa, which is higher than the high-temperature strength. As a result, triangular zone cracks form easily.
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°.
基金The Research Start-up Fund for Talents Introduction of Huaiyin Institute of Technology(Grant No.Z301B23517).
文摘The shear pin of the friction pendulum bearing(FPB)can be made of 40Cr steel.In conceptual design,the optimal cut-off point of the shear pin is predetermined,guiding the design of bridges isolated by FPBs to maximize their isolation performance.Current researches on the shear pins are mainly based on linear elastic models,neglecting their plasticity,damage,and fracture mechanical properties.To accurately predict its cutoff behavior,the elastic-plastic degradationmodel of 40Cr steel is indeed calibrated.For this purpose,the Ramberg-Osgoodmodel,the Bao-Wierzbicki damage initiation criterion,and the linear damage evolution criterion were selected to develop the elastic-plastic degradation model of 40Cr.Subsequently,parameter calibration of this model was performed through uniaxial tensile tests on two sets of six smooth,round bars with different diameters.Following this,finite element simulations were conducted for the pure shear test of grade 10.9 high-strength bolts made of 40Cr steel,aiming to verify the elasticplastic degradation model.The results showed that the failure modes and force-displacement curves simulated by the finite element method were in good agreement with the test results.Moreover,the error between the primary characteristic parameters(initial stiffness,peak load,fracture displacement,and absorbed energy)obtained by finite element calculation and the test values was within 15%.These results demonstrated that the calibration elastic-plastic degradation model of 40Cr steel can predict the cutoff of the shear pin.
基金Project supported by the Xi’an Science and Technology Plan Project of Shaanxi Province of China(Grant No.23GXFW0086).
文摘The effects of temperature and Re content on the mechanical properties,dislocation morphology,and deformation mechanism of γ-γ′phases nickel-based single crystal superalloys are investigated by using the molecular dynamics method through the model of γ-γ′phases containing hole defect.The addition of Re makes the dislocation distribution tend towards the γ phase.The higher the Re content,the earlier theγphase yields,while the γ′phase yields later.Dislocation bends under the combined action of the applied force and the resistance of the Re atoms to form a bend point.The Re atoms are located at the bend points and strengthen the alloy by fixing the dislocation and preventing it from cutting the γ′phase.Dislocations nucleate first in the γ phase,causing theγphase to deform plastically before the γ′phase.As the strain increases,the dislocation length first remains unchanged,then increases rapidly,and finally fluctuates and changes.The dislocation lengths in the γ phase are larger than those in the γ′phase at different temperatures.The dislocation length shows a decreasing tendency with the increase of the temperature.Temperature can affect movement of the dislocation,and superalloys have different plastic deformation mechanisms at low,medium and high temperatures.
基金The authors would like to acknowledge the supports by the National Natural Science Foundation of China under contract No.40206004partly by the East-Asia and Pacific Program of US National Science Foundation under contract No.INT-9912246.
文摘A new viscoelastic-plastic （VEP） constitutive model for sea ice dynamics was developed based on continuum mechanics. This model consists of four components： Kelvin-Vogit viscoelastic model, Mohr-Coulomb yielding criterion, associated normality flow rule for plastic rehololgy, and hydrostatic pressure. The numerical simulations for ice motion in an idealized rectangular basin were made using smoothed particle hydrodynamics （SPH） method, and compared with the analytical solution as well as those based on the modified viscous plastic（VP） model and static ice jam theory. These simulations show that the new VEP model can simulate ice dynamics accurately. The new constitutive model was further applied to simulate ice dynamics of the Bohai Sea and compared with the traditional VP, and modified VP models. The results of the VEP model are compared better with the satellite remote images, and the simulated ice conditions in the JZ20-2 oil platform area were more reasonable.
基金State Key Basic Development and Program Project (G19980407).
文摘The model of rigid and elastic-plastic motion and strain in intraplate blocks is established in the paper. The unique of strain parameters and minimum root-mean-square error of velocity residual of blocks are tested in the model. Based on the velocity fields in Chinese mainland and its peripheral areas, the strain parameters of 8 blocks are estimated and their strain status analyzed. The estimated strain status of each block is well consistent with those derived by the methods of geology and geophysics. The principal direction of collision force from India plate to Eurasia plate estimated from the azimuth of principal compressive strain of Himalaya block might be N7.1°E.
基金China Post Doctor Science Foundation(No. 20070420748)Fujian Provincial Natural Science Foundation of China(E0610032).
文摘The adhesion of single asperity contacting with a rigid flat is investigated. The microcontact model of the deformable asperity is established utilizing fractal geometry, which makes the resuRed adhesion model to relate with the surface characteristics that the asperity belongs to. The Dugdale approximation is utilized to consider the adhesive interaction within and outside the contact area. Then the model for solving the elastic-plastic adhesion of single asperity is presented by combing the Maugis-Dugdale（MD） model. To illustrate the necessity of considering the plastic deformation in microcontact, simulations of the relationship between the adhesive contact load and the interference of the asperity are performed. The result shows that the presented model is more suitable for the solution of the elastic-plastic microcontact of spherical asperity due to intermolecular adhesive interactions.
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Supported by National Natural Science Foundation of China(Grant Nos.52202431,52172353)Talent Fund of Beijing Jiaotong University of China(Grant No.2024XKRC044).
文摘Due to the different microstructures caused by the heat source effect,welding joints exhibit significant differences in mechanical properties compared to the base material.Precise characterization of the constitutive characteristics of the welded joint requires a large number of repetitive experiments,which are costly,inefficient,and have limited accuracy improvements.This paper proposes an integrated experimental-simulation-based inverse calibration method,which establishes a calibration optimization problem based on the corresponding constitutive model and a finite element calculation model built by the distribution of hardness in the weldment.Using the global tensile force-displacement curve of the MIG-welded 6005A-T6 aluminum alloy specimen and the experimental data of local deformation with time change obtained from DIC(Digital Image Correlation),the parameters involved in the constitutive models are optimized accordingly.This method can directly obtain the constitutive characteristics of the weldment under conditions of limited experiments and insufficient data.Additionally,the adaptability of the constitutive model to the calibration method and the influence of optimization results are discussed and analyzed.The results indicate that the global force-displacement response of the non-saturated Ramberg-Osgood(R-O)model is in the best agreement with that of the experimental data,and the energy error is only 2.62%,followed by the MPL model,while the saturation-based Voce model shows the largest simulation error in terms of the presented object.Furthermore,the simulation results of R-O,Voce,and MPL models in the local area are far superior to traditional fitting methods.
基金Supported by Projects of National Natural Science Foundation of China(Nos.40372122, 40672180)Education Reform and Development Fund of Jilin University (No.498020200029)
文摘On the basis of elastic-plastic damage model of cement consolidated soil,the authors took organic contents into reasonable damage variable evolution equation in order to seek relation between the organic contents and parameters in the equation,and established the elastic-plastic damage model of cement consolidated soil considering organic contents.The results show that the parameters change correspondingly with difference of the organic contents.The higher the organic contents are,the less the valves of the parameters such as elastic modulus(E),material parameters(K,n) and damage evolution parameter(ε) become,but the larger strain damage threshold value(εd) of the sample is.Meanwhile,the calculation results obtained from established model are compared with the test data in the condition of common indoors test,which is testified with reliability.
基金support from High-Level Natural ScienceFoundation of Hainan Province of China (Grant No. 2019RC055)National Natural Science Foundation ofChina (Grant No. 51808176) and the Project Funded by the National First-Class Disciplines (PNFD).
文摘Traditional methods focus on the ultimate bending moment of glulam beams and the fracture failure of materials with defects,which usually depends on empirical parameters.There is no systematic theoretical method to predict the stiffness and shear distribution of glulam beams in elastic-plastic stage,and consequently,the failure of such glulam beams cannot be predicted effectively.To address these issues,an analytical method considering material nonlinearity was proposed for glulam beams,and the calculating equations of deflection and shear stress distribution for different failure modes were established.The proposed method was verified by experiments and numerical models under the corresponding conditions.Results showed that the theoretical calculations were in good agreement with experimental and numerical results,indicating that the equations proposed in this paper were reliable and accurate for such glulam beams with wood material in the elastic-plastic stage ignoring the influence of mechanic properties in radial and tangential directions of wood.Furthermore,the experimental results reported by the previous studies indicated that the method was applicable and could be used as a theoretical reference for predicting the failure of glulam beams.
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.
