The uplift resistance of the soil overlying shield tunnels significantly impacts their anti-floating stability.However,research on uplift resistance concerning special-shaped shield tunnels is limited.This study combi...The uplift resistance of the soil overlying shield tunnels significantly impacts their anti-floating stability.However,research on uplift resistance concerning special-shaped shield tunnels is limited.This study combines numerical simulation with machine learning techniques to explore this issue.It presents a summary of special-shaped tunnel geometries and introduces a shape coefficient.Through the finite element software,Plaxis3D,the study simulates six key parameters—shape coefficient,burial depth ratio,tunnel’s longest horizontal length,internal friction angle,cohesion,and soil submerged bulk density—that impact uplift resistance across different conditions.Employing XGBoost and ANN methods,the feature importance of each parameter was analyzed based on the numerical simulation results.The findings demonstrate that a tunnel shape more closely resembling a circle leads to reduced uplift resistance in the overlying soil,whereas other parameters exhibit the contrary effects.Furthermore,the study reveals a diminishing trend in the feature importance of buried depth ratio,internal friction angle,tunnel longest horizontal length,cohesion,soil submerged bulk density,and shape coefficient in influencing uplift resistance.展开更多
Software systems are vulnerable to security breaches as they expand in complexity and functionality.The confidentiality,integrity,and availability of data are gravely threatened by flaws in a system’s design,implemen...Software systems are vulnerable to security breaches as they expand in complexity and functionality.The confidentiality,integrity,and availability of data are gravely threatened by flaws in a system’s design,implementation,or configuration.To guarantee the durability&robustness of the software,vulnerability identification and fixation have become crucial areas of focus for developers,cybersecurity experts and industries.This paper presents a thorough multi-phase mathematical model for efficient patch management and vulnerability detection.To uniquely model these processes,the model incorporated the notion of the learning phenomenon in describing vulnerability fixation using a logistic learning function.Furthermore,the authors have used numerical methods to approximate the solution of the proposed framework where an analytical solution is difficult to attain.The suggested systematic architecture has been demonstrated through statistical analysis using patch datasets,which offers a solid basis for the research conclusions.According to computational research,learning dynamics improves security response and results in more effective vulnerability management.The suggested model offers a systematic approach to proactive vulnerability mitigation and has important uses in risk assessment,software maintenance,and cybersecurity.This study helps create more robust software systems by increasing patch management effectiveness,which benefits developers,cybersecurity experts,and sectors looking to reduce security threats in a growing digital world.展开更多
In order to solve the problem of the variable coefficient ordinary differen-tial equation on the bounded domain,the Lagrange interpolation method is used to approximate the exact solution of the equation,and the error...In order to solve the problem of the variable coefficient ordinary differen-tial equation on the bounded domain,the Lagrange interpolation method is used to approximate the exact solution of the equation,and the error between the numerical solution and the exact solution is obtained,and then compared with the error formed by the difference method,it is concluded that the Lagrange interpolation method is more effective in solving the variable coefficient ordinary differential equation.展开更多
This article reviews the application and progress of deep learning in efficient numerical computing methods.Deep learning,as an important branch of machine learning,provides new ideas for numerical computation by cons...This article reviews the application and progress of deep learning in efficient numerical computing methods.Deep learning,as an important branch of machine learning,provides new ideas for numerical computation by constructing multi-layer neural networks to simulate the learning process of the human brain.The article explores the application of deep learning in solving partial differential equations,optimizing problems,and data-driven modeling,and analyzes its advantages in computational efficiency,accuracy,and adaptability.At the same time,this article also points out the challenges faced by deep learning numerical computation methods in terms of computational efficiency,interpretability,and generalization ability,and proposes strategies and future development directions for integrating with traditional numerical methods.展开更多
This review paper provides a comprehensive introduction to various numerical methods for the phase-field model used to simulate the phase separation dynamics of diblock copolymer melts.Diblock copolymer systems form c...This review paper provides a comprehensive introduction to various numerical methods for the phase-field model used to simulate the phase separation dynamics of diblock copolymer melts.Diblock copolymer systems form complex structures at the nanometer scale and play a significant role in various applications.The phase-field model,in particular,is essential for describing the formation and evolution of these structures and is widely used as a tool to effectively predict the movement of phase boundaries and the distribution of phases over time.In this paper,we discuss the principles and implementations of various numerical methodologies for this model and analyze the strengths,limitations,stability,accuracy,and computational efficiency of each method.Traditional approaches such as Fourier spectral methods,finite difference methods and alternating direction explicit methods are reviewed,as well as recent advancements such as the invariant energy quadratization method and the scalar auxiliary variable scheme are also presented.In addition,we introduce examples of the phase-field model,which are fingerprint image restoration and 3D printing.These examples demonstrate the extensive applicability of the reviewed methods and models.展开更多
The feasibility of using a problem-dependent method to solve systems of second order ODEs is corroborated by an eigen-based theory and a methodology to develop such a numerical method is constructed.The key steps of t...The feasibility of using a problem-dependent method to solve systems of second order ODEs is corroborated by an eigen-based theory and a methodology to develop such a numerical method is constructed.The key steps of this methodology are to decouple a system of ODEs of second order into a set of uncoupled ODEs of second order;next,an eigen-dependent method is proposed to approximate the solution of each uncoupled ODE of second order.It is vital to transform all eigen-dependent methods to a problem-dependent method to bypass an Eigen analysis.The development of an eigen-dependent method plays a key role in this methodology so that slow eigenmodes can be accurately integrated while there is no instability or excessive amplitude growth in fast eigenmodes.This can explain why a problem-dependent method can simultaneously combine the explicitness of each step and A-stability.Consequently,huge computational efforts can be saved for solving nonlinear stiff problems.A new family of problem-dependent methods is developed in this work so that the feasibility of the proposed methodology can be affirmed.It has almost the same performance as that of the HHT-αmethod.However,it can save more than 99.5%of CPU demand in approximating a solution for a system of 1000 nonlinear second order ODEs.展开更多
Coal underground gasification(UCG)transforms the physical extraction of coal into the chemical extraction of gas,which is effective for exploiting deep coal deposits.Numerical simulation technology for UCG is a crucia...Coal underground gasification(UCG)transforms the physical extraction of coal into the chemical extraction of gas,which is effective for exploiting deep coal deposits.Numerical simulation technology for UCG is a crucial tool for studying the complex processes involved in coal gasification.This study was conducted to determine the direction in which UCG numerical simulation is developing,specifically by reviewing the research progress and achievements made in this area and identifying the existing problems and future research directions.The findings indicate the following:(1)Research has focused on the reaction issues of coal underground gasification,considering mass and heat transfer effects and gasification cavity expansion.Chemical equilibrium,gasification block,packed bed,and gasification channel models have been developed,which have certain advantages in solving gasification reaction problems influenced by cavity structure and reasonable simplifications capable of describing local issues.(2)The dynamic description of gasification cavity structures is a challenging problem that UCG numerical simulation needs to address.The cavity expansion mechanism includes thermochemical consumption,coal spalling,roof collapse,and debris accumulation.Thermochemical consumption causes the mechanical properties of coal and rock to change,leading to spalling under stress.(3)Process models emphasize dynamic simulations of the gasification process,including cavity evolution and gasification products.The reactor combination model,continuous medium equivalent model,and multimodule integration model are primarily used.(4)Future UCG numerical simulation technology development will prioritize modularity,systematization,and intelligence.There is an urgent need to facilitate the chemical reaction kinetics of large coal blocks,the coupling of discontinuous media,and the integration of multifunctional systems,including that of numerical simulation technology with artificial intelligence.With continuous improvements,numerical simulation technology will play a greater technical supporting role in UCG industrialization.展开更多
As binary geological media,soil-rock mixtures(SRMs)exhibit a distinct gradational composition,leading to their unique mechanical behaviors.To appraise the stability of SRM slopes,it is essential to determine equivalen...As binary geological media,soil-rock mixtures(SRMs)exhibit a distinct gradational composition,leading to their unique mechanical behaviors.To appraise the stability of SRM slopes,it is essential to determine equivalent parameters of SRMs,which are typically obtained through experimental and numerical methods.In contrasted to other numerical methods,the numerical manifold method(NMM)is more effective in addressing SRM problems.This is because the high-precision regular mathematical meshes in NMM can be used without aligning with the soil-rock interfaces and boundaries of SRMs.In the current research,the equivalent strength parameters of SRMs,i.e.the equivalent cohesion ce and internal friction angleϕ_(e),are determined using NMM.Initially,an NMM triaxial numerical model is established and validated based on triaxial experiments.Subsequently,the soil and rock parameters are derived through parameter inversion.Moreover,the impacts of rock content,size,shape and rock blocks'major-axis orientation on ce andϕ_(e) of SRMs are thoroughly examined using the NMM triaxial numerical model.Additionally,a fitting function is proposed to linkϕ_(e) to the rock content and size of SRMs.When other influencing factors are fixed,the above fitting model leads to the following conclusions:(1)the predictedϕ_(e) of SRMs increase with the increase of rock content;and(2)SRM samples with smaller rocks display a higher predictedϕ_(e).展开更多
Existing numerical methods for complex composites, such as multiscale simulation and neural network algorithms, face significant limitations. Multiscale techniques are often prohibitively expensive for large models, w...Existing numerical methods for complex composites, such as multiscale simulation and neural network algorithms, face significant limitations. Multiscale techniques are often prohibitively expensive for large models, while neural networks struggle to represent underlying microscopic material properties. To overcome these challenges, a meso-micro scale numerical method using a virtual node approach is developed in this study. A Wbraid/Al/Epoxy functional structural material is fabricated, and a representative periodic unit cell is identified based on its architecture. The complex structure is then discretized into nodes, and mechanical interactions are governed by pre-defined computation rules. This virtual node method is systematically compared against both multiscale simulation and a neural network algorithm, with validation provided through mechanical experiments. The results demonstrate that the nodal operation strategy significantly reduces computational resource requirements. By quantifying microscopic bonding with coefficients, explicit interface treatment is avoided, granting the method strong adaptability to lattice materials. The method can simulate extremely complex structures using parameters from simple tests and is suited for large systems. Compared to three-point bending experiments, errors for multiscale, virtual node, and neural network methods were 12.4%, 6.9%, and 34.5%, respectively. Under dynamic compression, the errors were 2.7%, 9.3%, and 15.43%. The virtual node method demonstrated superior accuracy under static conditions, enabling efficient prediction and auxiliary development of complex structural materials.展开更多
Marine thin plates are susceptible to welding deformation owing to their low structural stiffness.Therefore,the efficient and accurate prediction of welding deformation is essential for improving welding quality.The t...Marine thin plates are susceptible to welding deformation owing to their low structural stiffness.Therefore,the efficient and accurate prediction of welding deformation is essential for improving welding quality.The traditional thermal elastic-plastic finite element method(TEP-FEM)can accurately predict welding deformation.However,its efficiency is low because of the complex nonlinear transient computation,making it difficult to meet the needs of rapid engineering evaluation.To address this challenge,this study proposes an efficient prediction method for welding deformation in marine thin plate butt welds.This method is based on the coupled temperature gradient-thermal strain method(TG-TSM)that integrates inherent strain theory with a shell element finite element model.The proposed method first extracts the distribution pattern and characteristic value of welding-induced inherent strain through TEP-FEM analysis.This strain is then converted into the equivalent thermal load applied to the shell element model for rapid computation.The proposed method-particularly,the gradual temperature gradient-thermal strain method(GTG-TSM)-achieved improved computational efficiency and consistent precision.Furthermore,the proposed method required much less computation time than the traditional TEP-FEM.Thus,this study lays the foundation for future prediction of welding deformation in more complex marine thin plates.展开更多
Functionally graded material(FGM)plates are widely used in various engineering structures owing to their tailor-made mechanical properties,whereas cracked homogeneous plates constitute a canonical setting in fracture ...Functionally graded material(FGM)plates are widely used in various engineering structures owing to their tailor-made mechanical properties,whereas cracked homogeneous plates constitute a canonical setting in fracture mechanics analysis.These two classes of problems respectively embody material non-uniformity and geometric discontinuity,thereby imposing more stringent requirements on numerical methods in terms of high-order field continuity and accurate defect representation.Based on the classical Kirchhoff-Love plate theory,a numerical manifold method(MLS-NMM)incorporating moving least squares(MLS)interpolation is developed for bending analysis of FGM plates and fracture simulation of homogeneous plates with defects.The method constructs an H^(2)-regular approximation with high-order continuous weighting functions and,combined with the separation of mathematical and physical covers,establishes a unified framework that accurately handles material gradients and cracks without mesh reconstruction.For the crack tip,a singular physical cover incorporating the Williams asymptotic field is introduced to achieve local enrichment,enabling the natural capture of displacement discontinuity and stress singularity.Stress intensity factors are extracted using the interaction integral method,and the dimensionless J-integral shows a maximum relative error below 1.2%compared with the reference solution.Numerical results indicate that MLS-NMM exhibits excellent convergence performance:using 676 mathematical nodes,the nondimensional central deflection of both FGM and homogeneous plates agrees with reference solutions with a maximum relative error below 0.81%,and no shear locking occurs.A systematic analysis reveals that for a simply supported on all four edges(SSSS)FGM square plate with a/h=10,the nondimensional central deflection increases by 212%as the gradient index nrises from 0 to 5.For a homogeneous plate containing a central crack with c/a=0.6,the nondimensional central deflection increases by approximately 46%compared with the intact plate.Under weak boundary constraints(e.g.,SFSF),the deformation is markedly amplified,with the deflection reaching more than three times that under strong constraints(SCSC).The proposed method provides an efficient,reconstruction-free numerical tool for high-accuracy bending and fracture analyses of FGM and cracked thin-plate structures.展开更多
Wire arc additive manufacturing(WAAM)has emerged as a promising approach for fabricating large-scale components.However,conventional WAAM still faces challenges in optimizing microstructural evolution,minimizing addit...Wire arc additive manufacturing(WAAM)has emerged as a promising approach for fabricating large-scale components.However,conventional WAAM still faces challenges in optimizing microstructural evolution,minimizing additive-induced defects,and alleviating residual stress and deformation,all of which are critical for enhancing the mechanical performance of the manufactured parts.Integrating interlayer friction stir processing(FSP)into WAAM significantly enhances the quality of deposited materials.However,numerical simulation research focusing on elucidating the associated thermomechanical coupling mechanisms remains insufficient.A comprehensive numerical model was developed to simulate the thermomechanical coupling behavior in friction stir-assisted WAAM.The influence of post-deposition FSP on the coupled thermomechanical response of the WAAM process was analyzed quantitatively.Moreover,the residual stress distribution and deformation behavior under both single-layer and multilayer deposition conditions were investigated.Thermal analysis of different deposition layers in WAAM and friction stir-assisted WAAM was conducted.Results show that subsequent layer deposition induces partial remelting of the previously solidified layer,whereas FSP does not cause such remelting.Furthermore,thermal stress and deformation analysis confirm that interlayer FSP effectively mitigates residual stresses and distortion in WAAM components,thereby improving their structural integrity and mechanical properties.展开更多
Granular flow,such as hopper discharge and debris flows,involves complex multi-scale,multi-phase,and multi-physics coupling,posing significant challenges for numerical simulation.Over the past two decades,methods like...Granular flow,such as hopper discharge and debris flows,involves complex multi-scale,multi-phase,and multi-physics coupling,posing significant challenges for numerical simulation.Over the past two decades,methods like the Discrete Element Method(DEM),Smoothed Particle Hydrodynamics(SPH),and Depth-Averaging Method(DAM),have been developed to address these problems.However,their applicability across different scales remains unclear due to differences in physical assumptions and numerical algorithms.Therefore,a comprehensive evaluation is critically needed.This study selects three typical methods(DEM,SPH,and DAM)to examine their convergence behavior,boundary condition implementation,and limitations in physical and numerical modeling.We numerically studied three extreme deformation flow cases with the three chosen methods.These cases include granular column collapse at the particle scale,flow-structure interaction at the laboratory scale,and reconstruction of the 2015 Shenzhen Guangming landslide at the field scale.By comparing the granular flow dynamics,deposition morphology,and structure interactions,and also the simulation accuracy and computational efficiency,we show the applicability of the three models across different scales.Further,we provide practical guidance for model selection in large-deformation flow problems in a granular system of different scales.展开更多
Burden is one of the main parameters in blast design.However,field tests,either single-or multi-hole blasts,used to determine an appropriate burden,are difficult to capture crack propagation,evolution of breakage angl...Burden is one of the main parameters in blast design.However,field tests,either single-or multi-hole blasts,used to determine an appropriate burden,are difficult to capture crack propagation,evolution of breakage angle,and the mechanism governing these processes in the rock.In this study,a single-hole bench blasting model is developed using LS-DYNA software to comprehensively investigate the relationship between burden and rock breakage.The simulation results show that the breakage angle decreases with the increase in burden,and the blasted volume reaches a peak value with a burden of 4 m.Meanwhile,backbreak distance increases with increasing burden.The optimum burden in this simulation is found to be 4.0 m,as the ratio of burden to blasthole diameter is equal to 20.62 and the ratio of burden to bench height is 0.44,based on a comprehensive analysis of the blasted volume,average damage,and total damage.Under the optimum burden condition,tensile stress wave regions are simultaneously generated at the free surfaces of both the bench top and bench slope,allowing more effective utilization of the two free surfaces and resulting in a more uniform damage distribution within the burden region.展开更多
In this investigation,a hybrid approach integrating the IDDES turbulence model and FW-H is employed to forecast the hydroacoustic of the rim driven thruster(RDT)under non-cavitation and uniform flow conditions at vary...In this investigation,a hybrid approach integrating the IDDES turbulence model and FW-H is employed to forecast the hydroacoustic of the rim driven thruster(RDT)under non-cavitation and uniform flow conditions at varying loading conditions(J=0.3 and J=0.6).It is revealed that the quadrupole term contribution in the P-FWH method significantly affects the monopole term in the low-frequency region,while it mainly affects the dipole term in the high-frequency region.Specifically,the overall sound pressure levels(SPL)of the RDT using the P-FWH method are 2.27 dB,10.03 dB,and 16.73 dB at the receiving points from R1 to R3 under the heavy-loaded condition,while they increase by 0.67 dB at R1,and decrease by 14.93 dB at R2,and 22.20 dB at R3,for the light-loaded condition.The study also utilizes the pressure-time derivatives to visualize the numerical noise and to pinpoint the dynamics of the vortex cores,and the optimization of the grid design can significantly reduce the numerical noise.The computational accuracy of the P-FWH method can meet the noise requirements for the preliminary design of rim driven thrusters.展开更多
Ice crystal icing is an important cause of accidents in aircraft engines.Ice formation in aircraft engines can cause internal blades to freeze,affecting the quality of the air flow field and blocking the flow path.On ...Ice crystal icing is an important cause of accidents in aircraft engines.Ice formation in aircraft engines can cause internal blades to freeze,affecting the quality of the air flow field and blocking the flow path.On the other hand,the entry of ice crystal particles into the combustion chamber can cause a decrease in temperature or even flameout,leading to engine surge or shutdown.Therefore,it is necessary to conduct multiphase flow tests on ice crystals for aircraft components such as aircraft engines.Conducting ice crystal multiphase flow tests on aircraft is an effective research method,but it requires the construction of an ice crystal multiphase flow test platform that meets relevant technical requirements.The paper focuses on the relevant experimental requirements and combines wind tunnel test structures to conduct multiphase flow numerical simulations on various forms of jet pipelines,obtaining particle motion distribution results.After comparison,the optimal form of jet structure is obtained,providing the best selection scheme for the design of relevant wind tunnel structures.展开更多
Machine learning-assisted methods for rapid and accurate prediction of temperature field,mushy zone,and grain size were proposed for the heating−cooling combined mold(HCCM)horizontal continuous casting of C70250 alloy...Machine learning-assisted methods for rapid and accurate prediction of temperature field,mushy zone,and grain size were proposed for the heating−cooling combined mold(HCCM)horizontal continuous casting of C70250 alloy plates.First,finite element simulations of casting processes were carried out with various parameters to build a dataset.Subsequently,different machine learning algorithms were employed to achieve high precision in predicting temperature fields,mushy zone locations,mushy zone inclination angle,and billet grain size.Finally,the process parameters were quickly optimized using a strategy consisting of random generation,prediction,and screening,allowing the mushy zone to be controlled to the desired target.The optimized parameters are 1234℃for heating mold temperature,47 mm/min for casting speed,and 10 L/min for cooling water flow rate.The optimized mushy zone is located in the middle of the second heat insulation section and has an inclination angle of roughly 7°.展开更多
Adhesively bonded joints are widely used in modern lightweight structures due to their high strengthto-weight ratio and design flexibility.However,the reliable non-destructive evaluation of bond integrity remains a si...Adhesively bonded joints are widely used in modern lightweight structures due to their high strengthto-weight ratio and design flexibility.However,the reliable non-destructive evaluation of bond integrity remains a significant challenge.This study presents a numerical investigation of adhesively bonded joints with different adhesive properties using ultrasonic guided waves.The main focus of the investigation is to evaluate the feasibility of using guided waves to assess bond integrity,particularly for detecting challenging weak bonds.For this purpose,a theoretical analysis of dispersion curves was conducted,revealing that the S0 Lamb wave mode is significantly sensitive to variations in adhesive properties in the 300-700 kHz frequency range.Finite element modelling was used to analyse the propagation of guided waves in two scenarios:an adhesively bonded aluminum structure and a more complex configuration-adhesively bonded lap joints.The Short-Time Fourier Transform(STFT)was used to process the obtained results and determine the group velocities of guided waves.By analysing the group velocity characteristics,their dependence on the adhesive properties was identified.In the first scenario,a clear separation of S0 modes from A0 modes was observed in the STFT analysis,with a decrease in group velocity as adhesive stiffness increased.For the more complex lap joint scenario,the separation between A0 and S0 modes was less distinct.However,the analysis of the average group velocity shows a dependence of average group velocity on adhesive properties.This is similar to the first scenario.There is a decrease in average group velocity as adhesive stiffness increases.The results obtained demonstrate that guided wavebased methods have a high potential for non-destructive evaluation of adhesively bonded structures,including the detection of weak bonds.展开更多
Regenerative catalytic oxidizers(RCO)are widely used to remove volatile organic compounds(VOCs)due to their energy-saving and stability.In this study,a multi-component catalytic reaction model was constructed to numer...Regenerative catalytic oxidizers(RCO)are widely used to remove volatile organic compounds(VOCs)due to their energy-saving and stability.In this study,a multi-component catalytic reaction model was constructed to numerically investigate the reaction process of hydrocarbon-containing VOCs in RCO using computational fluid dynamics(CFD)simulation.To obtain the conversion characteristics of multi-component hydrocarbons,the effects of intake load,equivalence ratio,and the composition of multi-component hydrocarbons on the flow,heat transfer,and conversion rate of the reactor were analyzed.A feasibility study plan targeting the hard-to-convert components was also proposed.The results indicated that as the load increases,the conversion rates of the various components decrease,while the reaction rates increase.Moreover,increasing the flow velocity intensifies turbulence and enhances the collision frequency between the gas and the wall surfaces.This,in turn,amplifies the resistance effect of the porous medium.As the equivalence ratio of VOCs to oxygen increases,the oxygen-deficient condition leads to a decrease in the molecular weight of the hydrocarbons involved in the reaction.The reaction temperature also shows a downward trend.A comparative analysis of the catalytic combustion characteristics of multi-component VOCs and single-component gases reveals that adding ethane and propane can facilitate methane oxidation.展开更多
Prenatal exposure to bisphenols and metals has raised significant concerns regarding their potential impact on fetal development,particularly the risk of fetal chromosome numerical abnormalities(CNA).In this case-cont...Prenatal exposure to bisphenols and metals has raised significant concerns regarding their potential impact on fetal development,particularly the risk of fetal chromosome numerical abnormalities(CNA).In this case-control study,we analyzed bisphenol and metal concentrations in amniotic fluid of high-risk pregnant women undergoing amniocentesis.Concentrations of bisphenols and metals were measured using ultra-performance liquid chromatography-tandem mass spectrometry and inductively coupled plasma-mass spectrometry,respectively.Logistic regression and quantile-based g-computation were applied to evaluate individual and combined effects,while dose-response relationships were assessed using restricted cubic splines.Our findings indicated that bisphenol S(BPS),bisphenol Z(BPZ),bisphenol AF(BPAF),antimony(Sb),and vanadium(V)were significantly associated with an increased risk of CNA when analyzed individually,whereas manganese,iron,copper(Cu),nickel(Ni),and zinc(Zn)were significantly and inversely associated with CNA risk.Combined exposure to bisphenol and metal mixtures was associated with an increased risk of CNA in multi-pollutant models.Cu and Ni exhibited a positive additive interaction.Furthermore,BPS,BPZ,and BPAF were individually associated with an increased risk of Down syndrome,while Zn was associated with a decreased risk of Down syndrome.BPS,Sb,V,and Zn were individually associated with an increased risk of Klinefelter syndrome.These findings underscore the potential role of prenatal bisphenol and metal exposure in the pathogenesis of fetal CNA,highlighting both additive and synergistic effects.展开更多
基金Guangzhou Metro Scientific Research Project(No.JT204-100111-23001)Chongqing Municipal Special Project for Technological Innovation and Application Development(No.CSTB2022TIAD-KPX0101)Science and Technology Research and Development Program of China State Railway Group Co.,Ltd.(No.N2023G045)。
文摘The uplift resistance of the soil overlying shield tunnels significantly impacts their anti-floating stability.However,research on uplift resistance concerning special-shaped shield tunnels is limited.This study combines numerical simulation with machine learning techniques to explore this issue.It presents a summary of special-shaped tunnel geometries and introduces a shape coefficient.Through the finite element software,Plaxis3D,the study simulates six key parameters—shape coefficient,burial depth ratio,tunnel’s longest horizontal length,internal friction angle,cohesion,and soil submerged bulk density—that impact uplift resistance across different conditions.Employing XGBoost and ANN methods,the feature importance of each parameter was analyzed based on the numerical simulation results.The findings demonstrate that a tunnel shape more closely resembling a circle leads to reduced uplift resistance in the overlying soil,whereas other parameters exhibit the contrary effects.Furthermore,the study reveals a diminishing trend in the feature importance of buried depth ratio,internal friction angle,tunnel longest horizontal length,cohesion,soil submerged bulk density,and shape coefficient in influencing uplift resistance.
基金supported by grants received by the first author and third author from the Institute of Eminence,Delhi University,Delhi,India,as part of the Faculty Research Program via Ref.No./IoE/2024-25/12/FRP.
文摘Software systems are vulnerable to security breaches as they expand in complexity and functionality.The confidentiality,integrity,and availability of data are gravely threatened by flaws in a system’s design,implementation,or configuration.To guarantee the durability&robustness of the software,vulnerability identification and fixation have become crucial areas of focus for developers,cybersecurity experts and industries.This paper presents a thorough multi-phase mathematical model for efficient patch management and vulnerability detection.To uniquely model these processes,the model incorporated the notion of the learning phenomenon in describing vulnerability fixation using a logistic learning function.Furthermore,the authors have used numerical methods to approximate the solution of the proposed framework where an analytical solution is difficult to attain.The suggested systematic architecture has been demonstrated through statistical analysis using patch datasets,which offers a solid basis for the research conclusions.According to computational research,learning dynamics improves security response and results in more effective vulnerability management.The suggested model offers a systematic approach to proactive vulnerability mitigation and has important uses in risk assessment,software maintenance,and cybersecurity.This study helps create more robust software systems by increasing patch management effectiveness,which benefits developers,cybersecurity experts,and sectors looking to reduce security threats in a growing digital world.
文摘In order to solve the problem of the variable coefficient ordinary differen-tial equation on the bounded domain,the Lagrange interpolation method is used to approximate the exact solution of the equation,and the error between the numerical solution and the exact solution is obtained,and then compared with the error formed by the difference method,it is concluded that the Lagrange interpolation method is more effective in solving the variable coefficient ordinary differential equation.
文摘This article reviews the application and progress of deep learning in efficient numerical computing methods.Deep learning,as an important branch of machine learning,provides new ideas for numerical computation by constructing multi-layer neural networks to simulate the learning process of the human brain.The article explores the application of deep learning in solving partial differential equations,optimizing problems,and data-driven modeling,and analyzes its advantages in computational efficiency,accuracy,and adaptability.At the same time,this article also points out the challenges faced by deep learning numerical computation methods in terms of computational efficiency,interpretability,and generalization ability,and proposes strategies and future development directions for integrating with traditional numerical methods.
文摘This review paper provides a comprehensive introduction to various numerical methods for the phase-field model used to simulate the phase separation dynamics of diblock copolymer melts.Diblock copolymer systems form complex structures at the nanometer scale and play a significant role in various applications.The phase-field model,in particular,is essential for describing the formation and evolution of these structures and is widely used as a tool to effectively predict the movement of phase boundaries and the distribution of phases over time.In this paper,we discuss the principles and implementations of various numerical methodologies for this model and analyze the strengths,limitations,stability,accuracy,and computational efficiency of each method.Traditional approaches such as Fourier spectral methods,finite difference methods and alternating direction explicit methods are reviewed,as well as recent advancements such as the invariant energy quadratization method and the scalar auxiliary variable scheme are also presented.In addition,we introduce examples of the phase-field model,which are fingerprint image restoration and 3D printing.These examples demonstrate the extensive applicability of the reviewed methods and models.
文摘The feasibility of using a problem-dependent method to solve systems of second order ODEs is corroborated by an eigen-based theory and a methodology to develop such a numerical method is constructed.The key steps of this methodology are to decouple a system of ODEs of second order into a set of uncoupled ODEs of second order;next,an eigen-dependent method is proposed to approximate the solution of each uncoupled ODE of second order.It is vital to transform all eigen-dependent methods to a problem-dependent method to bypass an Eigen analysis.The development of an eigen-dependent method plays a key role in this methodology so that slow eigenmodes can be accurately integrated while there is no instability or excessive amplitude growth in fast eigenmodes.This can explain why a problem-dependent method can simultaneously combine the explicitness of each step and A-stability.Consequently,huge computational efforts can be saved for solving nonlinear stiff problems.A new family of problem-dependent methods is developed in this work so that the feasibility of the proposed methodology can be affirmed.It has almost the same performance as that of the HHT-αmethod.However,it can save more than 99.5%of CPU demand in approximating a solution for a system of 1000 nonlinear second order ODEs.
基金supported by Natural Science Research Project of Yangzhou University Guangling College,China(Grant No.ZKZD25002)Youth Science and Technology Special Project of PetroChina(Grant No.2024DQ03221)Basic Research Project of Institute Research Institute of Exploration and Development,PetroChina(Grant No.101001cq0b52394).
文摘Coal underground gasification(UCG)transforms the physical extraction of coal into the chemical extraction of gas,which is effective for exploiting deep coal deposits.Numerical simulation technology for UCG is a crucial tool for studying the complex processes involved in coal gasification.This study was conducted to determine the direction in which UCG numerical simulation is developing,specifically by reviewing the research progress and achievements made in this area and identifying the existing problems and future research directions.The findings indicate the following:(1)Research has focused on the reaction issues of coal underground gasification,considering mass and heat transfer effects and gasification cavity expansion.Chemical equilibrium,gasification block,packed bed,and gasification channel models have been developed,which have certain advantages in solving gasification reaction problems influenced by cavity structure and reasonable simplifications capable of describing local issues.(2)The dynamic description of gasification cavity structures is a challenging problem that UCG numerical simulation needs to address.The cavity expansion mechanism includes thermochemical consumption,coal spalling,roof collapse,and debris accumulation.Thermochemical consumption causes the mechanical properties of coal and rock to change,leading to spalling under stress.(3)Process models emphasize dynamic simulations of the gasification process,including cavity evolution and gasification products.The reactor combination model,continuous medium equivalent model,and multimodule integration model are primarily used.(4)Future UCG numerical simulation technology development will prioritize modularity,systematization,and intelligence.There is an urgent need to facilitate the chemical reaction kinetics of large coal blocks,the coupling of discontinuous media,and the integration of multifunctional systems,including that of numerical simulation technology with artificial intelligence.With continuous improvements,numerical simulation technology will play a greater technical supporting role in UCG industrialization.
基金supported by the National Natural Science Foundation of China(Grant Nos.12272393 and 52130905).
文摘As binary geological media,soil-rock mixtures(SRMs)exhibit a distinct gradational composition,leading to their unique mechanical behaviors.To appraise the stability of SRM slopes,it is essential to determine equivalent parameters of SRMs,which are typically obtained through experimental and numerical methods.In contrasted to other numerical methods,the numerical manifold method(NMM)is more effective in addressing SRM problems.This is because the high-precision regular mathematical meshes in NMM can be used without aligning with the soil-rock interfaces and boundaries of SRMs.In the current research,the equivalent strength parameters of SRMs,i.e.the equivalent cohesion ce and internal friction angleϕ_(e),are determined using NMM.Initially,an NMM triaxial numerical model is established and validated based on triaxial experiments.Subsequently,the soil and rock parameters are derived through parameter inversion.Moreover,the impacts of rock content,size,shape and rock blocks'major-axis orientation on ce andϕ_(e) of SRMs are thoroughly examined using the NMM triaxial numerical model.Additionally,a fitting function is proposed to linkϕ_(e) to the rock content and size of SRMs.When other influencing factors are fixed,the above fitting model leads to the following conclusions:(1)the predictedϕ_(e) of SRMs increase with the increase of rock content;and(2)SRM samples with smaller rocks display a higher predictedϕ_(e).
文摘Existing numerical methods for complex composites, such as multiscale simulation and neural network algorithms, face significant limitations. Multiscale techniques are often prohibitively expensive for large models, while neural networks struggle to represent underlying microscopic material properties. To overcome these challenges, a meso-micro scale numerical method using a virtual node approach is developed in this study. A Wbraid/Al/Epoxy functional structural material is fabricated, and a representative periodic unit cell is identified based on its architecture. The complex structure is then discretized into nodes, and mechanical interactions are governed by pre-defined computation rules. This virtual node method is systematically compared against both multiscale simulation and a neural network algorithm, with validation provided through mechanical experiments. The results demonstrate that the nodal operation strategy significantly reduces computational resource requirements. By quantifying microscopic bonding with coefficients, explicit interface treatment is avoided, granting the method strong adaptability to lattice materials. The method can simulate extremely complex structures using parameters from simple tests and is suited for large systems. Compared to three-point bending experiments, errors for multiscale, virtual node, and neural network methods were 12.4%, 6.9%, and 34.5%, respectively. Under dynamic compression, the errors were 2.7%, 9.3%, and 15.43%. The virtual node method demonstrated superior accuracy under static conditions, enabling efficient prediction and auxiliary development of complex structural materials.
基金Supported by the National Natural Science Foundation of China under Grant No.51975138the High-Tech Ship Scientific Research Project from the Ministry of Industry and Information Technology under Grant No.CJ05N20the National Defense Basic Research Project under Grant No.JCKY2023604C006.
文摘Marine thin plates are susceptible to welding deformation owing to their low structural stiffness.Therefore,the efficient and accurate prediction of welding deformation is essential for improving welding quality.The traditional thermal elastic-plastic finite element method(TEP-FEM)can accurately predict welding deformation.However,its efficiency is low because of the complex nonlinear transient computation,making it difficult to meet the needs of rapid engineering evaluation.To address this challenge,this study proposes an efficient prediction method for welding deformation in marine thin plate butt welds.This method is based on the coupled temperature gradient-thermal strain method(TG-TSM)that integrates inherent strain theory with a shell element finite element model.The proposed method first extracts the distribution pattern and characteristic value of welding-induced inherent strain through TEP-FEM analysis.This strain is then converted into the equivalent thermal load applied to the shell element model for rapid computation.The proposed method-particularly,the gradual temperature gradient-thermal strain method(GTG-TSM)-achieved improved computational efficiency and consistent precision.Furthermore,the proposed method required much less computation time than the traditional TEP-FEM.Thus,this study lays the foundation for future prediction of welding deformation in more complex marine thin plates.
基金supported by Beijing Natural Science Foundation(L233025)。
文摘Functionally graded material(FGM)plates are widely used in various engineering structures owing to their tailor-made mechanical properties,whereas cracked homogeneous plates constitute a canonical setting in fracture mechanics analysis.These two classes of problems respectively embody material non-uniformity and geometric discontinuity,thereby imposing more stringent requirements on numerical methods in terms of high-order field continuity and accurate defect representation.Based on the classical Kirchhoff-Love plate theory,a numerical manifold method(MLS-NMM)incorporating moving least squares(MLS)interpolation is developed for bending analysis of FGM plates and fracture simulation of homogeneous plates with defects.The method constructs an H^(2)-regular approximation with high-order continuous weighting functions and,combined with the separation of mathematical and physical covers,establishes a unified framework that accurately handles material gradients and cracks without mesh reconstruction.For the crack tip,a singular physical cover incorporating the Williams asymptotic field is introduced to achieve local enrichment,enabling the natural capture of displacement discontinuity and stress singularity.Stress intensity factors are extracted using the interaction integral method,and the dimensionless J-integral shows a maximum relative error below 1.2%compared with the reference solution.Numerical results indicate that MLS-NMM exhibits excellent convergence performance:using 676 mathematical nodes,the nondimensional central deflection of both FGM and homogeneous plates agrees with reference solutions with a maximum relative error below 0.81%,and no shear locking occurs.A systematic analysis reveals that for a simply supported on all four edges(SSSS)FGM square plate with a/h=10,the nondimensional central deflection increases by 212%as the gradient index nrises from 0 to 5.For a homogeneous plate containing a central crack with c/a=0.6,the nondimensional central deflection increases by approximately 46%compared with the intact plate.Under weak boundary constraints(e.g.,SFSF),the deformation is markedly amplified,with the deflection reaching more than three times that under strong constraints(SCSC).The proposed method provides an efficient,reconstruction-free numerical tool for high-accuracy bending and fracture analyses of FGM and cracked thin-plate structures.
基金National Key Research and Development Program of China(2022YFB4600902)Shandong Provincial Science Foundation for Outstanding Young Scholars(ZR2024YQ020)。
文摘Wire arc additive manufacturing(WAAM)has emerged as a promising approach for fabricating large-scale components.However,conventional WAAM still faces challenges in optimizing microstructural evolution,minimizing additive-induced defects,and alleviating residual stress and deformation,all of which are critical for enhancing the mechanical performance of the manufactured parts.Integrating interlayer friction stir processing(FSP)into WAAM significantly enhances the quality of deposited materials.However,numerical simulation research focusing on elucidating the associated thermomechanical coupling mechanisms remains insufficient.A comprehensive numerical model was developed to simulate the thermomechanical coupling behavior in friction stir-assisted WAAM.The influence of post-deposition FSP on the coupled thermomechanical response of the WAAM process was analyzed quantitatively.Moreover,the residual stress distribution and deformation behavior under both single-layer and multilayer deposition conditions were investigated.Thermal analysis of different deposition layers in WAAM and friction stir-assisted WAAM was conducted.Results show that subsequent layer deposition induces partial remelting of the previously solidified layer,whereas FSP does not cause such remelting.Furthermore,thermal stress and deformation analysis confirm that interlayer FSP effectively mitigates residual stresses and distortion in WAAM components,thereby improving their structural integrity and mechanical properties.
基金supported by the National Natural Science Foundation of China(Nos.12572465,12032005).
文摘Granular flow,such as hopper discharge and debris flows,involves complex multi-scale,multi-phase,and multi-physics coupling,posing significant challenges for numerical simulation.Over the past two decades,methods like the Discrete Element Method(DEM),Smoothed Particle Hydrodynamics(SPH),and Depth-Averaging Method(DAM),have been developed to address these problems.However,their applicability across different scales remains unclear due to differences in physical assumptions and numerical algorithms.Therefore,a comprehensive evaluation is critically needed.This study selects three typical methods(DEM,SPH,and DAM)to examine their convergence behavior,boundary condition implementation,and limitations in physical and numerical modeling.We numerically studied three extreme deformation flow cases with the three chosen methods.These cases include granular column collapse at the particle scale,flow-structure interaction at the laboratory scale,and reconstruction of the 2015 Shenzhen Guangming landslide at the field scale.By comparing the granular flow dynamics,deposition morphology,and structure interactions,and also the simulation accuracy and computational efficiency,we show the applicability of the three models across different scales.Further,we provide practical guidance for model selection in large-deformation flow problems in a granular system of different scales.
基金supported by the European Union in the frame of Horizon Europe AVANTIS project,Grant Agreement No.101137552.
文摘Burden is one of the main parameters in blast design.However,field tests,either single-or multi-hole blasts,used to determine an appropriate burden,are difficult to capture crack propagation,evolution of breakage angle,and the mechanism governing these processes in the rock.In this study,a single-hole bench blasting model is developed using LS-DYNA software to comprehensively investigate the relationship between burden and rock breakage.The simulation results show that the breakage angle decreases with the increase in burden,and the blasted volume reaches a peak value with a burden of 4 m.Meanwhile,backbreak distance increases with increasing burden.The optimum burden in this simulation is found to be 4.0 m,as the ratio of burden to blasthole diameter is equal to 20.62 and the ratio of burden to bench height is 0.44,based on a comprehensive analysis of the blasted volume,average damage,and total damage.Under the optimum burden condition,tensile stress wave regions are simultaneously generated at the free surfaces of both the bench top and bench slope,allowing more effective utilization of the two free surfaces and resulting in a more uniform damage distribution within the burden region.
基金The National Natural Science Foundation of China(Grant No.52201376)the Natural Science Foundation of Hubei Province,China(Grant No.2023AFB683).
文摘In this investigation,a hybrid approach integrating the IDDES turbulence model and FW-H is employed to forecast the hydroacoustic of the rim driven thruster(RDT)under non-cavitation and uniform flow conditions at varying loading conditions(J=0.3 and J=0.6).It is revealed that the quadrupole term contribution in the P-FWH method significantly affects the monopole term in the low-frequency region,while it mainly affects the dipole term in the high-frequency region.Specifically,the overall sound pressure levels(SPL)of the RDT using the P-FWH method are 2.27 dB,10.03 dB,and 16.73 dB at the receiving points from R1 to R3 under the heavy-loaded condition,while they increase by 0.67 dB at R1,and decrease by 14.93 dB at R2,and 22.20 dB at R3,for the light-loaded condition.The study also utilizes the pressure-time derivatives to visualize the numerical noise and to pinpoint the dynamics of the vortex cores,and the optimization of the grid design can significantly reduce the numerical noise.The computational accuracy of the P-FWH method can meet the noise requirements for the preliminary design of rim driven thrusters.
文摘Ice crystal icing is an important cause of accidents in aircraft engines.Ice formation in aircraft engines can cause internal blades to freeze,affecting the quality of the air flow field and blocking the flow path.On the other hand,the entry of ice crystal particles into the combustion chamber can cause a decrease in temperature or even flameout,leading to engine surge or shutdown.Therefore,it is necessary to conduct multiphase flow tests on ice crystals for aircraft components such as aircraft engines.Conducting ice crystal multiphase flow tests on aircraft is an effective research method,but it requires the construction of an ice crystal multiphase flow test platform that meets relevant technical requirements.The paper focuses on the relevant experimental requirements and combines wind tunnel test structures to conduct multiphase flow numerical simulations on various forms of jet pipelines,obtaining particle motion distribution results.After comparison,the optimal form of jet structure is obtained,providing the best selection scheme for the design of relevant wind tunnel structures.
基金financially supported by the National Key Research and Development Program of China (No. 2023YFB3812601)the National Natural Science Foundation of China (No. 51925401)the Young Elite Scientists Sponsorship Program by CAST, China (No. 2022QNRC001)。
文摘Machine learning-assisted methods for rapid and accurate prediction of temperature field,mushy zone,and grain size were proposed for the heating−cooling combined mold(HCCM)horizontal continuous casting of C70250 alloy plates.First,finite element simulations of casting processes were carried out with various parameters to build a dataset.Subsequently,different machine learning algorithms were employed to achieve high precision in predicting temperature fields,mushy zone locations,mushy zone inclination angle,and billet grain size.Finally,the process parameters were quickly optimized using a strategy consisting of random generation,prediction,and screening,allowing the mushy zone to be controlled to the desired target.The optimized parameters are 1234℃for heating mold temperature,47 mm/min for casting speed,and 10 L/min for cooling water flow rate.The optimized mushy zone is located in the middle of the second heat insulation section and has an inclination angle of roughly 7°.
基金supported by the Research Council of Lithuania(LMTLT),agreement no.S-MIP-22-5.
文摘Adhesively bonded joints are widely used in modern lightweight structures due to their high strengthto-weight ratio and design flexibility.However,the reliable non-destructive evaluation of bond integrity remains a significant challenge.This study presents a numerical investigation of adhesively bonded joints with different adhesive properties using ultrasonic guided waves.The main focus of the investigation is to evaluate the feasibility of using guided waves to assess bond integrity,particularly for detecting challenging weak bonds.For this purpose,a theoretical analysis of dispersion curves was conducted,revealing that the S0 Lamb wave mode is significantly sensitive to variations in adhesive properties in the 300-700 kHz frequency range.Finite element modelling was used to analyse the propagation of guided waves in two scenarios:an adhesively bonded aluminum structure and a more complex configuration-adhesively bonded lap joints.The Short-Time Fourier Transform(STFT)was used to process the obtained results and determine the group velocities of guided waves.By analysing the group velocity characteristics,their dependence on the adhesive properties was identified.In the first scenario,a clear separation of S0 modes from A0 modes was observed in the STFT analysis,with a decrease in group velocity as adhesive stiffness increased.For the more complex lap joint scenario,the separation between A0 and S0 modes was less distinct.However,the analysis of the average group velocity shows a dependence of average group velocity on adhesive properties.This is similar to the first scenario.There is a decrease in average group velocity as adhesive stiffness increases.The results obtained demonstrate that guided wavebased methods have a high potential for non-destructive evaluation of adhesively bonded structures,including the detection of weak bonds.
基金supported by National Key Research&Development Program of China(2022YFB4101500).
文摘Regenerative catalytic oxidizers(RCO)are widely used to remove volatile organic compounds(VOCs)due to their energy-saving and stability.In this study,a multi-component catalytic reaction model was constructed to numerically investigate the reaction process of hydrocarbon-containing VOCs in RCO using computational fluid dynamics(CFD)simulation.To obtain the conversion characteristics of multi-component hydrocarbons,the effects of intake load,equivalence ratio,and the composition of multi-component hydrocarbons on the flow,heat transfer,and conversion rate of the reactor were analyzed.A feasibility study plan targeting the hard-to-convert components was also proposed.The results indicated that as the load increases,the conversion rates of the various components decrease,while the reaction rates increase.Moreover,increasing the flow velocity intensifies turbulence and enhances the collision frequency between the gas and the wall surfaces.This,in turn,amplifies the resistance effect of the porous medium.As the equivalence ratio of VOCs to oxygen increases,the oxygen-deficient condition leads to a decrease in the molecular weight of the hydrocarbons involved in the reaction.The reaction temperature also shows a downward trend.A comparative analysis of the catalytic combustion characteristics of multi-component VOCs and single-component gases reveals that adding ethane and propane can facilitate methane oxidation.
基金supported by the Key Project of Natural Science Foundation of Tianjin(No.23JCZDJC00330)Tianjin Municipal Education Commission Scientific Research Program(No.2022ZD056).
文摘Prenatal exposure to bisphenols and metals has raised significant concerns regarding their potential impact on fetal development,particularly the risk of fetal chromosome numerical abnormalities(CNA).In this case-control study,we analyzed bisphenol and metal concentrations in amniotic fluid of high-risk pregnant women undergoing amniocentesis.Concentrations of bisphenols and metals were measured using ultra-performance liquid chromatography-tandem mass spectrometry and inductively coupled plasma-mass spectrometry,respectively.Logistic regression and quantile-based g-computation were applied to evaluate individual and combined effects,while dose-response relationships were assessed using restricted cubic splines.Our findings indicated that bisphenol S(BPS),bisphenol Z(BPZ),bisphenol AF(BPAF),antimony(Sb),and vanadium(V)were significantly associated with an increased risk of CNA when analyzed individually,whereas manganese,iron,copper(Cu),nickel(Ni),and zinc(Zn)were significantly and inversely associated with CNA risk.Combined exposure to bisphenol and metal mixtures was associated with an increased risk of CNA in multi-pollutant models.Cu and Ni exhibited a positive additive interaction.Furthermore,BPS,BPZ,and BPAF were individually associated with an increased risk of Down syndrome,while Zn was associated with a decreased risk of Down syndrome.BPS,Sb,V,and Zn were individually associated with an increased risk of Klinefelter syndrome.These findings underscore the potential role of prenatal bisphenol and metal exposure in the pathogenesis of fetal CNA,highlighting both additive and synergistic effects.
