This study investigates the flexural performance of ultra-high performance concrete(UHPC)in reinforced concrete T-beams,focusing on the effects of interfacial treatments.Three concrete T-beam specimens were fabricated...This study investigates the flexural performance of ultra-high performance concrete(UHPC)in reinforced concrete T-beams,focusing on the effects of interfacial treatments.Three concrete T-beam specimens were fabricated and tested:a control beam(RC-T),a UHPC-reinforced beam with a chiseled interface(UN-C-50F),and a UHPC-reinforced beam featuring both a chiseled interface and anchored steel rebars(UN-CS-50F).The test results indicated that both chiseling and the incorporation of anchored rebars effectively created a synergistic combination between the concrete T-beam and the UHPC reinforcement layer,with the UN-CS-50F exhibiting the highest flexural resistance.The cracking load and ultimate load of UN-CS-50F were 221.5%and 40.8%,respectively,higher than those of the RC-T.Finite element(FE)models were developed to provide further insights into the behavior of the UHPCreinforced T-beams,showing a maximumdeviation of just 8%when validated against experimental data.A parametric analysis varied the height,thickness,andmaterial strength of the UHPC reinforcement layer based on the validated FE model,revealing that increasing the UHPC layer thickness from 30 to 50 mm improved the ultimate resistance by 20%while reducing the UHPC reinforcement height from 440 to 300 mm led to a 10%decrease in bending resistance.The interfacial anchoring rebars significantly reduced crack propagation and enhanced stress redistribution,highlighting the importance of strengthening interfacial bonds and optimizing geometric parameters ofUHPCfor improved T-beam performance.These findings offer valuable insights for the design and retrofitting of UHPC-reinforced bridge girders.展开更多
Accurate estimation of mineralogy from geophysical well logs is crucial for characterizing geological formations,particularly in hydrocarbon exploration,CO_(2) sequestration,and geothermal energy development.Current t...Accurate estimation of mineralogy from geophysical well logs is crucial for characterizing geological formations,particularly in hydrocarbon exploration,CO_(2) sequestration,and geothermal energy development.Current techniques,such as multimineral petrophysical analysis,offer details into mineralogical distribution.However,it is inherently time-intensive and demands substantial geological expertise for accurate model evaluation.Furthermore,traditional machine learning techniques often struggle to predict mineralogy accurately and sometimes produce estimations that violate fundamental physical principles.To address this,we present a new approach using Physics-Integrated Neural Networks(PINNs),that combines data-driven learning with domain-specific physical constraints,embedding petrophysical relationships directly into the neural network architecture.This approach enforces that predictions adhere to physical laws.The methodology is applied to the Broom Creek Deep Saline aquifer,a CO_(2) sequestration site in the Williston Basin,to predict the volumes of key mineral constituents—quartz,dolomite,feldspar,anhydrite,illite—along with porosity.Compared to traditional artificial neural networks (ANN),the PINN approach demonstrates higher accuracy and better generalizability,significantly enhancing predictive performance on unseen well datasets.The average mean error across the three blind wells is 0.123 for ANN and 0.042 for PINN,highlighting the superior accuracy of the PINN approach.This method reduces uncertainties in reservoir characterization by improving the reliability of mineralogy and porosity predictions,providing a more robust tool for decision-making in various subsurface geoscience applications.展开更多
The graded density impactor(GDI)dynamic loading technique is crucial for acquiring the dynamic physical property parameters of materials used in weapons.The accuracy and timeliness of GDI structural design are key to ...The graded density impactor(GDI)dynamic loading technique is crucial for acquiring the dynamic physical property parameters of materials used in weapons.The accuracy and timeliness of GDI structural design are key to achieving controllable stress-strain rate loading.In this study,we have,for the first time,combined one-dimensional fluid computational software with machine learning methods.We first elucidated the mechanisms by which GDI structures control stress and strain rates.Subsequently,we constructed a machine learning model to create a structure-property response surface.The results show that altering the loading velocity and interlayer thickness has a pronounced regulatory effect on stress and strain rates.In contrast,the impedance distribution index and target thickness have less significant effects on stress regulation,although there is a matching relationship between target thickness and interlayer thickness.Compared with traditional design methods,the machine learning approach offers a10^(4)—10^(5)times increase in efficiency and the potential to achieve a global optimum,holding promise for guiding the design of GDI.展开更多
A suction casting experiment was conducted on Zr_(55)Cu_(30)Al_(10)Ni_(5)(at%)amorphous alloy.Using ProCAST software,numerical simulations were performed to analyze the filling and solidification processes.The velocit...A suction casting experiment was conducted on Zr_(55)Cu_(30)Al_(10)Ni_(5)(at%)amorphous alloy.Using ProCAST software,numerical simulations were performed to analyze the filling and solidification processes.The velocity field during the filling process and the temperature field during the solidification process of the alloy melt under different process parameters were obtained.Based on the simulation results,a Zr-based amorphous alloy micro-gear was prepared via casting.The results indicate that increasing the suction casting temperature enhances the fluidity of alloy melt but induces unstable flow rate during filling,which is detrimental to complete filling.Zr-based amorphous micro-gears with a module of 0.6 mm,a tooth top diameter of 8 mm,and 10 teeth were prepared through the suction casting.X-ray diffraction and differential scanning calorimetry analyses confirm that the fabricated micro-gear exhibits characteristic amorphous structural features,demonstrating well-defined geometrical contours and satisfactory forming completeness.展开更多
A three-dimensional numerical model of laser-arc hybrid plasma for aluminum alloy fillet joints is developed in this study.This mod-el accounts for the geometric complexity of fillet joints,the physical properties of ...A three-dimensional numerical model of laser-arc hybrid plasma for aluminum alloy fillet joints is developed in this study.This mod-el accounts for the geometric complexity of fillet joints,the physical properties of shielding gases with varying He-Ar ratios,and the coupling between arc plasma and laser-induced metal plume.The accuracy of the model is validated using a high-speed camera.The effects of varying He contents in the shielding gas on both the temperature and flow velocity of hybrid plasma,as well as the distribu-tion of laser-induced metal vapor mass,were investigated separately.The maximum temperature and size of arc plasma decrease as the He volume ratio increases,the arc distribution becomes more concentrated,and its flow velocity initially decreases and then sharply increases.At high helium content,both the flow velocity of hybrid plasma and metal vapor are high,the metal vapor is con-centrated on the right side of keyhole,and its flow appears chaotic.The flow state of arc plasma is most stable when the shielding gas consists of 50%He+50%Ar.展开更多
Heat treatment processes, such as annealing and quenching, are crucial in determining residual stress evolution, microstructural changes and mechanical properties of metallic materials, with residual stresses playing ...Heat treatment processes, such as annealing and quenching, are crucial in determining residual stress evolution, microstructural changes and mechanical properties of metallic materials, with residual stresses playing a greater role in the performance of components. This paper investigates the effect of heat treatment on residual stresses induced in AISI 1025, manufactured using LENS. Finite element model was developed and simulated to analyze residual stress development. AISI 1025 samples suitable for tool and die applications in Fused Deposition Modelling (FDM) filament production, were fabricated using Laser Engineered Net Shaping (LENS) process, followed by heat treatment where annealing and quenching processes were done. The material’s microstructure, residual stress and hardness of heat-treated samples under investigation, were compared against the as-built samples. The results indicated that after annealing, tensile residual stresses were reduced by 93%, resulting in a reduced crack growth rate, compared to the as-built sample, although the hardness was reduced significantly by 25%. On the other hand, high tensile residual stresses of 425 ± 14 MPa were recorded after quenching process with an improvement of hardness by 21%.展开更多
We study the trimer state in a three-body system,where two of the atoms are subject to Rashba-type spin-orbit coupling and spin-dependent loss while interacting spin-selectively with the third atom.The short-time cond...We study the trimer state in a three-body system,where two of the atoms are subject to Rashba-type spin-orbit coupling and spin-dependent loss while interacting spin-selectively with the third atom.The short-time conditional dynamics of the three-body system is effectively governed by a non-Hermitian Hamiltonian with an imaginary Zeeman field.Remarkably,the interplay of non-Hermitian single particle dispersion and the spin-selective interaction results in a Borromean state and an enlarged trimer phase.The stability of trimer state can be reflected by the imaginary part of trimer energy and the momentum distribution of trimer wave function.We also show the phase diagram of the three-body system under both real and imaginary Zeeman fields.Our results illustrate the interesting consequence of non-Hermitian spectral symmetry on the few-body level,which may be readily observable in current cold-atom experiments.展开更多
Rock is geometrically and mechanically multiscale in nature,and the traditional phenomenological laws at the macroscale cannot render a quantitative relationship between microscopic damage of rocks and overall rock st...Rock is geometrically and mechanically multiscale in nature,and the traditional phenomenological laws at the macroscale cannot render a quantitative relationship between microscopic damage of rocks and overall rock structural degradation.This may lead to problems in the evaluation of rock structure stability and safe life.Multiscale numerical modeling is regarded as an effective way to gain insight into factors affecting rock properties from a cross-scale view.This study compiles the history of theoretical developments and numerical techniques related to rock multiscale issues according to different modeling architectures,that is,the homogenization theory,the hierarchical approach,and the concurrent approach.For these approaches,their benefits,drawbacks,and application scope are underlined.Despite the considerable attempts that have been made,some key issues still result in multiple challenges.Therefore,this study points out the perspectives of rock multiscale issues so as to provide a research direction for the future.The review results show that,in addition to numerical techniques,for example,high-performance computing,more attention should be paid to the development of an advanced constitutive model with consideration of fine geometrical descriptions of rock to facilitate solutions to multiscale problems in rock mechanics and rock engineering.展开更多
In the realm of all-electric aircraft research,the integration of cathode-open proton exchange membrane fuel cells(PEMFC)with lithiumbatteries as a hybrid power source for small to medium-sized unmanned aerial vehicle...In the realm of all-electric aircraft research,the integration of cathode-open proton exchange membrane fuel cells(PEMFC)with lithiumbatteries as a hybrid power source for small to medium-sized unmanned aerial vehicles(UAVs)has garnered significant attention.The PEMFC,serving as the primary energy supply,markedly extends the UAV’s operational endurance.However,due to payload limitations and spatial constraints in the airframe layout of UAVs,the stack requires customized adaptation.Moreover,the implementation of auxiliary systems to facilitate cold starts of the PEMFC under low-temperature conditions is not feasible.Relying solely on thermal insulation measures also proves inadequate to address the challenges posed by complex low-temperature startup scenarios.To overcomethis,our study leverages the UAV’s lithium battery to heat the cathode inlet airflow,aiding the cathode-open PEMFC cold start process.To validate the feasibility of the proposed air-assisted heating strategy during the conceptual design phase,this study develops a transient,non-isothermal 3Dcathode-open PEMF Cunitmodel incorporating cathode air-assisted heating and gas-ice phase change.The model’s accuracy was verified against experimental cold-start data from a stack composed of identical single cells.This computational framework enables quantitative analysis of temperature fields and ice fraction distributions across domains under varying air-assisted heating powers during cold starts.Building upon this model,the study further investigates the improvement in cold start performance by heating the cathode intake air with varying power levels.The results demonstrate that the fuel cell achieves self-startup at temperatures as low as−13℃ under a constant current density of 100mA/cm^(2) without air-assisted heating.At an ambient temperature of−20℃,a successful start-up can be achieved with a heating power of 0.45 W/cm^(2).The temperature variation overtime during the cold start process can be represented by a sum of two exponential functions.The air-assisted heating scheme proposed in this study has significantly improved the cold start performance of fuel cells in low-temperature environments.Additionally,it provides critical reference data and validation support for component selection and feasibility assessment of hybrid power systems.展开更多
Numerical modelling is an effective technique to improve the understanding of outburst initiation mechanisms and to take appropriate measures to address their threats.Based on the existing two-way sequential coupling ...Numerical modelling is an effective technique to improve the understanding of outburst initiation mechanisms and to take appropriate measures to address their threats.Based on the existing two-way sequential coupling method,two typical types of outbursts,i.e.the gas pocket outburst and the dynamic fracturing outburst,have been successfully simulated using field data from a coalfield in central China.The geological structure commonly observed in the coalfield,known as the‘bedding shear zone’,contributes to the gas pocket outbursts in the region.The model for this type of outburst simulates mininginduced stress and gas pressure distributions during the outburst initiation stage and the subsequent development stage.Both coal ejection and gas release are observed in the model,and the simulation results are consistent with mine site observations,i.e.the amount of ejected coal,outburst cavity profile,and gas release rate change prior to an outburst.The second type of outburst is attributed to gas accumulation and elevated gas pressure due to the gassy floor seam and the heterogeneity in the floor strata,which is explained by the dynamic fracturing theory.While the dynamic coal ejection phenomenon is not captured in the simulation,the abrupt release of retained gas from a floor coal seam is successfully replicated.Both outburst models reveal that abnormal gas emission trends can be used as indicators of an upcoming outburst.The results of this study are expected to provide new insights into the outburst initiation mechanisms and outburst prevention measures.展开更多
Following the fundamental characteristics of the porosity windbreak,this study suggests a new numerical investigation method for the wind field of the windbreak based on the porous medium physical model.This method ca...Following the fundamental characteristics of the porosity windbreak,this study suggests a new numerical investigation method for the wind field of the windbreak based on the porous medium physical model.This method can transform the reasonable matching problem of the porosity and windproof performance of the windbreak into a study of the relationship between the resistance coefficient of the porous medium and the aerodynamic load of the train.This study examines the influence of the hole type on the wind field behind the porosity windbreak.Then,the relationship between the resistance coefficient of the porous medium,the porosity of the windbreak,and the aerodynamic loads of the train is investigated.The results show that the porous media physical model can be used instead of the windbreak geometry to study the windbreak-train aerodynamic performance,and the process of using this method is suggested.展开更多
Full waveform inversion(FWI)has showed great potential in the detection of musculoskeletal disease.However,FWI is an ill-posed inverse problem and has a high requirement on the initial model during the imaging process...Full waveform inversion(FWI)has showed great potential in the detection of musculoskeletal disease.However,FWI is an ill-posed inverse problem and has a high requirement on the initial model during the imaging process.An inaccurate initial model may lead to local minima in the inversion and unexpected imaging results caused by cycle-skipping phenomenon.Deep learning methods have been applied in musculoskeletal imaging,but need a large amount of data for training.Inspired by work related to generative adversarial networks with physical informed constrain,we proposed a method named as bone ultrasound imaging with physics informed generative adversarial network(BUIPIGAN)to achieve unsupervised multi-parameter imaging for musculoskeletal tissues,focusing on speed of sound(SOS)and density.In the in-silico experiments using a ring array transducer,conventional FWI methods and BUIPIGAN were employed for multiparameter imaging of two musculoskeletal tissue models.The results were evaluated based on visual appearance,structural similarity index measure(SSIM),signal-to-noise ratio(SNR),and relative error(RE).For SOS imaging of the tibia–fibula model,the proposed BUIPIGAN achieved accurate SOS imaging with best performance.The specific quantitative metrics for SOS imaging were SSIM 0.9573,SNR 28.70 dB,and RE 5.78%.For the multi-parameter imaging of the tibia–fibula and human forearm,the BUIPIGAN successfully reconstructed SOS and density distributions with SSIM above 94%,SNR above 21 dB,and RE below 10%.The BUIPIGAN also showed robustness across various noise levels(i.e.,30 dB,10 dB).The results demonstrated that the proposed BUIPIGAN can achieve high-accuracy SOS and density imaging,proving its potential for applications in musculoskeletal ultrasound imaging.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
ABOUT THIS JOURNAL Launched in 1988,the Chinese Journal of Chemical Physics (CJCP)is devoted to reporting new and original experimental and theoretical research on interdisciplinary areas,with chemistry and physics gr...ABOUT THIS JOURNAL Launched in 1988,the Chinese Journal of Chemical Physics (CJCP)is devoted to reporting new and original experimental and theoretical research on interdisciplinary areas,with chemistry and physics groundwork of interest to researchers,faculty and students domestic and abroad in the fields of chemistry,physics,material and biological sciences and their interdisciplinary areas.As one of the 24 peer-reviewed journals under the Chinese Physical Society (CPS),CJCP has been covered in ISI products (SCIE) as well as other major indexes.CJCP is currently a bimonthly journal,and it publishes in English with Chinese abstract as of 2006.展开更多
The primary goal of this study is to provide an efficient numerical tool to analyze the seismic performance of nailed walls.Modeling such excavation supports involves complexities due partly to the interaction of supp...The primary goal of this study is to provide an efficient numerical tool to analyze the seismic performance of nailed walls.Modeling such excavation supports involves complexities due partly to the interaction of support with soil and partly because of the amplification of seismic waves through an excavation wall.Consequently,innovative modeling is suggested herein,incorporating the calibration of the soil constitutive model in a targeted range of stress and strain,and the detection of a natural period of complex systems,including soil and structure,while benefiting from Rayleigh damping to filter unwanted noises.The numerical model was achieved by simulating a previous centrifuge test of the excavation wall,manifested at the pre-failure state.Notably,the calibration of the soil constitutive model through empirical relations,which replaces the numerical reproduction of an element test,more accurately simulated the soil-nail-wall interaction.Two factors were crucial to a successful result.First,probing the natural period of the complicated geometry of the model by applying white noises.Second,considering Rayleigh damping to withdraw unwanted noises and thus assess their permanent effects on the model.Rayleigh damping was applied instead of filtering the obtained results.展开更多
With the development of educational digitalization,how to effectively apply digital animation technology to traditional classroom teaching has become an urgent problem to be solved.This study explores the application ...With the development of educational digitalization,how to effectively apply digital animation technology to traditional classroom teaching has become an urgent problem to be solved.This study explores the application of Manim in the course of Mathematical Methods for Physics.Taking the visualization of Fourier series,complex numbers,and other content as examples,it improves students’understanding of complex and abstract mathematical physics concepts through dynamic and visual teaching methods.The teaching effect shows that Manim helps to enhance students’learning experience,improve teaching efficiency and effectiveness,and has a positive impact on students’active learning ability.The research in this paper can provide references and inspiration for the educational digitalization of higher education.展开更多
Deep learning(DL)is making significant inroads into biomedical imaging as it provides novel and powerful ways of accurately and efficiently improving the image quality of photoacoustic microscopy(PAM).Off-the-shelf DL...Deep learning(DL)is making significant inroads into biomedical imaging as it provides novel and powerful ways of accurately and efficiently improving the image quality of photoacoustic microscopy(PAM).Off-the-shelf DL models,however,do not necessarily obey the fundamental governing laws of PAM physical systems,nor do they generalize well to scenarios on which they have not been trained.In this work,a physics-embedded degeneration learning(PEDL)approach is proposed to enhance the image quality of PAM with a self-attention enhanced U-Net network,which obtains greater physical consistency,improves data efficiency,and higher adaptability.The proposed method is demonstrated on both synthetic and real datasets,including animal experiments in vivo(blood vessels of mouse's ear and brain).And the results show that compared with previous DL methods,the PEDL algorithm exhibits good performance in recovering PAM images qualitatively and quantitatively.It overcomes the challenges related to training data,accuracy,and robustness which a typical data-driven approach encounters,whose exemplary application envisions to provide a new perspective for existing DL tools of enhanced PAM.展开更多
Icing of water droplets is a ubiquitous phenomenon with significant implications across natural systems and industrial applications.Despite extensive research,the intricate interplay among heat transfer,mass transport...Icing of water droplets is a ubiquitous phenomenon with significant implications across natural systems and industrial applications.Despite extensive research,the intricate interplay among heat transfer,mass transport,and phase change during droplet freezing remains incompletely understood,particularly in the context of multiscale dynamics and environmental dependencies.This review critically examines recent advances in uncovering the fundamental mechanisms of droplet icing through experimental,theoretical,and computational approaches.We begin by revisiting the classical tip singularity problem in the freezing of pure water droplets,analyzing its mathematical formulation and physical significance.Subsequent sections explore how environmental boundary conditions and multicomponent effects influence freezing kinetics,solute redistribution,and ice morphology.Furthermore,we evaluate emerging hybrid numerical frameworks that resolve coupled multiphase physics during solidification processes.Finally,we identify key challenges and open questions that require further investigation in this field.展开更多
基金The National Natural Science Foundation of China(Grant#52278161)the Science and Technology Project of Guangzhou(Grant#2024A04J9888)the Guangdong Basic and Applied Basic Research Foundation(Grant#2023A1515010535).
文摘This study investigates the flexural performance of ultra-high performance concrete(UHPC)in reinforced concrete T-beams,focusing on the effects of interfacial treatments.Three concrete T-beam specimens were fabricated and tested:a control beam(RC-T),a UHPC-reinforced beam with a chiseled interface(UN-C-50F),and a UHPC-reinforced beam featuring both a chiseled interface and anchored steel rebars(UN-CS-50F).The test results indicated that both chiseling and the incorporation of anchored rebars effectively created a synergistic combination between the concrete T-beam and the UHPC reinforcement layer,with the UN-CS-50F exhibiting the highest flexural resistance.The cracking load and ultimate load of UN-CS-50F were 221.5%and 40.8%,respectively,higher than those of the RC-T.Finite element(FE)models were developed to provide further insights into the behavior of the UHPCreinforced T-beams,showing a maximumdeviation of just 8%when validated against experimental data.A parametric analysis varied the height,thickness,andmaterial strength of the UHPC reinforcement layer based on the validated FE model,revealing that increasing the UHPC layer thickness from 30 to 50 mm improved the ultimate resistance by 20%while reducing the UHPC reinforcement height from 440 to 300 mm led to a 10%decrease in bending resistance.The interfacial anchoring rebars significantly reduced crack propagation and enhanced stress redistribution,highlighting the importance of strengthening interfacial bonds and optimizing geometric parameters ofUHPCfor improved T-beam performance.These findings offer valuable insights for the design and retrofitting of UHPC-reinforced bridge girders.
基金the North Dakota Industrial Commission (NDIC) for their financial supportprovided by the University of North Dakota Computational Research Center。
文摘Accurate estimation of mineralogy from geophysical well logs is crucial for characterizing geological formations,particularly in hydrocarbon exploration,CO_(2) sequestration,and geothermal energy development.Current techniques,such as multimineral petrophysical analysis,offer details into mineralogical distribution.However,it is inherently time-intensive and demands substantial geological expertise for accurate model evaluation.Furthermore,traditional machine learning techniques often struggle to predict mineralogy accurately and sometimes produce estimations that violate fundamental physical principles.To address this,we present a new approach using Physics-Integrated Neural Networks(PINNs),that combines data-driven learning with domain-specific physical constraints,embedding petrophysical relationships directly into the neural network architecture.This approach enforces that predictions adhere to physical laws.The methodology is applied to the Broom Creek Deep Saline aquifer,a CO_(2) sequestration site in the Williston Basin,to predict the volumes of key mineral constituents—quartz,dolomite,feldspar,anhydrite,illite—along with porosity.Compared to traditional artificial neural networks (ANN),the PINN approach demonstrates higher accuracy and better generalizability,significantly enhancing predictive performance on unseen well datasets.The average mean error across the three blind wells is 0.123 for ANN and 0.042 for PINN,highlighting the superior accuracy of the PINN approach.This method reduces uncertainties in reservoir characterization by improving the reliability of mineralogy and porosity predictions,providing a more robust tool for decision-making in various subsurface geoscience applications.
基金supported by the Guangdong Major Project of Basic and Applied Basic Research(Grant No.2021B0301030001)the National Key Research and Development Program of China(Grant No.2021YFB3802300)the Foundation of National Key Laboratory of Shock Wave and Detonation Physics(Grant No.JCKYS2022212004)。
文摘The graded density impactor(GDI)dynamic loading technique is crucial for acquiring the dynamic physical property parameters of materials used in weapons.The accuracy and timeliness of GDI structural design are key to achieving controllable stress-strain rate loading.In this study,we have,for the first time,combined one-dimensional fluid computational software with machine learning methods.We first elucidated the mechanisms by which GDI structures control stress and strain rates.Subsequently,we constructed a machine learning model to create a structure-property response surface.The results show that altering the loading velocity and interlayer thickness has a pronounced regulatory effect on stress and strain rates.In contrast,the impedance distribution index and target thickness have less significant effects on stress regulation,although there is a matching relationship between target thickness and interlayer thickness.Compared with traditional design methods,the machine learning approach offers a10^(4)—10^(5)times increase in efficiency and the potential to achieve a global optimum,holding promise for guiding the design of GDI.
基金National Natural Science Foundation of China(51971103)Key Research and Development Program in Gansu Province(20YF8GA052)。
文摘A suction casting experiment was conducted on Zr_(55)Cu_(30)Al_(10)Ni_(5)(at%)amorphous alloy.Using ProCAST software,numerical simulations were performed to analyze the filling and solidification processes.The velocity field during the filling process and the temperature field during the solidification process of the alloy melt under different process parameters were obtained.Based on the simulation results,a Zr-based amorphous alloy micro-gear was prepared via casting.The results indicate that increasing the suction casting temperature enhances the fluidity of alloy melt but induces unstable flow rate during filling,which is detrimental to complete filling.Zr-based amorphous micro-gears with a module of 0.6 mm,a tooth top diameter of 8 mm,and 10 teeth were prepared through the suction casting.X-ray diffraction and differential scanning calorimetry analyses confirm that the fabricated micro-gear exhibits characteristic amorphous structural features,demonstrating well-defined geometrical contours and satisfactory forming completeness.
基金supported by the National Natural Science Foundation of China(Grant No.52375340,51975263,52405366).
文摘A three-dimensional numerical model of laser-arc hybrid plasma for aluminum alloy fillet joints is developed in this study.This mod-el accounts for the geometric complexity of fillet joints,the physical properties of shielding gases with varying He-Ar ratios,and the coupling between arc plasma and laser-induced metal plume.The accuracy of the model is validated using a high-speed camera.The effects of varying He contents in the shielding gas on both the temperature and flow velocity of hybrid plasma,as well as the distribu-tion of laser-induced metal vapor mass,were investigated separately.The maximum temperature and size of arc plasma decrease as the He volume ratio increases,the arc distribution becomes more concentrated,and its flow velocity initially decreases and then sharply increases.At high helium content,both the flow velocity of hybrid plasma and metal vapor are high,the metal vapor is con-centrated on the right side of keyhole,and its flow appears chaotic.The flow state of arc plasma is most stable when the shielding gas consists of 50%He+50%Ar.
文摘Heat treatment processes, such as annealing and quenching, are crucial in determining residual stress evolution, microstructural changes and mechanical properties of metallic materials, with residual stresses playing a greater role in the performance of components. This paper investigates the effect of heat treatment on residual stresses induced in AISI 1025, manufactured using LENS. Finite element model was developed and simulated to analyze residual stress development. AISI 1025 samples suitable for tool and die applications in Fused Deposition Modelling (FDM) filament production, were fabricated using Laser Engineered Net Shaping (LENS) process, followed by heat treatment where annealing and quenching processes were done. The material’s microstructure, residual stress and hardness of heat-treated samples under investigation, were compared against the as-built samples. The results indicated that after annealing, tensile residual stresses were reduced by 93%, resulting in a reduced crack growth rate, compared to the as-built sample, although the hardness was reduced significantly by 25%. On the other hand, high tensile residual stresses of 425 ± 14 MPa were recorded after quenching process with an improvement of hardness by 21%.
基金supported by the National Natural Science Foundation of China(Grant No.11974331)。
文摘We study the trimer state in a three-body system,where two of the atoms are subject to Rashba-type spin-orbit coupling and spin-dependent loss while interacting spin-selectively with the third atom.The short-time conditional dynamics of the three-body system is effectively governed by a non-Hermitian Hamiltonian with an imaginary Zeeman field.Remarkably,the interplay of non-Hermitian single particle dispersion and the spin-selective interaction results in a Borromean state and an enlarged trimer phase.The stability of trimer state can be reflected by the imaginary part of trimer energy and the momentum distribution of trimer wave function.We also show the phase diagram of the three-body system under both real and imaginary Zeeman fields.Our results illustrate the interesting consequence of non-Hermitian spectral symmetry on the few-body level,which may be readily observable in current cold-atom experiments.
基金National Natural Science Foundation of China,Grant/Award Numbers:52192691,52192690。
文摘Rock is geometrically and mechanically multiscale in nature,and the traditional phenomenological laws at the macroscale cannot render a quantitative relationship between microscopic damage of rocks and overall rock structural degradation.This may lead to problems in the evaluation of rock structure stability and safe life.Multiscale numerical modeling is regarded as an effective way to gain insight into factors affecting rock properties from a cross-scale view.This study compiles the history of theoretical developments and numerical techniques related to rock multiscale issues according to different modeling architectures,that is,the homogenization theory,the hierarchical approach,and the concurrent approach.For these approaches,their benefits,drawbacks,and application scope are underlined.Despite the considerable attempts that have been made,some key issues still result in multiple challenges.Therefore,this study points out the perspectives of rock multiscale issues so as to provide a research direction for the future.The review results show that,in addition to numerical techniques,for example,high-performance computing,more attention should be paid to the development of an advanced constitutive model with consideration of fine geometrical descriptions of rock to facilitate solutions to multiscale problems in rock mechanics and rock engineering.
基金funded by Zhejiang Province Spearhead and Leading Goose Research and Development Key Program,grant number 2023C01239.
文摘In the realm of all-electric aircraft research,the integration of cathode-open proton exchange membrane fuel cells(PEMFC)with lithiumbatteries as a hybrid power source for small to medium-sized unmanned aerial vehicles(UAVs)has garnered significant attention.The PEMFC,serving as the primary energy supply,markedly extends the UAV’s operational endurance.However,due to payload limitations and spatial constraints in the airframe layout of UAVs,the stack requires customized adaptation.Moreover,the implementation of auxiliary systems to facilitate cold starts of the PEMFC under low-temperature conditions is not feasible.Relying solely on thermal insulation measures also proves inadequate to address the challenges posed by complex low-temperature startup scenarios.To overcomethis,our study leverages the UAV’s lithium battery to heat the cathode inlet airflow,aiding the cathode-open PEMFC cold start process.To validate the feasibility of the proposed air-assisted heating strategy during the conceptual design phase,this study develops a transient,non-isothermal 3Dcathode-open PEMF Cunitmodel incorporating cathode air-assisted heating and gas-ice phase change.The model’s accuracy was verified against experimental cold-start data from a stack composed of identical single cells.This computational framework enables quantitative analysis of temperature fields and ice fraction distributions across domains under varying air-assisted heating powers during cold starts.Building upon this model,the study further investigates the improvement in cold start performance by heating the cathode intake air with varying power levels.The results demonstrate that the fuel cell achieves self-startup at temperatures as low as−13℃ under a constant current density of 100mA/cm^(2) without air-assisted heating.At an ambient temperature of−20℃,a successful start-up can be achieved with a heating power of 0.45 W/cm^(2).The temperature variation overtime during the cold start process can be represented by a sum of two exponential functions.The air-assisted heating scheme proposed in this study has significantly improved the cold start performance of fuel cells in low-temperature environments.Additionally,it provides critical reference data and validation support for component selection and feasibility assessment of hybrid power systems.
基金the National Natural Science Foundation of China(52304105)National Natural Science Foundation of China-National major scientific research instrument development project(52227901)Jiangsu Province International Collaboration Program-Key national industrial technology research and development cooperation projects(BZ2023050).
文摘Numerical modelling is an effective technique to improve the understanding of outburst initiation mechanisms and to take appropriate measures to address their threats.Based on the existing two-way sequential coupling method,two typical types of outbursts,i.e.the gas pocket outburst and the dynamic fracturing outburst,have been successfully simulated using field data from a coalfield in central China.The geological structure commonly observed in the coalfield,known as the‘bedding shear zone’,contributes to the gas pocket outbursts in the region.The model for this type of outburst simulates mininginduced stress and gas pressure distributions during the outburst initiation stage and the subsequent development stage.Both coal ejection and gas release are observed in the model,and the simulation results are consistent with mine site observations,i.e.the amount of ejected coal,outburst cavity profile,and gas release rate change prior to an outburst.The second type of outburst is attributed to gas accumulation and elevated gas pressure due to the gassy floor seam and the heterogeneity in the floor strata,which is explained by the dynamic fracturing theory.While the dynamic coal ejection phenomenon is not captured in the simulation,the abrupt release of retained gas from a floor coal seam is successfully replicated.Both outburst models reveal that abnormal gas emission trends can be used as indicators of an upcoming outburst.The results of this study are expected to provide new insights into the outburst initiation mechanisms and outburst prevention measures.
基金Projects(52302447,52388102,52372369)supported by the National Natural Science Foundation of China。
文摘Following the fundamental characteristics of the porosity windbreak,this study suggests a new numerical investigation method for the wind field of the windbreak based on the porous medium physical model.This method can transform the reasonable matching problem of the porosity and windproof performance of the windbreak into a study of the relationship between the resistance coefficient of the porous medium and the aerodynamic load of the train.This study examines the influence of the hole type on the wind field behind the porosity windbreak.Then,the relationship between the resistance coefficient of the porous medium,the porosity of the windbreak,and the aerodynamic loads of the train is investigated.The results show that the porous media physical model can be used instead of the windbreak geometry to study the windbreak-train aerodynamic performance,and the process of using this method is suggested.
基金Project supported by the National Natural Science Foundation of China(Grant Nos.12122403 and 12327807).
文摘Full waveform inversion(FWI)has showed great potential in the detection of musculoskeletal disease.However,FWI is an ill-posed inverse problem and has a high requirement on the initial model during the imaging process.An inaccurate initial model may lead to local minima in the inversion and unexpected imaging results caused by cycle-skipping phenomenon.Deep learning methods have been applied in musculoskeletal imaging,but need a large amount of data for training.Inspired by work related to generative adversarial networks with physical informed constrain,we proposed a method named as bone ultrasound imaging with physics informed generative adversarial network(BUIPIGAN)to achieve unsupervised multi-parameter imaging for musculoskeletal tissues,focusing on speed of sound(SOS)and density.In the in-silico experiments using a ring array transducer,conventional FWI methods and BUIPIGAN were employed for multiparameter imaging of two musculoskeletal tissue models.The results were evaluated based on visual appearance,structural similarity index measure(SSIM),signal-to-noise ratio(SNR),and relative error(RE).For SOS imaging of the tibia–fibula model,the proposed BUIPIGAN achieved accurate SOS imaging with best performance.The specific quantitative metrics for SOS imaging were SSIM 0.9573,SNR 28.70 dB,and RE 5.78%.For the multi-parameter imaging of the tibia–fibula and human forearm,the BUIPIGAN successfully reconstructed SOS and density distributions with SSIM above 94%,SNR above 21 dB,and RE below 10%.The BUIPIGAN also showed robustness across various noise levels(i.e.,30 dB,10 dB).The results demonstrated that the proposed BUIPIGAN can achieve high-accuracy SOS and density imaging,proving its potential for applications in musculoskeletal ultrasound imaging.
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ABOUT THIS JOURNAL Launched in 1988,the Chinese Journal of Chemical Physics (CJCP)is devoted to reporting new and original experimental and theoretical research on interdisciplinary areas,with chemistry and physics groundwork of interest to researchers,faculty and students domestic and abroad in the fields of chemistry,physics,material and biological sciences and their interdisciplinary areas.As one of the 24 peer-reviewed journals under the Chinese Physical Society (CPS),CJCP has been covered in ISI products (SCIE) as well as other major indexes.CJCP is currently a bimonthly journal,and it publishes in English with Chinese abstract as of 2006.
基金supported by the International Institute of Earthquake Engineering and Seismology(IIEES) as technical project No.760
文摘The primary goal of this study is to provide an efficient numerical tool to analyze the seismic performance of nailed walls.Modeling such excavation supports involves complexities due partly to the interaction of support with soil and partly because of the amplification of seismic waves through an excavation wall.Consequently,innovative modeling is suggested herein,incorporating the calibration of the soil constitutive model in a targeted range of stress and strain,and the detection of a natural period of complex systems,including soil and structure,while benefiting from Rayleigh damping to filter unwanted noises.The numerical model was achieved by simulating a previous centrifuge test of the excavation wall,manifested at the pre-failure state.Notably,the calibration of the soil constitutive model through empirical relations,which replaces the numerical reproduction of an element test,more accurately simulated the soil-nail-wall interaction.Two factors were crucial to a successful result.First,probing the natural period of the complicated geometry of the model by applying white noises.Second,considering Rayleigh damping to withdraw unwanted noises and thus assess their permanent effects on the model.Rayleigh damping was applied instead of filtering the obtained results.
基金supported by the Teaching Reform Research Project of Shaanxi University of Science&Technology(23Y083)the Project of National University Association for Mathematical Methods in Physics(JZW-23-SL-02)+3 种基金the Graduate Course Construction Project of Shaanxi University of Science&Technology(KC2024Y03)the 2024 National Higher Education University Physics Reform Research Project(2024PR064)the Teaching Reform Research Project of the International Office of Shaanxi University of Science&Technology(YB202410)Graduate Education and Teaching Reform Research Project of Shaanxi University of Science&Technology(JG2025Y18).
文摘With the development of educational digitalization,how to effectively apply digital animation technology to traditional classroom teaching has become an urgent problem to be solved.This study explores the application of Manim in the course of Mathematical Methods for Physics.Taking the visualization of Fourier series,complex numbers,and other content as examples,it improves students’understanding of complex and abstract mathematical physics concepts through dynamic and visual teaching methods.The teaching effect shows that Manim helps to enhance students’learning experience,improve teaching efficiency and effectiveness,and has a positive impact on students’active learning ability.The research in this paper can provide references and inspiration for the educational digitalization of higher education.
基金supported by National Natural Science Foundation of China(62227818,12204239,62275121)Youth Foundation of Jiangsu Province(BK20220946)+1 种基金Fundamental Research Funds for the Central Universities(30923011024)Open Research Fund of Jiangsu Key Laboratory of Spectral Imaging&Intelligent Sense(JSGP202201).
文摘Deep learning(DL)is making significant inroads into biomedical imaging as it provides novel and powerful ways of accurately and efficiently improving the image quality of photoacoustic microscopy(PAM).Off-the-shelf DL models,however,do not necessarily obey the fundamental governing laws of PAM physical systems,nor do they generalize well to scenarios on which they have not been trained.In this work,a physics-embedded degeneration learning(PEDL)approach is proposed to enhance the image quality of PAM with a self-attention enhanced U-Net network,which obtains greater physical consistency,improves data efficiency,and higher adaptability.The proposed method is demonstrated on both synthetic and real datasets,including animal experiments in vivo(blood vessels of mouse's ear and brain).And the results show that compared with previous DL methods,the PEDL algorithm exhibits good performance in recovering PAM images qualitatively and quantitatively.It overcomes the challenges related to training data,accuracy,and robustness which a typical data-driven approach encounters,whose exemplary application envisions to provide a new perspective for existing DL tools of enhanced PAM.
基金supported by National Natural Science Foundation of China Excellence Research Group Program for“Multiscale Problems in Nonlinear Mechanics”(Grant No.12588201)the National Natural Science Foundation of China(Grant No.12402321)+3 种基金the National Key R&D Program of China(Grant No.2021YFA0716201)the New Cornerstone Science Foundation through the New Cornerstone Investigator Program and the XPLORER PRIZEthe Postdoctoral Fellowship Program of the China Postdoctoral Science Foundation(Grant Nos.GZB20240366 and 2024M751637)Shuimu Tsinghua Scholar Program(Grant No.2023SM038).
文摘Icing of water droplets is a ubiquitous phenomenon with significant implications across natural systems and industrial applications.Despite extensive research,the intricate interplay among heat transfer,mass transport,and phase change during droplet freezing remains incompletely understood,particularly in the context of multiscale dynamics and environmental dependencies.This review critically examines recent advances in uncovering the fundamental mechanisms of droplet icing through experimental,theoretical,and computational approaches.We begin by revisiting the classical tip singularity problem in the freezing of pure water droplets,analyzing its mathematical formulation and physical significance.Subsequent sections explore how environmental boundary conditions and multicomponent effects influence freezing kinetics,solute redistribution,and ice morphology.Furthermore,we evaluate emerging hybrid numerical frameworks that resolve coupled multiphase physics during solidification processes.Finally,we identify key challenges and open questions that require further investigation in this field.
