Achieving continuous motions typically requires dynamic external stimuli for cyclic deformation,or crafted geometries with intricate modules to form a self-regulated feedback loop upon static stimulation.It is still a...Achieving continuous motions typically requires dynamic external stimuli for cyclic deformation,or crafted geometries with intricate modules to form a self-regulated feedback loop upon static stimulation.It is still a grand challenge to realize self-sustained motion in soft robots subject to unchanging environment,without complex geometry or a control module.In this work,we report soft robots based on an anisotropic cylindrical hydrogel showing self-regulated,continuous rolling motions under constant light irradiation.The robots are animated by mirror-symmetry-breaking induced by photothermal strain gradient.The self-sustained motion is attributed to the fast and reversible deformation of the gel and the autonomous refresh of the irradiated region during the rolling motion.The hydrogel robots can reach a rolling speed of 1.27 mm·s^(-1)on a horizonal surface and even climb a ramp of 18°at a speed of 0.57 mm·s^(-1)in an aqueous environment.Furthermore,the hydrogel robots can overcome an obstacle,with rolling direction controllable through irradiation angle of the light and local irradiation on selective regions.This work suggests a facile strategy to develop hydrogel robots and may provide unforeseen inspirations for the design of self-regulated soft robots by using other intelligent materials.展开更多
Human motion modeling is a core technology in computer animation,game development,and humancomputer interaction.In particular,generating natural and coherent in-between motion using only the initial and terminal frame...Human motion modeling is a core technology in computer animation,game development,and humancomputer interaction.In particular,generating natural and coherent in-between motion using only the initial and terminal frames remains a fundamental yet unresolved challenge.Existing methods typically rely on dense keyframe inputs or complex prior structures,making it difficult to balance motion quality and plausibility under conditions such as sparse constraints,long-term dependencies,and diverse motion styles.To address this,we propose a motion generation framework based on a frequency-domain diffusion model,which aims to better model complex motion distributions and enhance generation stability under sparse conditions.Our method maps motion sequences to the frequency domain via the Discrete Cosine Transform(DCT),enabling more effective modeling of low-frequency motion structures while suppressing high-frequency noise.A denoising network based on self-attention is introduced to capture long-range temporal dependencies and improve global structural awareness.Additionally,a multi-objective loss function is employed to jointly optimize motion smoothness,pose diversity,and anatomical consistency,enhancing the realism and physical plausibility of the generated sequences.Comparative experiments on the Human3.6M and LaFAN1 datasets demonstrate that our method outperforms state-of-the-art approaches across multiple performance metrics,showing stronger capabilities in generating intermediate motion frames.This research offers a new perspective and methodology for human motion generation and holds promise for applications in character animation,game development,and virtual interaction.展开更多
Wearable sensors integrated with deep learning techniques have the potential to revolutionize seamless human-machine interfaces for real-time health monitoring,clinical diagnosis,and robotic applications.Nevertheless,...Wearable sensors integrated with deep learning techniques have the potential to revolutionize seamless human-machine interfaces for real-time health monitoring,clinical diagnosis,and robotic applications.Nevertheless,it remains a critical challenge to simultaneously achieve desirable mechanical and electrical performance along with biocompatibility,adhesion,self-healing,and environmental robustness with excellent sensing metrics.Herein,we report a multifunctional,anti-freezing,selfadhesive,and self-healable organogel pressure sensor composed of cobalt nanoparticle encapsulated nitrogen-doped carbon nanotubes(CoN CNT)embedded in a polyvinyl alcohol-gelatin(PVA/GLE)matrix.Fabricated using a binary solvent system of water and ethylene glycol(EG),the CoN CNT/PVA/GLE organogel exhibits excellent flexibility,biocompatibility,and temperature tolerance with remarkable environmental stability.Electrochemical impedance spectroscopy confirms near-stable performance across a broad humidity range(40%-95%RH).Freeze-tolerant conductivity under sub-zero conditions(-20℃)is attributed to the synergistic role of CoN CNT and EG,preserving mobility and network integrity.The Co N CNT/PVA/GLE organogel sensor exhibits high sensitivity of 5.75 k Pa^(-1)in the detection range from 0 to 20 k Pa,ideal for subtle biomechanical motion detection.A smart human-machine interface for English letter recognition using deep learning achieved 98%accuracy.The organogel sensor utility was extended to detect human gestures like finger bending,wrist motion,and throat vibration during speech.展开更多
Background Computed tomography(CT) and cone-beam computed tomography(CBCT) image registration play pivotal roles in computer-assisted navigation for orthopedic surgery. Traditional methods often apply uniform deformat...Background Computed tomography(CT) and cone-beam computed tomography(CBCT) image registration play pivotal roles in computer-assisted navigation for orthopedic surgery. Traditional methods often apply uniform deformation models, neglecting the biomechanical differences between rigid structures and soft tissues, which compromises registration accuracy, especially during significant bone displacements. Method To address this issue, we introduce RE-Reg, a rigid-elastic CT-CBCT image registration framework that jointly learns rigid bone motion and soft tissue deformation. RE-Reg incorporates a rigid alignment(RA) module to estimate global bone motion and an elastic deformation(ED) module to model soft tissue deformation, preserving bony structures through bone shape preservation(BSP) loss. Result Our comprehensive evaluation on publicly available datasets demonstrates that RE-Reg significantly outperforms existing methods in terms of registration accuracy and rigid bone structure preservation, achieving a 1.3% improvement in Dice similarity coefficient(DSC) and a 23% reduction in rigid bone deformation(%Δvol) compared with the best baseline. Conclusion This framework not only enhances anatomical fidelity but also ensures biomechanical plausibility and provides a valuable tool for image-guided orthopedic surgery. This code is available athttps://github.com/Zq-Huang/RE-Reg.展开更多
In order to optimize the reaming process of the type IV composite hydrogen storage cylinder,the netting theory was employed for the design of stacking sequences,and the thickness in the head section was predicted.A fi...In order to optimize the reaming process of the type IV composite hydrogen storage cylinder,the netting theory was employed for the design of stacking sequences,and the thickness in the head section was predicted.A finite element model of the plastic-lined composite hydrogen storage cylinder,designed to withstand a working pressure of 70.0 MPa,was established by using the wound composite modeler(WCM)in the Abaqus software to analyze the forces acting on the winding layer.The Hashin failure criterion was utilized as the standard for assessing composite failure,and a progressive failure analysis of the cylinder was conducted to predict both the bursting pressure and the failure location of the composite hydrogen storage cylinder.The results indicate that the reaming process can effectively reduce the maximum filament winding thickness in the head section and promote a more uniform transition.At the bursting pressure,the stress within the head liner decreases,thereby enhancing the ultimate bearing capacity of the cylinder.A control system for a four-axis winding machine was designed by utilizing an industrial computer and a programmable multi-axis controller(PMAC).The winding line pattern is designed and the G-code trajectory is generated by the industrial computer.The numerical control system,composed of the PMAC and servo motor,executes the four-axis interpolation motion.展开更多
Next-generation fire safety systems demand precise detection and motion recognition of flames.In-sensor computing,which integrates sensing,memory,and processing capabilities,has emerged as a key technology in flame de...Next-generation fire safety systems demand precise detection and motion recognition of flames.In-sensor computing,which integrates sensing,memory,and processing capabilities,has emerged as a key technology in flame detection.However,the implementation of hardware-level functional demonstrations based on artificial vision systems in the solar-blind ultraviolet(UV)band(200-280 nm)is hindered by the weak detection capability.Here,we propose Ga_(2)O_(3)/In_(2)Se_(3) heterojunctions for the ferroelectric(abbreviation:Fe)optoelectronic sensor(abbreviation:OES)array(5×5 pixels),which is capable of ultraweak UV light detection with an ultrahigh detectivity through ferroelectric regulation and features in configurable multimode functionality.The Fe-OES array can directly sense different flame motions and simulate the non-spiking gradient neurons of insect visual system.Moreover,the flame signal can be effectively amplified in combination with leaky integration-and-fire neuron hardware.Using this Fe-OES system and neuromorphic hardware,we successfully demonstrate three flame processing tasks:achieving efficient flame detection across all time periods with terminal and cloud-based alarms;flame motion recognition with a lightweight convolutional neural network achieving 96.47%accuracy;and flame light recognition with 90.51%accuracy by means of a photosensitive artificial neural system.This work provides effective tools and approaches for addressing a variety of complex flame detection tasks.展开更多
Soft-tissue motion introduces significant challenges in robotic teleoperation,especially in medical scenarios where precise target tracking is critical.Latency across sensing,computation,and actuation chains leads to ...Soft-tissue motion introduces significant challenges in robotic teleoperation,especially in medical scenarios where precise target tracking is critical.Latency across sensing,computation,and actuation chains leads to degraded tracking performance,particularly around high-acceleration segments and trajectory inflection points.This study investigates machine learning-based predictive compensation for latency mitigation in soft-tissue tracking.Three models—autoregressive(AR),long short-term memory(LSTM),and temporal convolutional network(TCN)—were implemented and evaluated on both synthetic and real datasets.By aligning the prediction horizon with the end-to-end system delay,we demonstrate that prediction-based compensation significantly reduces tracking errors.Among the models,TCN achieved superior robustness and accuracy on complex motion patterns,particularly in multi-step prediction tasks,and exhibited better latency–horizon compatibility.The results suggest that TCN is a promising candidate for real-time latency compensation in teleoperated robotic systems involving dynamic soft-tissue interaction.展开更多
[Objective]This study aims to investigate the multi-body hydrodynamic interaction mechanisms during offshore lifting operations of aquaculture net cages in wind-fishery integration systems.By integrating numerical sim...[Objective]This study aims to investigate the multi-body hydrodynamic interaction mechanisms during offshore lifting operations of aquaculture net cages in wind-fishery integration systems.By integrating numerical simulations and dynamic analysis methods,this study systematically investigates the coupled dynamic response characteristics during the cage-carrier vessel separation process to reveal its dynamic evolution patterns and key influence mechanisms.[Method]Based on potential flow theory,a fully coupled dynamic analysis model of crane vessel-net cage-semi-submersible barge was established for a marine ranch project in Guangdong.The complete lifting process was dynamically simulated using SESAM software.Five typical operating sea states were configured to investigate the influence of wave parameters on the system's motion response under combined wave-current-wind actions.[Result]The results demonstrate that wave period dominates the system stability.Under short-period conditions,the system maintains stable motion with relatively small horizontal relative displacements,while long-period conditions excite low-frequency resonance,leading to significant slow-drift motions.Vertical response analysis reveals that long-period waves cause severe relative displacement fluctuations between the cage and semi-submersible vessel,with actual displacement amplitudes doubling the preset safety target of 2.045 m.Quantitative analysis further indicates that when significant wave height increases from 1.0 m to 1.5 m,the actual displacement amplitude increases by approximately 20%relative to the target displacement of 2.045 m,demonstrating that its influence is significantly weaker than the displacement variations induced by wave period changes.The complete dynamic simulation successfully captures the continuous dynamic response characteristics during the lifting process.[Conclusion]This research clarifies the influence mechanisms of wave parameters on the cage lifting process,identifying wave period as the crucial factor for operational safety.An operation window assessment method incorporating multi-body coupling effects is established,proposing a safety criterion with peak period not exceeding six seconds as the core requirement.The findings provide theoretical foundation for safe installation of marine ranch net cages and offer valuable references for similar offshore lifting operations.展开更多
In Human–Robot Interaction(HRI),generating robot trajectories that accurately reflect user intentions while ensuring physical realism remains challenging,especially in unstructured environments.In this study,we devel...In Human–Robot Interaction(HRI),generating robot trajectories that accurately reflect user intentions while ensuring physical realism remains challenging,especially in unstructured environments.In this study,we develop a multimodal framework that integrates symbolic task reasoning with continuous trajectory generation.The approach employs transformer models and adversarial training to map high-level intent to robotic motion.Information from multiple data sources,such as voice traits,hand and body keypoints,visual observations,and recorded paths,is integrated simultaneously.These signals are mapped into a shared representation that supports interpretable reasoning while enabling smooth and realistic motion generation.Based on this design,two different learning strategies are investigated.In the first step,grammar-constrained Linear Temporal Logic(LTL)expressions are created from multimodal human inputs.These expressions are subsequently decoded into robot trajectories.The second method generates trajectories directly from symbolic intent and linguistic data,bypassing an intermediate logical representation.Transformer encoders combine multiple types of information,and autoregressive transformer decoders generate motion sequences.Adding smoothness and speed limits during training increases the likelihood of physical feasibility.To improve the realism and stability of the generated trajectories during training,an adversarial discriminator is also included to guide them toward the distribution of actual robot motion.Tests on the NATSGLD dataset indicate that the complete system exhibits stable training behaviour and performance.In normalised coordinates,the logic-based pipeline has an Average Displacement Error(ADE)of 0.040 and a Final Displacement Error(FDE)of 0.036.The adversarial generator makes substantially more progress,reducing ADE to 0.021 and FDE to 0.018.Visual examination confirms that the generated trajectories closely align with observed motion patterns while preserving smooth temporal dynamics.展开更多
The widely distributed loess deposits in the Yellow River Basin exhibit unique engineering geological characteristics.The variations in their thickness and stratigraphic structure significantly amplify ground motion p...The widely distributed loess deposits in the Yellow River Basin exhibit unique engineering geological characteristics.The variations in their thickness and stratigraphic structure significantly amplify ground motion parameters,directly influencing the regional seismic hazard risk level.This study methodically conducted on-site studies and observations of building collapses and damages resulting from seismic amplification effects,using the Wenchuan M_(S)8.0 earthquake as a case study.Comprehensive experimental and numerical simulation studies were carried out.A large-scale shaking table test was performed,and numerical models for 14 different loess sites types were established.Various types of seismic waves were incorporated into these models for systematic numerical simulation calculations.The research reveals the mechanisms by which loess deposit thickness and stratigraphic structure in the Yellow River Basin affect seismic ground motion amplification.The results indicate that as the epicentral distance increases,the peak ground motion shows a marked attenuation trend,with the horizontal component attenuating substantially faster than the vertical component.As the overlying loess layer thickness increases from 50 to 100 m,the seismic intensity may escalate by 3−4 degrees,and the peak acceleration may amplify by 1.5−2.2 times.With the augmentation of loess deposit thickness and the proliferation of soil layers,both the peak acceleration response spectrum and the characteristic period demonstrate an upward tendency,exhibiting slight fluctuations contingent upon the seismic wave type.展开更多
The research findings on the ground motion and liquefaction potential analyses during the 2018 Great Indonesia Earthquake(M_(w)7.5)are significant and crucial.The earthquake triggered soil-structure damage due to liqu...The research findings on the ground motion and liquefaction potential analyses during the 2018 Great Indonesia Earthquake(M_(w)7.5)are significant and crucial.The earthquake triggered soil-structure damage due to liquefaction.This study,which thoroughly investigated four sites at Palu,was conducted by performing a comprehensive ground motion parameter analysis.The ground motion characteristics were presented and justified,particularly for the most impacted direction.Ground motion predictions were analysed to define the spectral accelerations,and matching spectral accelerations were conducted to produce ground motions for each site.Non-linear seismic ground response analysis based on the hyperbolic model of pressure pressure-dependent was performed to investigate cyclic soil behaviour.The results revealed that ground motion is crucial in significant soil damage,and the earthquake energy could trigger deep liquefaction.As the most significant ground motion,the vertical ground motion is essential in determining deep liquefaction.The discussion on the impact of liquefaction based on the results of the numerical analysis is presented.Significant ground motion with a longer duration could have a substantial impact on deep liquefaction in the study area.These findings depict how the 2018 Indonesia Earthquake(M_(w)7.5)triggered a mega-liquefaction in Palu City.The results could enhance the understanding of the importance of seismic hazard assessment.It is recommended that site investigation and soil improvement should be planned to counteract liquefaction damage before construction.This study also suggests conducting seismic hazard assessments for city development to minimise the potential disaster impact in the study area.展开更多
Deep neural networks have achieved excellent classification results on several computer vision benchmarks.This has led to the popularity of machine learning as a service,where trained algorithms are hosted on the clou...Deep neural networks have achieved excellent classification results on several computer vision benchmarks.This has led to the popularity of machine learning as a service,where trained algorithms are hosted on the cloud and inference can be obtained on real-world data.In most applications,it is important to compress the vision data due to the enormous bandwidth and memory requirements.Video codecs exploit spatial and temporal correlations to achieve high compression ratios,but they are computationally expensive.This work computes the motion fields between consecutive frames to facilitate the efficient classification of videos.However,contrary to the normal practice of reconstructing the full-resolution frames through motion compensation,this work proposes to infer the class label from the block-based computed motion fields directly.Motion fields are a richer and more complex representation of motion vectors,where each motion vector carries the magnitude and direction information.This approach has two advantages:the cost of motion compensation and video decoding is avoided,and the dimensions of the input signal are highly reduced.This results in a shallower network for classification.The neural network can be trained using motion vectors in two ways:complex representations and magnitude-direction pairs.The proposed work trains a convolutional neural network on the direction and magnitude tensors of the motion fields.Our experimental results show 20×faster convergence during training,reduced overfitting,and accelerated inference on a hand gesture recognition dataset compared to full-resolution and downsampled frames.We validate the proposed methodology on the HGds dataset,achieving a testing accuracy of 99.21%,on the HMDB51 dataset,achieving 82.54%accuracy,and on the UCF101 dataset,achieving 97.13%accuracy,outperforming state-of-the-art methods in computational efficiency.展开更多
The soft actuator is characterized by high safety,flexibility,and adaptability.It is capable of both active and passive defor-mations.This paper presents a discrete degree of freedom(DOF)method for soft actuators to r...The soft actuator is characterized by high safety,flexibility,and adaptability.It is capable of both active and passive defor-mations.This paper presents a discrete degree of freedom(DOF)method for soft actuators to reveal DOF characteristics.The method draws on the superposition mechanism of the deformation characteristics of the sarcomere in the skeletal muscles of living organisms.Firstly,the multi-DOF deformation characteristics of the soft actuator are discretized into superimposed combinations of single-DOF micro-units.Then,the soft actuator was determined to contain deformation characteristics such as extension-contraction,bending,and twisting.Eighteen types of micro-units with basic deforma-tion characteristics were obtained depending on the axis and orientation.Further,the mapping relationship between the combination of micro-units and the motion characteristics of the soft actuator based on the GF set theory was established.Finally,an active-passive DOF co-structured soft actuator(APCSA)was developed.The graphical approach analyzes the experimental results,and it can be concluded that active and passive DOFs can coexist in the composite deformation of the soft actuator.展开更多
This study presents an effective hybrid simulation approach for simulating broadband ground motion in complex near-fault locations.The approach utilizes a deterministic approach based on the spectral element method(SE...This study presents an effective hybrid simulation approach for simulating broadband ground motion in complex near-fault locations.The approach utilizes a deterministic approach based on the spectral element method(SEM),which is used to simulate low-frequency ground motion(f<1 Hz)by incorporating an innovative efficient discontinuous Galerkin(DG)method for grid division to accurately model basin sedimentary layers at reduced costs.It also introduces a comprehensive hybrid source model for high-frequency random scattering and a nonlinear analysis module for basin sedimentary layers.Deterministic outcomes are combined with modified three-dimensional stochastic finite fault method(3D-EXSIM)simulations of high-frequency ground motion(f>1 Hz).A fourth-order Butterworth filter with zero phase shift is employed for time-domain filtering of low-and high-frequency time series at a crossover frequency of 1 Hz,merging the low and high-frequency ground motions into a broadband time series.Taking an Ms 6.8 Luding earthquake,as an example,this hybrid method was used for a rapid and efficient simulation analysis of broadband ground motion in the region.The accuracy and efficiency of this hybrid method were verified through comparisons with actually observed station data and empirical attenuation curves.Deterministic method simulation results revealed the effects of mountainous topography,basin effects,nonlinear effects within the basin’s sedimentary layers,and a coupling interaction between the basin and the mountains.The findings are consistent with similar studies,showing that near-fault sedimentary basins significantly focus and amplify strong ground motion,and the soil’s nonlinear behavior in the basin influences ground motion to varying extents at different distances from the fault.The mountainous topography impacts the basin’s response to ground motion,leading to barrier effects.This research provides a scientific foundation for seismic zoning,urban planning,and seismic design in nearfault mountain basin regions.展开更多
Currently, many studies have been made for years on dimensions of pneumatic nozzle, which influence the flow characteristic of blowing system. For the purpose of outputting the same blowing force, the supply pressure ...Currently, many studies have been made for years on dimensions of pneumatic nozzle, which influence the flow characteristic of blowing system. For the purpose of outputting the same blowing force, the supply pressure could be reduced by decreasing the ratio of length to diameter of nozzle. The friction between high speed air and pipe wall would be reduced if the nozzle is designed to be converging shape comparing with straight shape. But the volume flow and pressure, discussed in these studies, do not describe energy loss of the blowing system directly. Pneumatic power is an innovative principle to estimate pneumatic system’s energy consumption directly. Based on the above principle, a pulse blowing method is put forward for saving energy. A flow experiment is carried out, in which the high speed air flows from the pulse blowing system and continuous blowing system respectively to a plate with grease on top. Supply pressure and the volume of air used for removing the grease are measured to calculate energy consumption. From the experiment result, the pulse blowing system performs to conserve energy comparing with the continuous blowing system. The frequency and duty ratio of pulse flow influence the blowing characteristic. The pulse blowing system performs to be the most efficient at the specified frequency and duty ratio. Then a pneumatic self-oscillated method based on air operated valve is put forward to generate pulse flow. A simulation is made about dynamic modeling the air operated valve and calculating the motion of the valve core and output pressure. The simulation result verifies the system to be able to generate pulse flow, and predicts the key parameters of the frequency and duty ratio measured by experiment well. Finally, on the basis of simplifying and solution of the pulse blowing system’s mathematic model, the relationship between system’s frequency duty ratio and the dimensions of components is simply described with four algebraic equations. The system could be designed with specified frequency and duty ratio according to the four equations. This study provides theoretical basis for designing energy-saving air blowing system.展开更多
In this paper we investigate spatiotemporal pattern formation in excitable media with only a long-range link. Besides the trivial solutions of spiral patterns, we find the asymptotic self-sustained target waves in the...In this paper we investigate spatiotemporal pattern formation in excitable media with only a long-range link. Besides the trivial solutions of spiral patterns, we find the asymptotic self-sustained target waves in the autonomous tissues. The wave source supporting this kind of new pattern is the oscillatory one-dimensional Winfree-loop self- organized under the presence of a long-range link, which is explored by the dominant phase-advanced driving method. Based on this understanding we can effectively regulate the oscillations of excitable media by suitably arranging the long-range link, including construction of self-sustained target waves with controllable period and wave length, or manipulation of system states between different patterns.展开更多
We generalize a simple model for superlattices to include the effect of differential capacitance. It is shown that the model always has a stable steady-state solution （SSS） if all differential capacitances are posit...We generalize a simple model for superlattices to include the effect of differential capacitance. It is shown that the model always has a stable steady-state solution （SSS） if all differential capacitances are positive. On the other hand, when negative differential capacitance is included, the model can have no stable SSS and be in a self-sustained current oscillation behavior. Therefore, we find a possible minimum toy model with both negative differential resistance and negative differential capacitance which can include the phenomena of both self-sustained current oscillation and I-V oscillation of stable SSSs.展开更多
Oscillatory behaviors can be ubiquitously observed in various systems. Biological rhythms are significant in governing living activities of all units. The emergence of biological rhythms is the consequence of large nu...Oscillatory behaviors can be ubiquitously observed in various systems. Biological rhythms are significant in governing living activities of all units. The emergence of biological rhythms is the consequence of large numbers of units. In this paper we discuss several important examples of sustained oscillations in biological media, where the unit composed in the system does not possess the oscillation behavior. The dominant phase-advanced driving method is applied to study the skeletons and oscillatory organizing motifs in excitable networks and gene regulatory networks.展开更多
In this paper,we present local functional law of the iterated logarithm for Cs?rg?-Révész type increments of fractional Brownian motion.The results obtained extend works of Gantert[Ann.Probab.,1993,21(2):104...In this paper,we present local functional law of the iterated logarithm for Cs?rg?-Révész type increments of fractional Brownian motion.The results obtained extend works of Gantert[Ann.Probab.,1993,21(2):1045-1049]and Monrad and Rootzén[Probab.Theory Related Fields,1995,101(2):173-192].展开更多
Numerical simulations are used to investigate the self-sustained oscillating flows past an open cavity. The two-dimensional incompressible Navier-Stokes equations are solved directly by using the finite difference met...Numerical simulations are used to investigate the self-sustained oscillating flows past an open cavity. The two-dimensional incompressible Navier-Stokes equations are solved directly by using the finite difference method for cavities with an upstream laminar boundary layer. A series of simulations are performed for a variety of cavity length-to-depth ratio. The results show the switching among some flow modes including non-oscillation mode, shear layer mode and wake mode. The variation of the Strouhal number is in favorable agreement with available experimental data. The results of flow fields in the cavity reveal the relationship between the cavity shear layer oscillation modes and recirculating vortices in the cavity.展开更多
基金supported by the National Natural Science Foundation of China(Nos.52325302 and 52173012)Shanxi-Zheda Institute of Advanced Materials and Chemical Engineering(No.2022SZ-FR005)。
文摘Achieving continuous motions typically requires dynamic external stimuli for cyclic deformation,or crafted geometries with intricate modules to form a self-regulated feedback loop upon static stimulation.It is still a grand challenge to realize self-sustained motion in soft robots subject to unchanging environment,without complex geometry or a control module.In this work,we report soft robots based on an anisotropic cylindrical hydrogel showing self-regulated,continuous rolling motions under constant light irradiation.The robots are animated by mirror-symmetry-breaking induced by photothermal strain gradient.The self-sustained motion is attributed to the fast and reversible deformation of the gel and the autonomous refresh of the irradiated region during the rolling motion.The hydrogel robots can reach a rolling speed of 1.27 mm·s^(-1)on a horizonal surface and even climb a ramp of 18°at a speed of 0.57 mm·s^(-1)in an aqueous environment.Furthermore,the hydrogel robots can overcome an obstacle,with rolling direction controllable through irradiation angle of the light and local irradiation on selective regions.This work suggests a facile strategy to develop hydrogel robots and may provide unforeseen inspirations for the design of self-regulated soft robots by using other intelligent materials.
基金supported by the National Natural Science Foundation of China(Grant No.72161034).
文摘Human motion modeling is a core technology in computer animation,game development,and humancomputer interaction.In particular,generating natural and coherent in-between motion using only the initial and terminal frames remains a fundamental yet unresolved challenge.Existing methods typically rely on dense keyframe inputs or complex prior structures,making it difficult to balance motion quality and plausibility under conditions such as sparse constraints,long-term dependencies,and diverse motion styles.To address this,we propose a motion generation framework based on a frequency-domain diffusion model,which aims to better model complex motion distributions and enhance generation stability under sparse conditions.Our method maps motion sequences to the frequency domain via the Discrete Cosine Transform(DCT),enabling more effective modeling of low-frequency motion structures while suppressing high-frequency noise.A denoising network based on self-attention is introduced to capture long-range temporal dependencies and improve global structural awareness.Additionally,a multi-objective loss function is employed to jointly optimize motion smoothness,pose diversity,and anatomical consistency,enhancing the realism and physical plausibility of the generated sequences.Comparative experiments on the Human3.6M and LaFAN1 datasets demonstrate that our method outperforms state-of-the-art approaches across multiple performance metrics,showing stronger capabilities in generating intermediate motion frames.This research offers a new perspective and methodology for human motion generation and holds promise for applications in character animation,game development,and virtual interaction.
基金supported by the Basic Science Research Program(2023R1A2C3004336,RS-202300243807)&Regional Leading Research Center(RS-202400405278)through the National Research Foundation of Korea(NRF)grant funded by the Korea Government(MSIT)。
文摘Wearable sensors integrated with deep learning techniques have the potential to revolutionize seamless human-machine interfaces for real-time health monitoring,clinical diagnosis,and robotic applications.Nevertheless,it remains a critical challenge to simultaneously achieve desirable mechanical and electrical performance along with biocompatibility,adhesion,self-healing,and environmental robustness with excellent sensing metrics.Herein,we report a multifunctional,anti-freezing,selfadhesive,and self-healable organogel pressure sensor composed of cobalt nanoparticle encapsulated nitrogen-doped carbon nanotubes(CoN CNT)embedded in a polyvinyl alcohol-gelatin(PVA/GLE)matrix.Fabricated using a binary solvent system of water and ethylene glycol(EG),the CoN CNT/PVA/GLE organogel exhibits excellent flexibility,biocompatibility,and temperature tolerance with remarkable environmental stability.Electrochemical impedance spectroscopy confirms near-stable performance across a broad humidity range(40%-95%RH).Freeze-tolerant conductivity under sub-zero conditions(-20℃)is attributed to the synergistic role of CoN CNT and EG,preserving mobility and network integrity.The Co N CNT/PVA/GLE organogel sensor exhibits high sensitivity of 5.75 k Pa^(-1)in the detection range from 0 to 20 k Pa,ideal for subtle biomechanical motion detection.A smart human-machine interface for English letter recognition using deep learning achieved 98%accuracy.The organogel sensor utility was extended to detect human gestures like finger bending,wrist motion,and throat vibration during speech.
基金Supported by the National Natural Science Foundation of China(Grant Nos.62025104,62331005,and U22A2052)the Beijing Natural Science Foundation(Grant No.L242100).
文摘Background Computed tomography(CT) and cone-beam computed tomography(CBCT) image registration play pivotal roles in computer-assisted navigation for orthopedic surgery. Traditional methods often apply uniform deformation models, neglecting the biomechanical differences between rigid structures and soft tissues, which compromises registration accuracy, especially during significant bone displacements. Method To address this issue, we introduce RE-Reg, a rigid-elastic CT-CBCT image registration framework that jointly learns rigid bone motion and soft tissue deformation. RE-Reg incorporates a rigid alignment(RA) module to estimate global bone motion and an elastic deformation(ED) module to model soft tissue deformation, preserving bony structures through bone shape preservation(BSP) loss. Result Our comprehensive evaluation on publicly available datasets demonstrates that RE-Reg significantly outperforms existing methods in terms of registration accuracy and rigid bone structure preservation, achieving a 1.3% improvement in Dice similarity coefficient(DSC) and a 23% reduction in rigid bone deformation(%Δvol) compared with the best baseline. Conclusion This framework not only enhances anatomical fidelity but also ensures biomechanical plausibility and provides a valuable tool for image-guided orthopedic surgery. This code is available athttps://github.com/Zq-Huang/RE-Reg.
文摘In order to optimize the reaming process of the type IV composite hydrogen storage cylinder,the netting theory was employed for the design of stacking sequences,and the thickness in the head section was predicted.A finite element model of the plastic-lined composite hydrogen storage cylinder,designed to withstand a working pressure of 70.0 MPa,was established by using the wound composite modeler(WCM)in the Abaqus software to analyze the forces acting on the winding layer.The Hashin failure criterion was utilized as the standard for assessing composite failure,and a progressive failure analysis of the cylinder was conducted to predict both the bursting pressure and the failure location of the composite hydrogen storage cylinder.The results indicate that the reaming process can effectively reduce the maximum filament winding thickness in the head section and promote a more uniform transition.At the bursting pressure,the stress within the head liner decreases,thereby enhancing the ultimate bearing capacity of the cylinder.A control system for a four-axis winding machine was designed by utilizing an industrial computer and a programmable multi-axis controller(PMAC).The winding line pattern is designed and the G-code trajectory is generated by the industrial computer.The numerical control system,composed of the PMAC and servo motor,executes the four-axis interpolation motion.
基金supported by the Major Program(JD)of Hubei Province under Grant No.2023BAA009the National Natural Science Foundation of China(Grant No.22105162)+1 种基金the Natural Science Foundation of Hubei Province(Grant No.2023AFB623)the Original Exploration Seed Fund of Hubei University。
文摘Next-generation fire safety systems demand precise detection and motion recognition of flames.In-sensor computing,which integrates sensing,memory,and processing capabilities,has emerged as a key technology in flame detection.However,the implementation of hardware-level functional demonstrations based on artificial vision systems in the solar-blind ultraviolet(UV)band(200-280 nm)is hindered by the weak detection capability.Here,we propose Ga_(2)O_(3)/In_(2)Se_(3) heterojunctions for the ferroelectric(abbreviation:Fe)optoelectronic sensor(abbreviation:OES)array(5×5 pixels),which is capable of ultraweak UV light detection with an ultrahigh detectivity through ferroelectric regulation and features in configurable multimode functionality.The Fe-OES array can directly sense different flame motions and simulate the non-spiking gradient neurons of insect visual system.Moreover,the flame signal can be effectively amplified in combination with leaky integration-and-fire neuron hardware.Using this Fe-OES system and neuromorphic hardware,we successfully demonstrate three flame processing tasks:achieving efficient flame detection across all time periods with terminal and cloud-based alarms;flame motion recognition with a lightweight convolutional neural network achieving 96.47%accuracy;and flame light recognition with 90.51%accuracy by means of a photosensitive artificial neural system.This work provides effective tools and approaches for addressing a variety of complex flame detection tasks.
基金Support by Sichuan Science and Technology Program[2023YFSY0026,2023YFH0004]Guangzhou Huashang University[2024HSZD01,HS2023JYSZH01].
文摘Soft-tissue motion introduces significant challenges in robotic teleoperation,especially in medical scenarios where precise target tracking is critical.Latency across sensing,computation,and actuation chains leads to degraded tracking performance,particularly around high-acceleration segments and trajectory inflection points.This study investigates machine learning-based predictive compensation for latency mitigation in soft-tissue tracking.Three models—autoregressive(AR),long short-term memory(LSTM),and temporal convolutional network(TCN)—were implemented and evaluated on both synthetic and real datasets.By aligning the prediction horizon with the end-to-end system delay,we demonstrate that prediction-based compensation significantly reduces tracking errors.Among the models,TCN achieved superior robustness and accuracy on complex motion patterns,particularly in multi-step prediction tasks,and exhibited better latency–horizon compatibility.The results suggest that TCN is a promising candidate for real-time latency compensation in teleoperated robotic systems involving dynamic soft-tissue interaction.
文摘[Objective]This study aims to investigate the multi-body hydrodynamic interaction mechanisms during offshore lifting operations of aquaculture net cages in wind-fishery integration systems.By integrating numerical simulations and dynamic analysis methods,this study systematically investigates the coupled dynamic response characteristics during the cage-carrier vessel separation process to reveal its dynamic evolution patterns and key influence mechanisms.[Method]Based on potential flow theory,a fully coupled dynamic analysis model of crane vessel-net cage-semi-submersible barge was established for a marine ranch project in Guangdong.The complete lifting process was dynamically simulated using SESAM software.Five typical operating sea states were configured to investigate the influence of wave parameters on the system's motion response under combined wave-current-wind actions.[Result]The results demonstrate that wave period dominates the system stability.Under short-period conditions,the system maintains stable motion with relatively small horizontal relative displacements,while long-period conditions excite low-frequency resonance,leading to significant slow-drift motions.Vertical response analysis reveals that long-period waves cause severe relative displacement fluctuations between the cage and semi-submersible vessel,with actual displacement amplitudes doubling the preset safety target of 2.045 m.Quantitative analysis further indicates that when significant wave height increases from 1.0 m to 1.5 m,the actual displacement amplitude increases by approximately 20%relative to the target displacement of 2.045 m,demonstrating that its influence is significantly weaker than the displacement variations induced by wave period changes.The complete dynamic simulation successfully captures the continuous dynamic response characteristics during the lifting process.[Conclusion]This research clarifies the influence mechanisms of wave parameters on the cage lifting process,identifying wave period as the crucial factor for operational safety.An operation window assessment method incorporating multi-body coupling effects is established,proposing a safety criterion with peak period not exceeding six seconds as the core requirement.The findings provide theoretical foundation for safe installation of marine ranch net cages and offer valuable references for similar offshore lifting operations.
基金The authors extend their appreciation to Prince Sattam bin Abdulaziz University for funding this research work through the project number(PSAU/2024/01/32082).
文摘In Human–Robot Interaction(HRI),generating robot trajectories that accurately reflect user intentions while ensuring physical realism remains challenging,especially in unstructured environments.In this study,we develop a multimodal framework that integrates symbolic task reasoning with continuous trajectory generation.The approach employs transformer models and adversarial training to map high-level intent to robotic motion.Information from multiple data sources,such as voice traits,hand and body keypoints,visual observations,and recorded paths,is integrated simultaneously.These signals are mapped into a shared representation that supports interpretable reasoning while enabling smooth and realistic motion generation.Based on this design,two different learning strategies are investigated.In the first step,grammar-constrained Linear Temporal Logic(LTL)expressions are created from multimodal human inputs.These expressions are subsequently decoded into robot trajectories.The second method generates trajectories directly from symbolic intent and linguistic data,bypassing an intermediate logical representation.Transformer encoders combine multiple types of information,and autoregressive transformer decoders generate motion sequences.Adding smoothness and speed limits during training increases the likelihood of physical feasibility.To improve the realism and stability of the generated trajectories during training,an adversarial discriminator is also included to guide them toward the distribution of actual robot motion.Tests on the NATSGLD dataset indicate that the complete system exhibits stable training behaviour and performance.In normalised coordinates,the logic-based pipeline has an Average Displacement Error(ADE)of 0.040 and a Final Displacement Error(FDE)of 0.036.The adversarial generator makes substantially more progress,reducing ADE to 0.021 and FDE to 0.018.Visual examination confirms that the generated trajectories closely align with observed motion patterns while preserving smooth temporal dynamics.
基金supported by the Earthquake Science and Technology Spark Plan Project(No.XH23041C)The Natural Science Foundation of Gansu Province(No.22JR11RA090)Gansu Lanzhou Geophysics National Observation and Research Station(No.2021Y14).
文摘The widely distributed loess deposits in the Yellow River Basin exhibit unique engineering geological characteristics.The variations in their thickness and stratigraphic structure significantly amplify ground motion parameters,directly influencing the regional seismic hazard risk level.This study methodically conducted on-site studies and observations of building collapses and damages resulting from seismic amplification effects,using the Wenchuan M_(S)8.0 earthquake as a case study.Comprehensive experimental and numerical simulation studies were carried out.A large-scale shaking table test was performed,and numerical models for 14 different loess sites types were established.Various types of seismic waves were incorporated into these models for systematic numerical simulation calculations.The research reveals the mechanisms by which loess deposit thickness and stratigraphic structure in the Yellow River Basin affect seismic ground motion amplification.The results indicate that as the epicentral distance increases,the peak ground motion shows a marked attenuation trend,with the horizontal component attenuating substantially faster than the vertical component.As the overlying loess layer thickness increases from 50 to 100 m,the seismic intensity may escalate by 3−4 degrees,and the peak acceleration may amplify by 1.5−2.2 times.With the augmentation of loess deposit thickness and the proliferation of soil layers,both the peak acceleration response spectrum and the characteristic period demonstrate an upward tendency,exhibiting slight fluctuations contingent upon the seismic wave type.
基金The World Class Professor(WCP)Program of the Directorate of Resources,Directorate General of Higher Education,Ministry of Education and Culture in 2023 supports this studythe JAPAN-ASEAN Science and Technology Innovation Platform(JASTIP-WP4)+3 种基金the University of Bengkulu's International Collaboration Research Fund(2183/UN30.15/LT/2019)for partial fundingthe C2F Fund for Postdoctoral Fellowship from Chulalongkorn Universitythe Thailand Science Research and Innovation Fund Chulalongkorn University(DISF68210001)the National Research Council of Thailand(N42A670572)。
文摘The research findings on the ground motion and liquefaction potential analyses during the 2018 Great Indonesia Earthquake(M_(w)7.5)are significant and crucial.The earthquake triggered soil-structure damage due to liquefaction.This study,which thoroughly investigated four sites at Palu,was conducted by performing a comprehensive ground motion parameter analysis.The ground motion characteristics were presented and justified,particularly for the most impacted direction.Ground motion predictions were analysed to define the spectral accelerations,and matching spectral accelerations were conducted to produce ground motions for each site.Non-linear seismic ground response analysis based on the hyperbolic model of pressure pressure-dependent was performed to investigate cyclic soil behaviour.The results revealed that ground motion is crucial in significant soil damage,and the earthquake energy could trigger deep liquefaction.As the most significant ground motion,the vertical ground motion is essential in determining deep liquefaction.The discussion on the impact of liquefaction based on the results of the numerical analysis is presented.Significant ground motion with a longer duration could have a substantial impact on deep liquefaction in the study area.These findings depict how the 2018 Indonesia Earthquake(M_(w)7.5)triggered a mega-liquefaction in Palu City.The results could enhance the understanding of the importance of seismic hazard assessment.It is recommended that site investigation and soil improvement should be planned to counteract liquefaction damage before construction.This study also suggests conducting seismic hazard assessments for city development to minimise the potential disaster impact in the study area.
基金Supported by Princess Nourah bint Abdulrahman University Researchers Supporting Project number(PNURSP2025R896).
文摘Deep neural networks have achieved excellent classification results on several computer vision benchmarks.This has led to the popularity of machine learning as a service,where trained algorithms are hosted on the cloud and inference can be obtained on real-world data.In most applications,it is important to compress the vision data due to the enormous bandwidth and memory requirements.Video codecs exploit spatial and temporal correlations to achieve high compression ratios,but they are computationally expensive.This work computes the motion fields between consecutive frames to facilitate the efficient classification of videos.However,contrary to the normal practice of reconstructing the full-resolution frames through motion compensation,this work proposes to infer the class label from the block-based computed motion fields directly.Motion fields are a richer and more complex representation of motion vectors,where each motion vector carries the magnitude and direction information.This approach has two advantages:the cost of motion compensation and video decoding is avoided,and the dimensions of the input signal are highly reduced.This results in a shallower network for classification.The neural network can be trained using motion vectors in two ways:complex representations and magnitude-direction pairs.The proposed work trains a convolutional neural network on the direction and magnitude tensors of the motion fields.Our experimental results show 20×faster convergence during training,reduced overfitting,and accelerated inference on a hand gesture recognition dataset compared to full-resolution and downsampled frames.We validate the proposed methodology on the HGds dataset,achieving a testing accuracy of 99.21%,on the HMDB51 dataset,achieving 82.54%accuracy,and on the UCF101 dataset,achieving 97.13%accuracy,outperforming state-of-the-art methods in computational efficiency.
基金The Central Government Guides Local Foundation for Science and Technology Development(Grant No.YDZJSX2024B004).
文摘The soft actuator is characterized by high safety,flexibility,and adaptability.It is capable of both active and passive defor-mations.This paper presents a discrete degree of freedom(DOF)method for soft actuators to reveal DOF characteristics.The method draws on the superposition mechanism of the deformation characteristics of the sarcomere in the skeletal muscles of living organisms.Firstly,the multi-DOF deformation characteristics of the soft actuator are discretized into superimposed combinations of single-DOF micro-units.Then,the soft actuator was determined to contain deformation characteristics such as extension-contraction,bending,and twisting.Eighteen types of micro-units with basic deforma-tion characteristics were obtained depending on the axis and orientation.Further,the mapping relationship between the combination of micro-units and the motion characteristics of the soft actuator based on the GF set theory was established.Finally,an active-passive DOF co-structured soft actuator(APCSA)was developed.The graphical approach analyzes the experimental results,and it can be concluded that active and passive DOFs can coexist in the composite deformation of the soft actuator.
基金National Natural Science Foundation of China under Grant Nos.U2139208 and 52278516Key Laboratory of Earthquake Engineering and Engineering Vibration,China Earthquake Administration under Grant No.2024D15Key Laboratory of Soft Soil Characteristic and Engineering Environment,Tianjin Chengjian University under Grant No.2022SCEEKL003。
文摘This study presents an effective hybrid simulation approach for simulating broadband ground motion in complex near-fault locations.The approach utilizes a deterministic approach based on the spectral element method(SEM),which is used to simulate low-frequency ground motion(f<1 Hz)by incorporating an innovative efficient discontinuous Galerkin(DG)method for grid division to accurately model basin sedimentary layers at reduced costs.It also introduces a comprehensive hybrid source model for high-frequency random scattering and a nonlinear analysis module for basin sedimentary layers.Deterministic outcomes are combined with modified three-dimensional stochastic finite fault method(3D-EXSIM)simulations of high-frequency ground motion(f>1 Hz).A fourth-order Butterworth filter with zero phase shift is employed for time-domain filtering of low-and high-frequency time series at a crossover frequency of 1 Hz,merging the low and high-frequency ground motions into a broadband time series.Taking an Ms 6.8 Luding earthquake,as an example,this hybrid method was used for a rapid and efficient simulation analysis of broadband ground motion in the region.The accuracy and efficiency of this hybrid method were verified through comparisons with actually observed station data and empirical attenuation curves.Deterministic method simulation results revealed the effects of mountainous topography,basin effects,nonlinear effects within the basin’s sedimentary layers,and a coupling interaction between the basin and the mountains.The findings are consistent with similar studies,showing that near-fault sedimentary basins significantly focus and amplify strong ground motion,and the soil’s nonlinear behavior in the basin influences ground motion to varying extents at different distances from the fault.The mountainous topography impacts the basin’s response to ground motion,leading to barrier effects.This research provides a scientific foundation for seismic zoning,urban planning,and seismic design in nearfault mountain basin regions.
文摘Currently, many studies have been made for years on dimensions of pneumatic nozzle, which influence the flow characteristic of blowing system. For the purpose of outputting the same blowing force, the supply pressure could be reduced by decreasing the ratio of length to diameter of nozzle. The friction between high speed air and pipe wall would be reduced if the nozzle is designed to be converging shape comparing with straight shape. But the volume flow and pressure, discussed in these studies, do not describe energy loss of the blowing system directly. Pneumatic power is an innovative principle to estimate pneumatic system’s energy consumption directly. Based on the above principle, a pulse blowing method is put forward for saving energy. A flow experiment is carried out, in which the high speed air flows from the pulse blowing system and continuous blowing system respectively to a plate with grease on top. Supply pressure and the volume of air used for removing the grease are measured to calculate energy consumption. From the experiment result, the pulse blowing system performs to conserve energy comparing with the continuous blowing system. The frequency and duty ratio of pulse flow influence the blowing characteristic. The pulse blowing system performs to be the most efficient at the specified frequency and duty ratio. Then a pneumatic self-oscillated method based on air operated valve is put forward to generate pulse flow. A simulation is made about dynamic modeling the air operated valve and calculating the motion of the valve core and output pressure. The simulation result verifies the system to be able to generate pulse flow, and predicts the key parameters of the frequency and duty ratio measured by experiment well. Finally, on the basis of simplifying and solution of the pulse blowing system’s mathematic model, the relationship between system’s frequency duty ratio and the dimensions of components is simply described with four algebraic equations. The system could be designed with specified frequency and duty ratio according to the four equations. This study provides theoretical basis for designing energy-saving air blowing system.
基金supported by the National Natural Science Foundation of China (Grant No. 11047146)the Science Foundation of the Education Bureau of Shaanxi Province of China (Grant No. 11JK0544)+1 种基金the Natural Science Foundation of Shaanxi Province of China (Grant No. 2010JQ1014)the Science Foundation of Baoji University of Arts and Sciences (Grant Nos. ZK1048 andZK1049)
文摘In this paper we investigate spatiotemporal pattern formation in excitable media with only a long-range link. Besides the trivial solutions of spiral patterns, we find the asymptotic self-sustained target waves in the autonomous tissues. The wave source supporting this kind of new pattern is the oscillatory one-dimensional Winfree-loop self- organized under the presence of a long-range link, which is explored by the dominant phase-advanced driving method. Based on this understanding we can effectively regulate the oscillations of excitable media by suitably arranging the long-range link, including construction of self-sustained target waves with controllable period and wave length, or manipulation of system states between different patterns.
基金The project supported by National Natural Science Foundation of China under Grant No. 10347101 and the Grant from Beijing Normal University
文摘We generalize a simple model for superlattices to include the effect of differential capacitance. It is shown that the model always has a stable steady-state solution （SSS） if all differential capacitances are positive. On the other hand, when negative differential capacitance is included, the model can have no stable SSS and be in a self-sustained current oscillation behavior. Therefore, we find a possible minimum toy model with both negative differential resistance and negative differential capacitance which can include the phenomena of both self-sustained current oscillation and I-V oscillation of stable SSSs.
基金Project supported by the National Natural Science Foundation of China(Grant Nos.11475022 and 11675001)the Scientific Research Funds of Huaqiao University,China(Grant No.15BS401)
文摘Oscillatory behaviors can be ubiquitously observed in various systems. Biological rhythms are significant in governing living activities of all units. The emergence of biological rhythms is the consequence of large numbers of units. In this paper we discuss several important examples of sustained oscillations in biological media, where the unit composed in the system does not possess the oscillation behavior. The dominant phase-advanced driving method is applied to study the skeletons and oscillatory organizing motifs in excitable networks and gene regulatory networks.
基金Supported by NSFC(Nos.11661025,12161024)Natural Science Foundation of Guangxi(Nos.2020GXNSFAA159118,2021GXNSFAA196045)+2 种基金Guangxi Science and Technology Project(No.Guike AD20297006)Training Program for 1000 Young and Middle-aged Cadre Teachers in Universities of GuangxiNational College Student's Innovation and Entrepreneurship Training Program(No.202110595049)。
文摘In this paper,we present local functional law of the iterated logarithm for Cs?rg?-Révész type increments of fractional Brownian motion.The results obtained extend works of Gantert[Ann.Probab.,1993,21(2):1045-1049]and Monrad and Rootzén[Probab.Theory Related Fields,1995,101(2):173-192].
文摘Numerical simulations are used to investigate the self-sustained oscillating flows past an open cavity. The two-dimensional incompressible Navier-Stokes equations are solved directly by using the finite difference method for cavities with an upstream laminar boundary layer. A series of simulations are performed for a variety of cavity length-to-depth ratio. The results show the switching among some flow modes including non-oscillation mode, shear layer mode and wake mode. The variation of the Strouhal number is in favorable agreement with available experimental data. The results of flow fields in the cavity reveal the relationship between the cavity shear layer oscillation modes and recirculating vortices in the cavity.
