A corrosion defect is recognized as one of the most severe phenomena for high-pressure pipelines,especially those served for a long time.Finite-element method and empirical formulas are thereby used for the strength p...A corrosion defect is recognized as one of the most severe phenomena for high-pressure pipelines,especially those served for a long time.Finite-element method and empirical formulas are thereby used for the strength prediction of such pipes with corrosion.However,it is time-consuming for finite-element method and there is a limited application range by using empirical formulas.In order to improve the prediction of strength,this paper investigates the burst pressure of line pipelines with a single corrosion defect subjected to internal pressure based on data-driven methods.Three supervised ML(machine learning)algorithms,including the ANN(artificial neural network),the SVM(support vector machine)and the LR(linear regression),are deployed to train models based on experimental data.Data analysis is first conducted to determine proper pipe features for training.Hyperparameter tuning to control the learning process is then performed to fit the best strength models for corroded pipelines.Among all the proposed data-driven models,the ANN model with three neural layers has the highest training accuracy,but also presents the largest variance.The SVM model provides both high training accuracy and high validation accuracy.The LR model has the best performance in terms of generalization ability.These models can be served as surrogate models by transfer learning with new coming data in future research,facilitating a sustainable and intelligent decision-making of corroded pipelines.展开更多
This study employs a data-driven methodology that embeds the principle of dimensional invariance into an artificial neural network to automatically identify dominant dimensionless quantities in the penetration of rod ...This study employs a data-driven methodology that embeds the principle of dimensional invariance into an artificial neural network to automatically identify dominant dimensionless quantities in the penetration of rod projectiles into semi-infinite metal targets from experimental measurements.The derived mathematical expressions of dimensionless quantities are simplified by the examination of the exponent matrix and coupling relationships between feature variables.As a physics-based dimension reduction methodology,this way reduces high-dimensional parameter spaces to descriptions involving only a few physically interpretable dimensionless quantities in penetrating cases.Then the relative importance of various dimensionless feature variables on the penetration efficiencies for four impacting conditions is evaluated through feature selection engineering.The results indicate that the selected critical dimensionless feature variables by this synergistic method,without referring to the complex theoretical equations and aiding in the detailed knowledge of penetration mechanics,are in accordance with those reported in the reference.Lastly,the determined dimensionless quantities can be efficiently applied to conduct semi-empirical analysis for the specific penetrating case,and the reliability of regression functions is validated.展开更多
Ocean energy has progressively gained considerable interest due to its sufficient potential to meet the world’s energy demand,and the blade is the core component in electricity generation from the ocean current.Howev...Ocean energy has progressively gained considerable interest due to its sufficient potential to meet the world’s energy demand,and the blade is the core component in electricity generation from the ocean current.However,the widened hydraulic excitation frequency may satisfy the blade resonance due to the time variation in the velocity and angle of attack of the ocean current,even resulting in blade fatigue and destructively interfering with grid stability.A key parameter that determines the resonance amplitude of the blade is the hydrodynamic damping ratio(HDR).However,HDR is difficult to obtain due to the complex fluid-structure interaction(FSI).Therefore,a literature review was conducted on the hydrodynamic damping characteristics of blade-like structures.The experimental and simulation methods used to identify and obtain the HDR quantitatively were described,placing emphasis on the experimental processes and simulation setups.Moreover,the accuracy and efficiency of different simulation methods were compared,and the modal work approach was recommended.The effects of key typical parameters,including flow velocity,angle of attack,gap,rotational speed,and cavitation,on the HDR were then summarized,and the suggestions on operating conditions were presented from the perspective of increasing the HDR.Subsequently,considering multiple flow parameters,several theoretical derivations and semi-empirical prediction formulas for HDR were introduced,and the accuracy and application were discussed.Based on the shortcomings of the existing research,the direction of future research was finally determined.The current work offers a clear understanding of the HDR of blade-like structures,which could improve the evaluation accuracy of flow-induced vibration in the design stage.展开更多
In this study,the structural characters,antioxidant activities and bile acid-binding ability of sea buckthorn polysaccharides(HRPs)obtained by the commonly used hot water(HRP-W),pressurized hot water(HRP-H),ultrasonic...In this study,the structural characters,antioxidant activities and bile acid-binding ability of sea buckthorn polysaccharides(HRPs)obtained by the commonly used hot water(HRP-W),pressurized hot water(HRP-H),ultrasonic(HRP-U),acid(HRP-C)and alkali(HRP-A)assisted extraction methods were investigated.The results demonstrated that extraction methods had significant effects on extraction yield,monosaccharide composition,molecular weight,particle size,triple-helical structure,and surface morphology of HRPs except for the major linkage bands.Thermogravimetric analysis showed that HRP-U with filamentous reticular microstructure exhibited better thermal stability.The HRP-A with the lowest molecular weight and highest arabinose content possessed the best antioxidant activities.Moreover,the rheological analysis indicated that HRPs with higher galacturonic acid content and molecular weight showed higher viscosity and stronger crosslinking network(HRP-C,HRP-W and HRP-U),which exhibited stronger bile acid binding capacity.The present findings provide scientific evidence in the preparation technology of sea buckthorn polysaccharides with good antioxidant and bile acid binding capacity which are related to the structure affected by the extraction methods.展开更多
The shale gas development process is complex in terms of its flow mechanisms and the accuracy of the production forecasting is influenced by geological parameters and engineering parameters.Therefore,to quantitatively...The shale gas development process is complex in terms of its flow mechanisms and the accuracy of the production forecasting is influenced by geological parameters and engineering parameters.Therefore,to quantitatively evaluate the relative importance of model parameters on the production forecasting performance,sensitivity analysis of parameters is required.The parameters are ranked according to the sensitivity coefficients for the subsequent optimization scheme design.A data-driven global sensitivity analysis(GSA)method using convolutional neural networks(CNN)is proposed to identify the influencing parameters in shale gas production.The CNN is trained on a large dataset,validated against numerical simulations,and utilized as a surrogate model for efficient sensitivity analysis.Our approach integrates CNN with the Sobol'global sensitivity analysis method,presenting three key scenarios for sensitivity analysis:analysis of the production stage as a whole,analysis by fixed time intervals,and analysis by declining rate.The findings underscore the predominant influence of reservoir thickness and well length on shale gas production.Furthermore,the temporal sensitivity analysis reveals the dynamic shifts in parameter importance across the distinct production stages.展开更多
Quadratic matrix equations arise in many elds of scienti c computing and engineering applications.In this paper,we consider a class of quadratic matrix equations.Under a certain condition,we rst prove the existence of...Quadratic matrix equations arise in many elds of scienti c computing and engineering applications.In this paper,we consider a class of quadratic matrix equations.Under a certain condition,we rst prove the existence of minimal nonnegative solution for this quadratic matrix equation,and then propose some numerical methods for solving it.Convergence analysis and numerical examples are given to verify the theories and the numerical methods of this paper.展开更多
As an important material for manufacturing resonant components of musical instruments,Paulownia has an important influence on the sound quality of Ruan.In this paper,a model for evaluating the sound quality of Ruan ba...As an important material for manufacturing resonant components of musical instruments,Paulownia has an important influence on the sound quality of Ruan.In this paper,a model for evaluating the sound quality of Ruan based on the vibration characteristics of wood is developed using machine learning methods.Generally,the selection of materials for Ruan manufacturing relies primarily on manually weighing,observing,striking,and listening by the instrument technician.Deficiencies in scientific theory have hindered the quality of the finished Ruan.In this study,nine Ruans were manufactured,and a prediction model of Ruan sound quality was proposed based on the raw material information of Ruans.Out of a total of 180 data sets,145 and 45 sets were chosen for training and validation,respec-tively.In this paper,typical correlation analysis was used to determine the correlation between two single indicators in two adjacent pairwise combinations of the measured objects in each stage of the production process in Ruan.The vibra-tion characteristics of the wood were tested,and a model for predicting the evaluation of Ruan’s acoustic qualities was developed by measuring the vibration characteristics of the resonating plate material.The acoustic quality of the Ruan sound board wood was evaluated and predicted using machine learning model generalized regression neural net-work.The results show that the prediction of Ruan sound quality can be achieved using Matlab simulation based on the vibration characteristics of the soundboard wood.When the model-predicted values were compared with the tradi-tional predicted results,it was found that the generalized regression neural network had good performance,achieving an accuracy of 93.8%which was highly consistent with the experimental results.It was concluded that the model can accurately predict the acoustic quality of the Ruan based on the vibration performance of the soundboards.展开更多
Considerable efforts are being made to transition current lithium-ion and sodium-ion batteries towards the use of solid-state electrolytes.Computational methods,specifically nudged elastic band(NEB)and molecular dynam...Considerable efforts are being made to transition current lithium-ion and sodium-ion batteries towards the use of solid-state electrolytes.Computational methods,specifically nudged elastic band(NEB)and molecular dynamics(MD)methods,provide powerful tools for the design of solid-state electrolytes.The MD method is usually the choice for studying the materials involving complex multiple diffusion paths or having disordered structures.However,it relies on simulations at temperatures much higher than working temperature.This paper studies the reliability of the MD method using the system of Na diffusion in MgO as a benchmark.We carefully study the convergence behavior of the MD method and demonstrate that total effective simulation time of 12 ns can converge the calculated diffusion barrier to about 0.01 eV.The calculated diffusion barrier is 0.31 eV from both methods.The diffusion coefficients at room temperature are 4.3×10^(-9) cm^(2)⋅s^(−1) and 2.2×10^(-9) cm^(2)⋅s^(−1),respectively,from the NEB and MD methods.Our results justify the reliability of the MD method,even though high temperature simulations have to be employed to overcome the limitation on simulation time.展开更多
Electroencephalography(EEG)is a non-invasive measurement method for brain activity.Due to its safety,high resolution,and hypersensitivity to dynamic changes in brain neural signals,EEG has aroused much interest in sci...Electroencephalography(EEG)is a non-invasive measurement method for brain activity.Due to its safety,high resolution,and hypersensitivity to dynamic changes in brain neural signals,EEG has aroused much interest in scientific research and medical felds.This article reviews the types of EEG signals,multiple EEG signal analysis methods,and the application of relevant methods in the neuroscience feld and for diagnosing neurological diseases.First,3 types of EEG signals,including time-invariant EEG,accurate event-related EEG,and random event-related EEG,are introduced.Second,5 main directions for the methods of EEG analysis,including power spectrum analysis,time-frequency analysis,connectivity analysis,source localization methods,and machine learning methods,are described in the main section,along with diferent sub-methods and effect evaluations for solving the same problem.Finally,the application scenarios of different EEG analysis methods are emphasized,and the advantages and disadvantages of similar methods are distinguished.This article is expected to assist researchers in selecting suitable EEG analysis methods based on their research objectives,provide references for subsequent research,and summarize current issues and prospects for the future.展开更多
With the rapid advancement of machine learning technology and its growing adoption in research and engineering applications,an increasing number of studies have embraced data-driven approaches for modeling wind turbin...With the rapid advancement of machine learning technology and its growing adoption in research and engineering applications,an increasing number of studies have embraced data-driven approaches for modeling wind turbine wakes.These models leverage the ability to capture complex,high-dimensional characteristics of wind turbine wakes while offering significantly greater efficiency in the prediction process than physics-driven models.As a result,data-driven wind turbine wake models are regarded as powerful and effective tools for predicting wake behavior and turbine power output.This paper aims to provide a concise yet comprehensive review of existing studies on wind turbine wake modeling that employ data-driven approaches.It begins by defining and classifying machine learning methods to facilitate a clearer understanding of the reviewed literature.Subsequently,the related studies are categorized into four key areas:wind turbine power prediction,data-driven analytic wake models,wake field reconstruction,and the incorporation of explicit physical constraints.The accuracy of data-driven models is influenced by two primary factors:the quality of the training data and the performance of the model itself.Accordingly,both data accuracy and model structure are discussed in detail within the review.展开更多
To address the problems of low accuracy by the CONWEP model and poor efficiency by the Coupled Eulerian-Lagrangian(CEL)method in predicting close-range air blast loads of cylindrical charges,a neural network-based sim...To address the problems of low accuracy by the CONWEP model and poor efficiency by the Coupled Eulerian-Lagrangian(CEL)method in predicting close-range air blast loads of cylindrical charges,a neural network-based simulation(NNS)method with higher accuracy and better efficiency was proposed.The NNS method consisted of three main steps.First,the parameters of blast loads,including the peak pressures and impulses of cylindrical charges with different aspect ratios(L/D)at different stand-off distances and incident angles were obtained by two-dimensional numerical simulations.Subsequently,incident shape factors of cylindrical charges with arbitrary aspect ratios were predicted by a neural network.Finally,reflected shape factors were derived and implemented into the subroutine of the ABAQUS code to modify the CONWEP model,including modifications of impulse and overpressure.The reliability of the proposed NNS method was verified by related experimental results.Remarkable accuracy improvement was acquired by the proposed NNS method compared with the unmodified CONWEP model.Moreover,huge efficiency superiority was obtained by the proposed NNS method compared with the CEL method.The proposed NNS method showed good accuracy when the scaled distance was greater than 0.2 m/kg^(1/3).It should be noted that there is no need to generate a new dataset again since the blast loads satisfy the similarity law,and the proposed NNS method can be directly used to simulate the blast loads generated by different cylindrical charges.The proposed NNS method with high efficiency and accuracy can be used as an effective method to analyze the dynamic response of structures under blast loads,and it has significant application prospects in designing protective structures.展开更多
Currently,the cranes used at sea do not have enough flexibility,efficiency,and safety.Thus,this study proposed a floating multirobot coordinated towing system to meet the demands for offshore towing.Because of the fle...Currently,the cranes used at sea do not have enough flexibility,efficiency,and safety.Thus,this study proposed a floating multirobot coordinated towing system to meet the demands for offshore towing.Because of the flexibility of rope-driven robots,the one-way pulling characteristics of the rope,and the floating characteristics of the base,towing robots are easily overturned.First,the spatial configuration of the towing system was established according to the towing task,and the kinematic model of the towing system was established using the coordinate transformation.Then,the dynamic model of the towing system was established according to the rigid-body dynamics and hydrodynamic theory.Finally,the stability of the towing system was analyzed using the stability cone method.The simulation experiments provide a reference for the practical application of the floating multirobot coordinated towing system,which can improve the stability of towing systems by changing the configuration of the towing robot.展开更多
Solid-state batteries(SSBs) with high safety are promising for the energy fields,but the development has long been limited by machinability and interfacial problems.Hence,self-supporting,flexible Nano LLZO CSEs are pr...Solid-state batteries(SSBs) with high safety are promising for the energy fields,but the development has long been limited by machinability and interfacial problems.Hence,self-supporting,flexible Nano LLZO CSEs are prepared with a solvent-free method at 25℃.The 99.8 wt% contents of Nano LLZO particles enable the Nano LLZO CSEs to maintain good thermal stability while exhibiting a wide electrochemical window of 5.0 V and a high Li~+ transfer number of 0.8.The mean modulus reaches 4376 MPa.Benefiting from the interfacial modulation,the Li|Li symmetric batteries based on the Nano LLZO CSEs show benign stability with lithium at the current densities of 0.1 mA cm^(-2),0.2 mA cm^(-2),and 0.5 mA cm^(-2).In addition,the Li|LiFePO_(4)(LFP) SSBs achieve favorable cycling performance:the specific capacity reaches128.1 mAh g^(-1) at 0.5 C rate,with a capacity retention of about 80% after 600 cycles.In the further tests of the LiNi_(0.8)Co_(0.1)Mn_(0.1)O_(2)(NCM811) cathodes with higher energy density,the Nano LLZO CSEs also demonstrate good compatibility:the specific capacities of NCM811-based SSBs reach 177.9 mAh g^(-1) at 0.5 C rate,while the capacity retention is over 96% after 150 cycles.Furthermore,the Li|LFP soft-pack SSBs verify the safety characteristics and the potential for application,which have a desirable prospect.展开更多
An optical emission spectroscopy(OES)method with a non-invasive measurement capability,without inducing disturbance to the discharge,represents an effective method for material monitoring.However,when the OES method i...An optical emission spectroscopy(OES)method with a non-invasive measurement capability,without inducing disturbance to the discharge,represents an effective method for material monitoring.However,when the OES method is employed to monitor the trace erosion product within the ceramic channel of a Hall thruster,it becomes challenging to distinguish between signal and noise.In this study,we propose a model filtering method based on the signal characteristics of the Hall thruster plume spectrometer.This method integrates the slit imaging and spectral resolution features of the spectrometer.Employing this method,we extract the spectral signals of the erosion product and working gas from the Hall thruster under different operating conditions.The results indicate that our new method performs comparably to the traditional method without model filtering when extracting atom signals from strong xenon working gas.However,for trace amounts of the erosion product,our approach significantly enhances the signal-to-noise ratio(SNR),enabling the identification of extremely weak spectral signals even under low mass flow rate and low-voltage conditions.We obtain boron atom concentration of 3.91×10^(-3) kg/m^(3) at a mass flow rate of 4×10^(-7) kg/s and voltage of 200 V while monitoring a wider range of thruster operating conditions.The new method proposed in this study is suitable for monitoring other low-concentration elements,making it valuable for materials processing,environmental monitoring and space propulsion applications.展开更多
Controlled nuclear fusion represents a significant solution for future clean energy,with ion cyclotron range of frequency(ICRF)heating emerging as one of the most promising technologies for heating the fusion plasma.T...Controlled nuclear fusion represents a significant solution for future clean energy,with ion cyclotron range of frequency(ICRF)heating emerging as one of the most promising technologies for heating the fusion plasma.This study primarily presents a self-developed 2D ion cyclotron resonance antenna electromagnetic field solver(ICRAEMS)code implemented on the MATLAB platform,which solves the electric field wave equation by using the finite element method,establishing perfectly matched layer(PML)boundary conditions,and post-processing the electromagnetic field data.This code can be utilized to facilitate the design and optimization processes of antennas for ICRF heating technology.Furthermore,this study examines the electric field distribution and power spectrum associated with various antenna phases to investigate how different antenna configurations affect the electromagnetic field propagation and coupling characteristics.展开更多
In this paper,a composite numerical scheme is proposed to solve the threedimensional Darcy-Forchheimer miscible displacement problem with positive semi-definite assumptions.A mixed finite element is used for the fow e...In this paper,a composite numerical scheme is proposed to solve the threedimensional Darcy-Forchheimer miscible displacement problem with positive semi-definite assumptions.A mixed finite element is used for the fow equation.The velocity and pressure are computed simultaneously.The accuracy of velocity is improved one order.The concentration equation is solved by using mixed finite element,multi-step difference and upwind approximation.A multi-step method is used to approximate time derivative for improving the accuracy.The upwind approximation and an expanded mixed finite element are adopted to solve the convection and diffusion,respectively.The composite method could compute the diffusion flux and its gradient.It possibly becomes an eficient tool for solving convection-dominated diffusion problems.Firstly,the conservation of mass holds.Secondly,the multi-step method has high accuracy.Thirdly,the upwind approximation could avoid numerical dispersion.Using numerical analysis of a priori estimates and special techniques of differential equations,we give an error estimates for a positive definite problem.Numerical experiments illustrate its computational efficiency and feasibility of application.展开更多
The accumulation of^(222)Rn and^(220)Rn progeny in poorly ventilated environments poses the risk of natural radiation exposure to the public.A previous study indicated that satisfactory results in determining the^(222...The accumulation of^(222)Rn and^(220)Rn progeny in poorly ventilated environments poses the risk of natural radiation exposure to the public.A previous study indicated that satisfactory results in determining the^(222)Rn and^(220)Rn progeny concentrations by measuring the total alpha counts at five time intervals within 560 min should be expected only in the case of high progeny concentrations in air.To complete the measurement within a relatively short period and adapt it for simultaneous measurements at comparatively lower^(222)Rn and^(220)Rn progeny concentrations,a novel mathematical model was proposed based on the radioactive decay law.This model employs a nonlinear fitting method to distinguish nuclides with overlapping spectra by utilizing the alpha particle counts of non-overlapping spectra within consecutive measurement cycles to obtain the concentrations of^(222)Rn and^(220)Rn progeny in air.Several verification experiments were conducted using an alpha spectrometer.The experimental results demonstrate that the concentrations of^(222)Rn and^(220)Rn progeny calculated by the new method align more closely with the actual circumstances than those calculated by the total count method,and their relative uncertainties are all within±16%.Furthermore,the measurement time was reduced to 90 min,representing an acceleration of 84%.The improved capability of the new method in distinguishing alpha particles with similar energies emitted from ^(218)Po and^(212)Bi,both approximately 6 MeV,contributed to realizing more accurate results.The proposed method has the potential advantage of measuring relatively low concentrations of^(222)Rn and^(220)Rn progeny in air more quickly via air filtration.展开更多
The preconditioning method is used to solve the low Mach number flow. The space discritisation scheme is the Roe scheme and the DES turbulence model is used. Then, the low Mach number turbulence flow around the NACA00...The preconditioning method is used to solve the low Mach number flow. The space discritisation scheme is the Roe scheme and the DES turbulence model is used. Then, the low Mach number turbulence flow around the NACA0012 airfoil is used to verify the efficiency of the proposed method. Two cases of the low Mach number flows around the multi-element airfoil and the circular cylinder are also used to test the proposed method. Numerical results show that the methods combined the preconditioning method and compressible Navier-Stokes equations are efficient to solve low Mach number flows.展开更多
The stochastic boundary element method(SBEM)is developed in this paper for 3D problems with body forces and reliability analysis of engineering structures.The integral equations of SBEM are established by the approach...The stochastic boundary element method(SBEM)is developed in this paper for 3D problems with body forces and reliability analysis of engineering structures.The integral equations of SBEM are established by the approach of partial derivation with respect to stochastic variables,considering the yield limit,rotation speeds and material density to be the fundamental stochastic variables.Through analyzing a numerical example and a turbo-disk of an aeroengine,the results show that the method developed is successful.展开更多
文摘A corrosion defect is recognized as one of the most severe phenomena for high-pressure pipelines,especially those served for a long time.Finite-element method and empirical formulas are thereby used for the strength prediction of such pipes with corrosion.However,it is time-consuming for finite-element method and there is a limited application range by using empirical formulas.In order to improve the prediction of strength,this paper investigates the burst pressure of line pipelines with a single corrosion defect subjected to internal pressure based on data-driven methods.Three supervised ML(machine learning)algorithms,including the ANN(artificial neural network),the SVM(support vector machine)and the LR(linear regression),are deployed to train models based on experimental data.Data analysis is first conducted to determine proper pipe features for training.Hyperparameter tuning to control the learning process is then performed to fit the best strength models for corroded pipelines.Among all the proposed data-driven models,the ANN model with three neural layers has the highest training accuracy,but also presents the largest variance.The SVM model provides both high training accuracy and high validation accuracy.The LR model has the best performance in terms of generalization ability.These models can be served as surrogate models by transfer learning with new coming data in future research,facilitating a sustainable and intelligent decision-making of corroded pipelines.
基金supported by the National Natural Science Foundation of China(Grant Nos.12272257,12102292,12032006)the special fund for Science and Technology Innovation Teams of Shanxi Province(Nos.202204051002006).
文摘This study employs a data-driven methodology that embeds the principle of dimensional invariance into an artificial neural network to automatically identify dominant dimensionless quantities in the penetration of rod projectiles into semi-infinite metal targets from experimental measurements.The derived mathematical expressions of dimensionless quantities are simplified by the examination of the exponent matrix and coupling relationships between feature variables.As a physics-based dimension reduction methodology,this way reduces high-dimensional parameter spaces to descriptions involving only a few physically interpretable dimensionless quantities in penetrating cases.Then the relative importance of various dimensionless feature variables on the penetration efficiencies for four impacting conditions is evaluated through feature selection engineering.The results indicate that the selected critical dimensionless feature variables by this synergistic method,without referring to the complex theoretical equations and aiding in the detailed knowledge of penetration mechanics,are in accordance with those reported in the reference.Lastly,the determined dimensionless quantities can be efficiently applied to conduct semi-empirical analysis for the specific penetrating case,and the reliability of regression functions is validated.
基金Supported by the National Natural Science Foundation of China(Nos.52222904 and 52309117)China Postdoctoral Science Foundation(Nos.2022TQ0168 and 2023M731895).
文摘Ocean energy has progressively gained considerable interest due to its sufficient potential to meet the world’s energy demand,and the blade is the core component in electricity generation from the ocean current.However,the widened hydraulic excitation frequency may satisfy the blade resonance due to the time variation in the velocity and angle of attack of the ocean current,even resulting in blade fatigue and destructively interfering with grid stability.A key parameter that determines the resonance amplitude of the blade is the hydrodynamic damping ratio(HDR).However,HDR is difficult to obtain due to the complex fluid-structure interaction(FSI).Therefore,a literature review was conducted on the hydrodynamic damping characteristics of blade-like structures.The experimental and simulation methods used to identify and obtain the HDR quantitatively were described,placing emphasis on the experimental processes and simulation setups.Moreover,the accuracy and efficiency of different simulation methods were compared,and the modal work approach was recommended.The effects of key typical parameters,including flow velocity,angle of attack,gap,rotational speed,and cavitation,on the HDR were then summarized,and the suggestions on operating conditions were presented from the perspective of increasing the HDR.Subsequently,considering multiple flow parameters,several theoretical derivations and semi-empirical prediction formulas for HDR were introduced,and the accuracy and application were discussed.Based on the shortcomings of the existing research,the direction of future research was finally determined.The current work offers a clear understanding of the HDR of blade-like structures,which could improve the evaluation accuracy of flow-induced vibration in the design stage.
基金The Guangdong Basic and Applied Basic Research Foundation(2022A1515010730)National Natural Science Foundation of China(32001647)+2 种基金National Natural Science Foundation of China(31972022)Financial and moral assistance supported by the Guangdong Basic and Applied Basic Research Foundation(2019A1515011996)111 Project(B17018)。
文摘In this study,the structural characters,antioxidant activities and bile acid-binding ability of sea buckthorn polysaccharides(HRPs)obtained by the commonly used hot water(HRP-W),pressurized hot water(HRP-H),ultrasonic(HRP-U),acid(HRP-C)and alkali(HRP-A)assisted extraction methods were investigated.The results demonstrated that extraction methods had significant effects on extraction yield,monosaccharide composition,molecular weight,particle size,triple-helical structure,and surface morphology of HRPs except for the major linkage bands.Thermogravimetric analysis showed that HRP-U with filamentous reticular microstructure exhibited better thermal stability.The HRP-A with the lowest molecular weight and highest arabinose content possessed the best antioxidant activities.Moreover,the rheological analysis indicated that HRPs with higher galacturonic acid content and molecular weight showed higher viscosity and stronger crosslinking network(HRP-C,HRP-W and HRP-U),which exhibited stronger bile acid binding capacity.The present findings provide scientific evidence in the preparation technology of sea buckthorn polysaccharides with good antioxidant and bile acid binding capacity which are related to the structure affected by the extraction methods.
基金supported by the National Natural Science Foundation of China (Nos.52274048 and 52374017)Beijing Natural Science Foundation (No.3222037)the CNPC 14th five-year perspective fundamental research project (No.2021DJ2104)。
文摘The shale gas development process is complex in terms of its flow mechanisms and the accuracy of the production forecasting is influenced by geological parameters and engineering parameters.Therefore,to quantitatively evaluate the relative importance of model parameters on the production forecasting performance,sensitivity analysis of parameters is required.The parameters are ranked according to the sensitivity coefficients for the subsequent optimization scheme design.A data-driven global sensitivity analysis(GSA)method using convolutional neural networks(CNN)is proposed to identify the influencing parameters in shale gas production.The CNN is trained on a large dataset,validated against numerical simulations,and utilized as a surrogate model for efficient sensitivity analysis.Our approach integrates CNN with the Sobol'global sensitivity analysis method,presenting three key scenarios for sensitivity analysis:analysis of the production stage as a whole,analysis by fixed time intervals,and analysis by declining rate.The findings underscore the predominant influence of reservoir thickness and well length on shale gas production.Furthermore,the temporal sensitivity analysis reveals the dynamic shifts in parameter importance across the distinct production stages.
基金Supported by the National Natural Science Foundation of China(12001395)the special fund for Science and Technology Innovation Teams of Shanxi Province(202204051002018)+1 种基金Research Project Supported by Shanxi Scholarship Council of China(2022-169)Graduate Education Innovation Project of Taiyuan Normal University(SYYJSYC-2314)。
文摘Quadratic matrix equations arise in many elds of scienti c computing and engineering applications.In this paper,we consider a class of quadratic matrix equations.Under a certain condition,we rst prove the existence of minimal nonnegative solution for this quadratic matrix equation,and then propose some numerical methods for solving it.Convergence analysis and numerical examples are given to verify the theories and the numerical methods of this paper.
基金supported by China Postdoctoral Science Foundation(2019M651240)National Natural Science Foundation of China(31670559).
文摘As an important material for manufacturing resonant components of musical instruments,Paulownia has an important influence on the sound quality of Ruan.In this paper,a model for evaluating the sound quality of Ruan based on the vibration characteristics of wood is developed using machine learning methods.Generally,the selection of materials for Ruan manufacturing relies primarily on manually weighing,observing,striking,and listening by the instrument technician.Deficiencies in scientific theory have hindered the quality of the finished Ruan.In this study,nine Ruans were manufactured,and a prediction model of Ruan sound quality was proposed based on the raw material information of Ruans.Out of a total of 180 data sets,145 and 45 sets were chosen for training and validation,respec-tively.In this paper,typical correlation analysis was used to determine the correlation between two single indicators in two adjacent pairwise combinations of the measured objects in each stage of the production process in Ruan.The vibra-tion characteristics of the wood were tested,and a model for predicting the evaluation of Ruan’s acoustic qualities was developed by measuring the vibration characteristics of the resonating plate material.The acoustic quality of the Ruan sound board wood was evaluated and predicted using machine learning model generalized regression neural net-work.The results show that the prediction of Ruan sound quality can be achieved using Matlab simulation based on the vibration characteristics of the soundboard wood.When the model-predicted values were compared with the tradi-tional predicted results,it was found that the generalized regression neural network had good performance,achieving an accuracy of 93.8%which was highly consistent with the experimental results.It was concluded that the model can accurately predict the acoustic quality of the Ruan based on the vibration performance of the soundboards.
基金supported by the National Natural Science Foundation of China (Grant Nos.12164019,11991060,12088101,and U1930402)the Natural Science Foundation of Jiangxi Province of China (Grant No.20212BAB201017).
文摘Considerable efforts are being made to transition current lithium-ion and sodium-ion batteries towards the use of solid-state electrolytes.Computational methods,specifically nudged elastic band(NEB)and molecular dynamics(MD)methods,provide powerful tools for the design of solid-state electrolytes.The MD method is usually the choice for studying the materials involving complex multiple diffusion paths or having disordered structures.However,it relies on simulations at temperatures much higher than working temperature.This paper studies the reliability of the MD method using the system of Na diffusion in MgO as a benchmark.We carefully study the convergence behavior of the MD method and demonstrate that total effective simulation time of 12 ns can converge the calculated diffusion barrier to about 0.01 eV.The calculated diffusion barrier is 0.31 eV from both methods.The diffusion coefficients at room temperature are 4.3×10^(-9) cm^(2)⋅s^(−1) and 2.2×10^(-9) cm^(2)⋅s^(−1),respectively,from the NEB and MD methods.Our results justify the reliability of the MD method,even though high temperature simulations have to be employed to overcome the limitation on simulation time.
基金supported by the STI2030 Major Projects(2021ZD0204300)the National Natural Science Foundation of China(61803003,62003228).
文摘Electroencephalography(EEG)is a non-invasive measurement method for brain activity.Due to its safety,high resolution,and hypersensitivity to dynamic changes in brain neural signals,EEG has aroused much interest in scientific research and medical felds.This article reviews the types of EEG signals,multiple EEG signal analysis methods,and the application of relevant methods in the neuroscience feld and for diagnosing neurological diseases.First,3 types of EEG signals,including time-invariant EEG,accurate event-related EEG,and random event-related EEG,are introduced.Second,5 main directions for the methods of EEG analysis,including power spectrum analysis,time-frequency analysis,connectivity analysis,source localization methods,and machine learning methods,are described in the main section,along with diferent sub-methods and effect evaluations for solving the same problem.Finally,the application scenarios of different EEG analysis methods are emphasized,and the advantages and disadvantages of similar methods are distinguished.This article is expected to assist researchers in selecting suitable EEG analysis methods based on their research objectives,provide references for subsequent research,and summarize current issues and prospects for the future.
基金Supported by the National Natural Science Foundation of China under Grant No.52131102.
文摘With the rapid advancement of machine learning technology and its growing adoption in research and engineering applications,an increasing number of studies have embraced data-driven approaches for modeling wind turbine wakes.These models leverage the ability to capture complex,high-dimensional characteristics of wind turbine wakes while offering significantly greater efficiency in the prediction process than physics-driven models.As a result,data-driven wind turbine wake models are regarded as powerful and effective tools for predicting wake behavior and turbine power output.This paper aims to provide a concise yet comprehensive review of existing studies on wind turbine wake modeling that employ data-driven approaches.It begins by defining and classifying machine learning methods to facilitate a clearer understanding of the reviewed literature.Subsequently,the related studies are categorized into four key areas:wind turbine power prediction,data-driven analytic wake models,wake field reconstruction,and the incorporation of explicit physical constraints.The accuracy of data-driven models is influenced by two primary factors:the quality of the training data and the performance of the model itself.Accordingly,both data accuracy and model structure are discussed in detail within the review.
基金financially supported by the National Natural Science Foundation of China(Grant Nos.52271317 and 52071149)the Fundamental Research Funds for the Central Universities(HUST:2019kfy XJJS007)。
文摘To address the problems of low accuracy by the CONWEP model and poor efficiency by the Coupled Eulerian-Lagrangian(CEL)method in predicting close-range air blast loads of cylindrical charges,a neural network-based simulation(NNS)method with higher accuracy and better efficiency was proposed.The NNS method consisted of three main steps.First,the parameters of blast loads,including the peak pressures and impulses of cylindrical charges with different aspect ratios(L/D)at different stand-off distances and incident angles were obtained by two-dimensional numerical simulations.Subsequently,incident shape factors of cylindrical charges with arbitrary aspect ratios were predicted by a neural network.Finally,reflected shape factors were derived and implemented into the subroutine of the ABAQUS code to modify the CONWEP model,including modifications of impulse and overpressure.The reliability of the proposed NNS method was verified by related experimental results.Remarkable accuracy improvement was acquired by the proposed NNS method compared with the unmodified CONWEP model.Moreover,huge efficiency superiority was obtained by the proposed NNS method compared with the CEL method.The proposed NNS method showed good accuracy when the scaled distance was greater than 0.2 m/kg^(1/3).It should be noted that there is no need to generate a new dataset again since the blast loads satisfy the similarity law,and the proposed NNS method can be directly used to simulate the blast loads generated by different cylindrical charges.The proposed NNS method with high efficiency and accuracy can be used as an effective method to analyze the dynamic response of structures under blast loads,and it has significant application prospects in designing protective structures.
基金Supported by the National Natural Science Foundation of China under Grant No.51965032the Natural Science Foundation of Gansu Province of China under Grant No.22JR5RA319+2 种基金the Excellent Doctoral Student Foundation of Gansu Province of China under Grant No.23JRRA842the Sichuan Province Engineering Technology Research Center of General Aircraft Maintenance under Grant No.GAMRC2023YB05the Key Research and Development Project of Lanzhou Jiaotong University under Grant No.LZJTUZDYF2302.
文摘Currently,the cranes used at sea do not have enough flexibility,efficiency,and safety.Thus,this study proposed a floating multirobot coordinated towing system to meet the demands for offshore towing.Because of the flexibility of rope-driven robots,the one-way pulling characteristics of the rope,and the floating characteristics of the base,towing robots are easily overturned.First,the spatial configuration of the towing system was established according to the towing task,and the kinematic model of the towing system was established using the coordinate transformation.Then,the dynamic model of the towing system was established according to the rigid-body dynamics and hydrodynamic theory.Finally,the stability of the towing system was analyzed using the stability cone method.The simulation experiments provide a reference for the practical application of the floating multirobot coordinated towing system,which can improve the stability of towing systems by changing the configuration of the towing robot.
基金supported by Science and Technology Project of China Southern Power Grid (SZKJXM20230049/090000KC23010038)。
文摘Solid-state batteries(SSBs) with high safety are promising for the energy fields,but the development has long been limited by machinability and interfacial problems.Hence,self-supporting,flexible Nano LLZO CSEs are prepared with a solvent-free method at 25℃.The 99.8 wt% contents of Nano LLZO particles enable the Nano LLZO CSEs to maintain good thermal stability while exhibiting a wide electrochemical window of 5.0 V and a high Li~+ transfer number of 0.8.The mean modulus reaches 4376 MPa.Benefiting from the interfacial modulation,the Li|Li symmetric batteries based on the Nano LLZO CSEs show benign stability with lithium at the current densities of 0.1 mA cm^(-2),0.2 mA cm^(-2),and 0.5 mA cm^(-2).In addition,the Li|LiFePO_(4)(LFP) SSBs achieve favorable cycling performance:the specific capacity reaches128.1 mAh g^(-1) at 0.5 C rate,with a capacity retention of about 80% after 600 cycles.In the further tests of the LiNi_(0.8)Co_(0.1)Mn_(0.1)O_(2)(NCM811) cathodes with higher energy density,the Nano LLZO CSEs also demonstrate good compatibility:the specific capacities of NCM811-based SSBs reach 177.9 mAh g^(-1) at 0.5 C rate,while the capacity retention is over 96% after 150 cycles.Furthermore,the Li|LFP soft-pack SSBs verify the safety characteristics and the potential for application,which have a desirable prospect.
基金financially supported by National Natural Science Foundation of China(No.U22B2094)。
文摘An optical emission spectroscopy(OES)method with a non-invasive measurement capability,without inducing disturbance to the discharge,represents an effective method for material monitoring.However,when the OES method is employed to monitor the trace erosion product within the ceramic channel of a Hall thruster,it becomes challenging to distinguish between signal and noise.In this study,we propose a model filtering method based on the signal characteristics of the Hall thruster plume spectrometer.This method integrates the slit imaging and spectral resolution features of the spectrometer.Employing this method,we extract the spectral signals of the erosion product and working gas from the Hall thruster under different operating conditions.The results indicate that our new method performs comparably to the traditional method without model filtering when extracting atom signals from strong xenon working gas.However,for trace amounts of the erosion product,our approach significantly enhances the signal-to-noise ratio(SNR),enabling the identification of extremely weak spectral signals even under low mass flow rate and low-voltage conditions.We obtain boron atom concentration of 3.91×10^(-3) kg/m^(3) at a mass flow rate of 4×10^(-7) kg/s and voltage of 200 V while monitoring a wider range of thruster operating conditions.The new method proposed in this study is suitable for monitoring other low-concentration elements,making it valuable for materials processing,environmental monitoring and space propulsion applications.
基金Project supported by the National MCF Energy R&D Program(Grant No.2022YFE03190100)the National Natural Science Foundation of China(Grant Nos.12422513,12105035,and U21A20438)the Xiaomi Young Talents Program.
文摘Controlled nuclear fusion represents a significant solution for future clean energy,with ion cyclotron range of frequency(ICRF)heating emerging as one of the most promising technologies for heating the fusion plasma.This study primarily presents a self-developed 2D ion cyclotron resonance antenna electromagnetic field solver(ICRAEMS)code implemented on the MATLAB platform,which solves the electric field wave equation by using the finite element method,establishing perfectly matched layer(PML)boundary conditions,and post-processing the electromagnetic field data.This code can be utilized to facilitate the design and optimization processes of antennas for ICRF heating technology.Furthermore,this study examines the electric field distribution and power spectrum associated with various antenna phases to investigate how different antenna configurations affect the electromagnetic field propagation and coupling characteristics.
基金supported by the Natural Science Foundation of Shandong Province(ZR2021MA019)the National Natural Science Foundation of China(11871312)。
文摘In this paper,a composite numerical scheme is proposed to solve the threedimensional Darcy-Forchheimer miscible displacement problem with positive semi-definite assumptions.A mixed finite element is used for the fow equation.The velocity and pressure are computed simultaneously.The accuracy of velocity is improved one order.The concentration equation is solved by using mixed finite element,multi-step difference and upwind approximation.A multi-step method is used to approximate time derivative for improving the accuracy.The upwind approximation and an expanded mixed finite element are adopted to solve the convection and diffusion,respectively.The composite method could compute the diffusion flux and its gradient.It possibly becomes an eficient tool for solving convection-dominated diffusion problems.Firstly,the conservation of mass holds.Secondly,the multi-step method has high accuracy.Thirdly,the upwind approximation could avoid numerical dispersion.Using numerical analysis of a priori estimates and special techniques of differential equations,we give an error estimates for a positive definite problem.Numerical experiments illustrate its computational efficiency and feasibility of application.
基金supported by the National Natural Science Foundation of China(No.12075112)Natural Science Foundation of Hunan(No.2023JJ50121),Natural Science Foundation of Hunan Province(No.2023JJ50091)Key Projects of Hunan Provincial Department of Education(No.23A0516).
文摘The accumulation of^(222)Rn and^(220)Rn progeny in poorly ventilated environments poses the risk of natural radiation exposure to the public.A previous study indicated that satisfactory results in determining the^(222)Rn and^(220)Rn progeny concentrations by measuring the total alpha counts at five time intervals within 560 min should be expected only in the case of high progeny concentrations in air.To complete the measurement within a relatively short period and adapt it for simultaneous measurements at comparatively lower^(222)Rn and^(220)Rn progeny concentrations,a novel mathematical model was proposed based on the radioactive decay law.This model employs a nonlinear fitting method to distinguish nuclides with overlapping spectra by utilizing the alpha particle counts of non-overlapping spectra within consecutive measurement cycles to obtain the concentrations of^(222)Rn and^(220)Rn progeny in air.Several verification experiments were conducted using an alpha spectrometer.The experimental results demonstrate that the concentrations of^(222)Rn and^(220)Rn progeny calculated by the new method align more closely with the actual circumstances than those calculated by the total count method,and their relative uncertainties are all within±16%.Furthermore,the measurement time was reduced to 90 min,representing an acceleration of 84%.The improved capability of the new method in distinguishing alpha particles with similar energies emitted from ^(218)Po and^(212)Bi,both approximately 6 MeV,contributed to realizing more accurate results.The proposed method has the potential advantage of measuring relatively low concentrations of^(222)Rn and^(220)Rn progeny in air more quickly via air filtration.
文摘The preconditioning method is used to solve the low Mach number flow. The space discritisation scheme is the Roe scheme and the DES turbulence model is used. Then, the low Mach number turbulence flow around the NACA0012 airfoil is used to verify the efficiency of the proposed method. Two cases of the low Mach number flows around the multi-element airfoil and the circular cylinder are also used to test the proposed method. Numerical results show that the methods combined the preconditioning method and compressible Navier-Stokes equations are efficient to solve low Mach number flows.
文摘The stochastic boundary element method(SBEM)is developed in this paper for 3D problems with body forces and reliability analysis of engineering structures.The integral equations of SBEM are established by the approach of partial derivation with respect to stochastic variables,considering the yield limit,rotation speeds and material density to be the fundamental stochastic variables.Through analyzing a numerical example and a turbo-disk of an aeroengine,the results show that the method developed is successful.
