Despite dedicated effort for many decades,statistical description of highly technologically important wall turbulence remains a great challenge.Current models are unfortunately incomplete,or empirical,or qualitative.A...Despite dedicated effort for many decades,statistical description of highly technologically important wall turbulence remains a great challenge.Current models are unfortunately incomplete,or empirical,or qualitative.After a review of the existing theories of wall turbulence,we present a new framework,called the structure ensemble dynamics （SED）,which aims at integrating the turbulence dynamics into a quantitative description of the mean flow.The SED theory naturally evolves from a statistical physics understanding of non-equilibrium open systems,such as fluid turbulence, for which mean quantities are intimately coupled with the fluctuation dynamics.Starting from the ensemble-averaged Navier-Stokes（EANS） equations,the theory postulates the existence of a finite number of statistical states yielding a multi-layer picture for wall turbulence.Then,it uses order functions（ratios of terms in the mean momentum as well as energy equations） to characterize the states and transitions between states.Application of the SED analysis to an incompressible channel flow and a compressible turbulent boundary layer shows that the order functions successfully reveal the multi-layer structure for wall-bounded turbulence, which arises as a quantitative extension of the traditional view in terms of sub-layer,buffer layer,log layer and wake. Furthermore,an idea of using a set of hyperbolic functions for modeling transitions between layers is proposed for a quantitative model of order functions across the entire flow domain.We conclude that the SED provides a theoretical framework for expressing the yet-unknown effects of fluctuation structures on the mean quantities,and offers new methods to analyze experimental and simulation data.Combined with asymptotic analysis,it also offers a way to evaluate convergence of simulations.The SED approach successfully describes the dynamics at both momentum and energy levels, in contrast with all prevalent approaches describing the mean velocity profile only.Moreover,the SED theoretical fr展开更多
提出一种结合多层结构和稀疏最小二乘支持向量机(Sparse Least Squares Support Vector Machine,SLSSVM)的机械故障诊断方法。该方法构建了多层支持向量机(Support Vector Machine,SVM)结构,首先在输入层利用支持向量机对信号进行训练,...提出一种结合多层结构和稀疏最小二乘支持向量机(Sparse Least Squares Support Vector Machine,SLSSVM)的机械故障诊断方法。该方法构建了多层支持向量机(Support Vector Machine,SVM)结构,首先在输入层利用支持向量机对信号进行训练,学习信号的浅层特征,利用“降维公式”生成样本新的表示,并作为隐藏层的输入,隐藏层支持向量机对新样本训练并提取信号的深层特征,逐层学习,最终在输出层输出诊断结果。针对因多层结构带来算法的复杂度以及运行时间增加的问题,采用最小二乘支持向量机(Least Squares Support Vector Machine,LSSVM)技术,并将稀疏化理论与最小二乘支持向量机结合,通过构造特征空间近似最大线性无关向量组对样本进行稀疏表示并依此获得分类判别函数,有效解决了最小二乘支持向量机稀疏性缺乏的问题。最后,通过滚动轴承故障诊断实验验证了该方法的有效性。展开更多
Developing porous self-supporting electrodes with excellent conductivity,good mechanical properties,and high electrochemical activity is crucial for constructing electrode materials with lightweight,ultra-thin,flexibl...Developing porous self-supporting electrodes with excellent conductivity,good mechanical properties,and high electrochemical activity is crucial for constructing electrode materials with lightweight,ultra-thin,flexible,and high capacitance performance.In this work,we prepared a cellulose nanofibers(CNFs)/carbon nanotubes(CNTs)/vinasse activated carbon(VAC)(CCV)composite material with a multi-layer hierarchical conductive structure through simple vacuum filtration and freeze-drying.In this composite material,the self-assembly of CNF provides the main skeleton structure of a multi-layer hierarchical structure.CNT provides a fast path for the rapid transfer of electrons and is beneficial for the loss of electromagnetic waves.VAC provides sufficient double layer performance.The synergistic effect of the above three endows CCV composite materials with excellent energy storage performance and electromagnetic interference(EMI)shielding performance.In addition,we endowed the CCV composite with a certain shape and performance by introducing a vitrimer polymer with a dynamic cross-linked network structure.In summary,thanks to the synergistic effect of various components in the multi-layer hierarchical structure,CCV composite materials exhibit excellent integration performance,especially stable energy storage performance and EMI shielding performance.These significant properties make CCV composite materials have great application prospects in the fields of energy storage and intelligent EMI shielding.展开更多
Mechanical stimuli play critical roles in cardiovascular diseases,in which in vivo stresses in blood vessels present a great challenge to predict.Based on the structural-thermal coupled finite element method,we propos...Mechanical stimuli play critical roles in cardiovascular diseases,in which in vivo stresses in blood vessels present a great challenge to predict.Based on the structural-thermal coupled finite element method,we propose a thermal expansion method to estimate stresses in multi-layer blood vessels under healthy and pathological conditions.The proposed method provides a relatively simple and convenient means to predict reliable in vivo mechanical stresses with accurate residual stress.The method is first verified with the opening-up process and the pressure-radius responses for single and multi-layer vessel models.It is then applied to study the stress variation in a human carotid artery at different hypertension stages and in a plaque of vascular stenosis.Our results show that specific or optimal residual stresses exist for different blood pressures,which helps form a homogeneous stress distribution across vessel walls.High elastic shear stress is identified on the shoulder of the plaque,which contributes to the tearing effect in plaque rupture.The present study indicates that the proposed numerical method is a capable and efficient in vivo stress evaluation of patient-specific blood vessels for clinical purposes.展开更多
基金supported by the National Natural Science Foundation of China(90716008)the National Basic Research Program of China(2009CB724100).
文摘Despite dedicated effort for many decades,statistical description of highly technologically important wall turbulence remains a great challenge.Current models are unfortunately incomplete,or empirical,or qualitative.After a review of the existing theories of wall turbulence,we present a new framework,called the structure ensemble dynamics （SED）,which aims at integrating the turbulence dynamics into a quantitative description of the mean flow.The SED theory naturally evolves from a statistical physics understanding of non-equilibrium open systems,such as fluid turbulence, for which mean quantities are intimately coupled with the fluctuation dynamics.Starting from the ensemble-averaged Navier-Stokes（EANS） equations,the theory postulates the existence of a finite number of statistical states yielding a multi-layer picture for wall turbulence.Then,it uses order functions（ratios of terms in the mean momentum as well as energy equations） to characterize the states and transitions between states.Application of the SED analysis to an incompressible channel flow and a compressible turbulent boundary layer shows that the order functions successfully reveal the multi-layer structure for wall-bounded turbulence, which arises as a quantitative extension of the traditional view in terms of sub-layer,buffer layer,log layer and wake. Furthermore,an idea of using a set of hyperbolic functions for modeling transitions between layers is proposed for a quantitative model of order functions across the entire flow domain.We conclude that the SED provides a theoretical framework for expressing the yet-unknown effects of fluctuation structures on the mean quantities,and offers new methods to analyze experimental and simulation data.Combined with asymptotic analysis,it also offers a way to evaluate convergence of simulations.The SED approach successfully describes the dynamics at both momentum and energy levels, in contrast with all prevalent approaches describing the mean velocity profile only.Moreover,the SED theoretical fr
文摘提出一种结合多层结构和稀疏最小二乘支持向量机(Sparse Least Squares Support Vector Machine,SLSSVM)的机械故障诊断方法。该方法构建了多层支持向量机(Support Vector Machine,SVM)结构,首先在输入层利用支持向量机对信号进行训练,学习信号的浅层特征,利用“降维公式”生成样本新的表示,并作为隐藏层的输入,隐藏层支持向量机对新样本训练并提取信号的深层特征,逐层学习,最终在输出层输出诊断结果。针对因多层结构带来算法的复杂度以及运行时间增加的问题,采用最小二乘支持向量机(Least Squares Support Vector Machine,LSSVM)技术,并将稀疏化理论与最小二乘支持向量机结合,通过构造特征空间近似最大线性无关向量组对样本进行稀疏表示并依此获得分类判别函数,有效解决了最小二乘支持向量机稀疏性缺乏的问题。最后,通过滚动轴承故障诊断实验验证了该方法的有效性。
基金supported by the National Natural Science Foundation of China(Nos.22078184 and 22378249)the China Postdoctoral Science Foundation(No.2019M653853XB)the Natural Science Advance Research Foundation of Shaanxi University of Science and Technology(No.2018QNBJ-03).
文摘Developing porous self-supporting electrodes with excellent conductivity,good mechanical properties,and high electrochemical activity is crucial for constructing electrode materials with lightweight,ultra-thin,flexible,and high capacitance performance.In this work,we prepared a cellulose nanofibers(CNFs)/carbon nanotubes(CNTs)/vinasse activated carbon(VAC)(CCV)composite material with a multi-layer hierarchical conductive structure through simple vacuum filtration and freeze-drying.In this composite material,the self-assembly of CNF provides the main skeleton structure of a multi-layer hierarchical structure.CNT provides a fast path for the rapid transfer of electrons and is beneficial for the loss of electromagnetic waves.VAC provides sufficient double layer performance.The synergistic effect of the above three endows CCV composite materials with excellent energy storage performance and electromagnetic interference(EMI)shielding performance.In addition,we endowed the CCV composite with a certain shape and performance by introducing a vitrimer polymer with a dynamic cross-linked network structure.In summary,thanks to the synergistic effect of various components in the multi-layer hierarchical structure,CCV composite materials exhibit excellent integration performance,especially stable energy storage performance and EMI shielding performance.These significant properties make CCV composite materials have great application prospects in the fields of energy storage and intelligent EMI shielding.
基金The authors would like to thank Prof.Shu Takagi and Prof.Huaxiong Huang for their instructive comments.The authors would also like to acknowledge Jianda Yang for assisting with FEM simulations.This work was supported by the National Natural Science Foundation of China(Grants 11372191,11232010,11650(Grant 91111138)the National Institute of Health(Grant 2R01DC005642-10A1).
文摘Mechanical stimuli play critical roles in cardiovascular diseases,in which in vivo stresses in blood vessels present a great challenge to predict.Based on the structural-thermal coupled finite element method,we propose a thermal expansion method to estimate stresses in multi-layer blood vessels under healthy and pathological conditions.The proposed method provides a relatively simple and convenient means to predict reliable in vivo mechanical stresses with accurate residual stress.The method is first verified with the opening-up process and the pressure-radius responses for single and multi-layer vessel models.It is then applied to study the stress variation in a human carotid artery at different hypertension stages and in a plaque of vascular stenosis.Our results show that specific or optimal residual stresses exist for different blood pressures,which helps form a homogeneous stress distribution across vessel walls.High elastic shear stress is identified on the shoulder of the plaque,which contributes to the tearing effect in plaque rupture.The present study indicates that the proposed numerical method is a capable and efficient in vivo stress evaluation of patient-specific blood vessels for clinical purposes.
