Wall-bounded turbulent flow involves the development of multi-scale turbulent eddies, as well as a sharply varying boundary layer. Its theoretical descriptions are yet phenomenological. We present here a new framework...Wall-bounded turbulent flow involves the development of multi-scale turbulent eddies, as well as a sharply varying boundary layer. Its theoretical descriptions are yet phenomenological. We present here a new framework called structural ensemble dynamics （SED）, which aims at using systematically all relevant statistical properties of turbulent structures for a quantitative description of ensemble means. A new set of closure equations based on the SED approach for a turbulent channel flow is presented. SED order functions are defined, and numerically determined from data of direct numerical simulations （DNS）. Computational results show that the new closure model reproduces accurately the solution of the original Navier-Stokes simulation, including the mean velocity profile, the kinetic energy of the streamwise velocity component, and every term in the energy budget equation. It is suggested that the SED-based studies of turbulent structure builds a bridge between the studies of physical mechanisms of turbulence and the development of accurate model equations for engineering predictions.展开更多
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 framework is general,independent of the flow system to study, while the actual functional form of the order functions may vary from flow to flow.We assert that as the knowledge of order functions is accumulated and as more flows are analyzed, new principles（such as hierarchy,symmetry,group invariance,etc.） governing the role of turbulent structures in the mean flow properties will be clarified and a viable theory of turbulence might emerge.展开更多
We report the results of accurate prediction of lift(C L)and drag(C D)coefficients of two typical airfoil flows(NACA0012 and RAE2822)by a new algebraic turbulence model,in which the eddy viscosity is specified by a st...We report the results of accurate prediction of lift(C L)and drag(C D)coefficients of two typical airfoil flows(NACA0012 and RAE2822)by a new algebraic turbulence model,in which the eddy viscosity is specified by a stress length(SL)function predicted by structural ensemble dynamics(SED)theory.Unprecedented accuracy of the prediction of C D with error of a few counts(one count is 10−4)and of C L with error under 1%-2%are uniformly obtained for varying angles of attack(AoA),indicating an order of magnitude improvement of drag prediction accuracy compared to currently used models(typically around 20 to 30 counts).More interestingly,the SED-SL model is distinguished with fewer parameters of clear physical meaning,which quantify underlying turbulent boundary layer(TBL)with a universal multi-layer structure,and is thus promising to be more easily generalizable to complex TBL.The use of the new model for the calibration of flow condition in experiment and the extraction of flow physics from numerical simulation data of aeronautic flows are discussed.展开更多
Despite being one of the oldest and most widely-used turbulence models in engineering computational fluid dynamics(CFD),the k-ωmodel has not been fully understood theoretically because of its high nonlinearity and co...Despite being one of the oldest and most widely-used turbulence models in engineering computational fluid dynamics(CFD),the k-ωmodel has not been fully understood theoretically because of its high nonlinearity and complex model parameter setting.Here,a multi-layer analytic expression is postulated for two lengths(stress and kinetic energy lengths),yielding an analytic solution for the k-ωmodel equations in pipe flow.Approximate local balance equations are analyzed to determine the key parameters in the solution,which are shown to be rather close to the empirically-measured values from the numerical solution of the Wilcox k-ωmodel,and hence the analytic construction is fully validated.The results provide clear evidence that the k-ωmodel sets in it a multilayer structure,which is similar to but different,in some insignificant details,from the Navier-Stokes(N-S)turbulence.This finding explains why the k-ωmodel is so popular,especially in computing the near-wall flow.Finally,the analysis is extended to a newlyrefined k-ωmodel called the structural ensemble dynamics(SED)k-ωmodel,showing that the SED k-ωmodel has improved the multi-layer structure in the outer flow but preserved the setting of the k-ωmodel in the inner region.展开更多
Three-phase pulse width modulation converters using insulated gate bipolar transistors(IGBTs)have been widely used in industrial application.However,faults in IGBTs can severely affect the operation and safety of the ...Three-phase pulse width modulation converters using insulated gate bipolar transistors(IGBTs)have been widely used in industrial application.However,faults in IGBTs can severely affect the operation and safety of the power electronics equipment and loads.For ensuring system reliability,it is necessary to accurately detect IGBT faults accurately as soon as their occurrences.This paper proposes a diagnosis method based on data-driven theory.A novel randomized learning technology,namely extreme learning machine(ELM)is adopted into historical data learning.Ensemble classifier structure is used to improve diagnostic accuracy.Finally,time window is defined to illustrate the relevance between diagnostic accuracy and data sampling time.By this mean,an appropriate time window is achieved to guarantee a high accuracy with relatively short decision time.Compared to other traditional methods,ELM has a better classification performance.Simulation tests validate the proposed ELM ensemble diagnostic performance.展开更多
We propose a method based on the local breeding of growing modes(LBGM) considering strong local weather characteristics for convection-allowing ensemble forecasting. The impact radius was introduced in the breeding of...We propose a method based on the local breeding of growing modes(LBGM) considering strong local weather characteristics for convection-allowing ensemble forecasting. The impact radius was introduced in the breeding of growing modes to develop the LBGM method. In the local breeding process, the ratio between the root mean square error(RMSE) of local space forecast at each grid point and that of the initial full-field forecast is computed to rescale perturbations. Preliminary evaluations of the method based on a nature run were performed in terms of three aspects: perturbation structure, spread,and the RMSE of the forecast. The experimental results confirm that the local adaptability of perturbation schemes improves after rescaling by the LBGM method. For perturbation physical variables and some near-surface meteorological elements, the LBGM method could increase the spread and reduce the RMSE of forecast,improving the performance of the ensemble forecast system.In addition, different from those existing methods of global orthogonalization approach, this new initial-condition perturbation method takes into full consideration the local characteristics of the convective-scale weather system, thus making convectionallowing ensemble forecast more accurate.展开更多
Background:RNA secondary structures play a pivotal role in posttranscriptional regulation and the functions of non-coding RNAs,yet in vivo RNA secondary structures remain enigmatic.PARIS(Psoralen Analysis of RNA Inter...Background:RNA secondary structures play a pivotal role in posttranscriptional regulation and the functions of non-coding RNAs,yet in vivo RNA secondary structures remain enigmatic.PARIS(Psoralen Analysis of RNA Interactions and Structures)is a recently developed high-throughput sequencing-based approach that enables direct capture of RNA duplex structures in vivo.However,the existence of incompatible,fuzzy pairing information obstructs the integration of PARIS data with the existing tools for reconstructing RNA secondary structure models at the single-base resolution.Methods:We introduce IRIS,a method for predicting RNA secondary structure ensembles based on PARIS data.IRIS generates a large set of candidate RNA secondary structure models under the guidance of redistributed PARIS reads and then uses a Bayesian model to identify the optimal ensemble,according to both thermodynamic principles and PARIS data.Results:The predicted RNA structure ensembles by IRIS have been verified based on evolutionary conservation information and consistency with other experimental RNA structural data.HIS is implemented in Python and freely available at http://iris.zhanglab.net.Conclusion:IRIS capitalizes upon PARIS data to improve the prediction of in vivo RNA secondary structure ensembles.We expect that IRIS will enhance the application of the PARIS technology and shed more insight on in vivo RNA secondary structures.展开更多
基金supported by the National Natural Science Foundation of China (90716008)the MOST under 973 project (2009CB724100)
文摘Wall-bounded turbulent flow involves the development of multi-scale turbulent eddies, as well as a sharply varying boundary layer. Its theoretical descriptions are yet phenomenological. We present here a new framework called structural ensemble dynamics （SED）, which aims at using systematically all relevant statistical properties of turbulent structures for a quantitative description of ensemble means. A new set of closure equations based on the SED approach for a turbulent channel flow is presented. SED order functions are defined, and numerically determined from data of direct numerical simulations （DNS）. Computational results show that the new closure model reproduces accurately the solution of the original Navier-Stokes simulation, including the mean velocity profile, the kinetic energy of the streamwise velocity component, and every term in the energy budget equation. It is suggested that the SED-based studies of turbulent structure builds a bridge between the studies of physical mechanisms of turbulence and the development of accurate model equations for engineering predictions.
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 framework is general,independent of the flow system to study, while the actual functional form of the order functions may vary from flow to flow.We assert that as the knowledge of order functions is accumulated and as more flows are analyzed, new principles（such as hierarchy,symmetry,group invariance,etc.） governing the role of turbulent structures in the mean flow properties will be clarified and a viable theory of turbulence might emerge.
文摘We report the results of accurate prediction of lift(C L)and drag(C D)coefficients of two typical airfoil flows(NACA0012 and RAE2822)by a new algebraic turbulence model,in which the eddy viscosity is specified by a stress length(SL)function predicted by structural ensemble dynamics(SED)theory.Unprecedented accuracy of the prediction of C D with error of a few counts(one count is 10−4)and of C L with error under 1%-2%are uniformly obtained for varying angles of attack(AoA),indicating an order of magnitude improvement of drag prediction accuracy compared to currently used models(typically around 20 to 30 counts).More interestingly,the SED-SL model is distinguished with fewer parameters of clear physical meaning,which quantify underlying turbulent boundary layer(TBL)with a universal multi-layer structure,and is thus promising to be more easily generalizable to complex TBL.The use of the new model for the calibration of flow condition in experiment and the extraction of flow physics from numerical simulation data of aeronautic flows are discussed.
基金the National Numerical Wind Tunnel(No.NNW2019ZT1-A03)the National Natural Science Foundation of China(Nos.91952201,11372008,and 11452002)。
文摘Despite being one of the oldest and most widely-used turbulence models in engineering computational fluid dynamics(CFD),the k-ωmodel has not been fully understood theoretically because of its high nonlinearity and complex model parameter setting.Here,a multi-layer analytic expression is postulated for two lengths(stress and kinetic energy lengths),yielding an analytic solution for the k-ωmodel equations in pipe flow.Approximate local balance equations are analyzed to determine the key parameters in the solution,which are shown to be rather close to the empirically-measured values from the numerical solution of the Wilcox k-ωmodel,and hence the analytic construction is fully validated.The results provide clear evidence that the k-ωmodel sets in it a multilayer structure,which is similar to but different,in some insignificant details,from the Navier-Stokes(N-S)turbulence.This finding explains why the k-ωmodel is so popular,especially in computing the near-wall flow.Finally,the analysis is extended to a newlyrefined k-ωmodel called the structural ensemble dynamics(SED)k-ωmodel,showing that the SED k-ωmodel has improved the multi-layer structure in the outer flow but preserved the setting of the k-ωmodel in the inner region.
文摘Three-phase pulse width modulation converters using insulated gate bipolar transistors(IGBTs)have been widely used in industrial application.However,faults in IGBTs can severely affect the operation and safety of the power electronics equipment and loads.For ensuring system reliability,it is necessary to accurately detect IGBT faults accurately as soon as their occurrences.This paper proposes a diagnosis method based on data-driven theory.A novel randomized learning technology,namely extreme learning machine(ELM)is adopted into historical data learning.Ensemble classifier structure is used to improve diagnostic accuracy.Finally,time window is defined to illustrate the relevance between diagnostic accuracy and data sampling time.By this mean,an appropriate time window is achieved to guarantee a high accuracy with relatively short decision time.Compared to other traditional methods,ELM has a better classification performance.Simulation tests validate the proposed ELM ensemble diagnostic performance.
基金supported by the Natural Science Foundation of Nanjing Joint Center of Atmospheric Research(Grant Nos.NJCAR2016MS02 and NJCAR2016ZD04)the National Natural Science Foundation of China(Grant Nos.41205073 and41675007)the National Key Research and Development Program of China(Grant No.2017YFC1501800)
文摘We propose a method based on the local breeding of growing modes(LBGM) considering strong local weather characteristics for convection-allowing ensemble forecasting. The impact radius was introduced in the breeding of growing modes to develop the LBGM method. In the local breeding process, the ratio between the root mean square error(RMSE) of local space forecast at each grid point and that of the initial full-field forecast is computed to rescale perturbations. Preliminary evaluations of the method based on a nature run were performed in terms of three aspects: perturbation structure, spread,and the RMSE of the forecast. The experimental results confirm that the local adaptability of perturbation schemes improves after rescaling by the LBGM method. For perturbation physical variables and some near-surface meteorological elements, the LBGM method could increase the spread and reduce the RMSE of forecast,improving the performance of the ensemble forecast system.In addition, different from those existing methods of global orthogonalization approach, this new initial-condition perturbation method takes into full consideration the local characteristics of the convective-scale weather system, thus making convectionallowing ensemble forecast more accurate.
基金the Chinese Ministry of Science and Technology(No.2018YFA0107603 to Q.C.Z.)the National Natural Science Foundation ofChina(Nos.91740204 and 31761163007 to Q.C.Z.)+1 种基金the National Natural Science Foundation of China(No.61772197 to T.J.)the National Key Research and Development Program of China(No.2018YFC0910404 to T.J.)。
文摘Background:RNA secondary structures play a pivotal role in posttranscriptional regulation and the functions of non-coding RNAs,yet in vivo RNA secondary structures remain enigmatic.PARIS(Psoralen Analysis of RNA Interactions and Structures)is a recently developed high-throughput sequencing-based approach that enables direct capture of RNA duplex structures in vivo.However,the existence of incompatible,fuzzy pairing information obstructs the integration of PARIS data with the existing tools for reconstructing RNA secondary structure models at the single-base resolution.Methods:We introduce IRIS,a method for predicting RNA secondary structure ensembles based on PARIS data.IRIS generates a large set of candidate RNA secondary structure models under the guidance of redistributed PARIS reads and then uses a Bayesian model to identify the optimal ensemble,according to both thermodynamic principles and PARIS data.Results:The predicted RNA structure ensembles by IRIS have been verified based on evolutionary conservation information and consistency with other experimental RNA structural data.HIS is implemented in Python and freely available at http://iris.zhanglab.net.Conclusion:IRIS capitalizes upon PARIS data to improve the prediction of in vivo RNA secondary structure ensembles.We expect that IRIS will enhance the application of the PARIS technology and shed more insight on in vivo RNA secondary structures.
