The coupled models of LBM （Lattice Boltzmann Method） and RANS （Reynolds-Averaged Navier-Stokes） are more practical for the transient simulation of mixing processes at large spatial and temporal scales such as crud...The coupled models of LBM （Lattice Boltzmann Method） and RANS （Reynolds-Averaged Navier-Stokes） are more practical for the transient simulation of mixing processes at large spatial and temporal scales such as crude oil mixing in large-diameter storage tanks. To keep the efficiency of parallel computation of LBM, the RANS model should also be explicitly solved; whereas to keep the numerical stability the implicit method should be better for PANS model. This article explores the numerical stability of explicit methods in 2D cases on one hand, and on the other hand how to accelerate the computation of the coupled model of LBM and an implicitly solved RANS model in 3D cases. To ensure the numerical stability and meanwhile avoid the use of empirical artificial lim- itations on turbulent quantities in 2D cases, we investigated the impacts of collision models in LBM （LBGK, MRT） and the numerical schemes for convection terms （WENO, TVD） and production terms （FDM, NEQM） in an explic- itly solved standard k-e model. The combination of MRT and TVD or MRT and NEQM can be screened out for the 2D simulation of backward-facing step flow even at Re = 107. This scheme combination, however, may still not guarantee the numerical stability in 3D cases and hence much finer grids are required, which is not suitable for the simulation of industrial-scale processes.Then we proposed a new method to accelerate the coupled model of LBM with RANS （implicitly solved）. When implemented on multiple GPUs, this new method can achieve 13.5-fold accelera- tion relative to the original coupled model and 40-fold acceleration compared to the traditional CFD simulation based on Finite Volume （FV） method accelerated by multiple CPUs. This study provides the basis for the transient flow simulation of larger spatial and temporal scales in industrial applications with LBM-RANS methods.展开更多
Tandem learning via email and computer-mediated communication activities flanked standard frontal English lessons for Italian adult learners in Italy. It is here suggested that the combination of tandem learning, vari...Tandem learning via email and computer-mediated communication activities flanked standard frontal English lessons for Italian adult learners in Italy. It is here suggested that the combination of tandem learning, various communication activities performed on-line, and standard courses help mature students to boost their self-esteem, give them more autonomy, and enhance their motivation. Tandem learning, which is based on the principles of reciprocity and autonomy, is ideally suited for adult students. It helps overcome the problems connected to the affective reactions of adults, and at the same time, being content driven, offers a more natural setting to learn an L2 （second language） on a mutual supportive basis.展开更多
31 cases of atherosclerosis (AS) were treated with Jiang Zhi Tong Mai Fang ([symbol: see text], formula of JZTMF), and its effect was compared with 30 cases treated with lovastatin in the control group. Clinically, th...31 cases of atherosclerosis (AS) were treated with Jiang Zhi Tong Mai Fang ([symbol: see text], formula of JZTMF), and its effect was compared with 30 cases treated with lovastatin in the control group. Clinically, the JZTMF formula showed an effect of regulating blood lipids, and therefore it was antiatherosclerotic. The mechanism is, probably, restoration of the function of endothelial cells (EC) by increasing the synthesis of 6-keto-PGF1 alpha and decreasing the release of endothelin (ET) as evidenced in the experimental study.展开更多
The evolutionary dynamics of behavioral traits reflect phenotypic and genetic changes. Methodological difficulties in analyzing the genetic dynamics of complex traits have left open questions on the mechanisms that ha...The evolutionary dynamics of behavioral traits reflect phenotypic and genetic changes. Methodological difficulties in analyzing the genetic dynamics of complex traits have left open questions on the mechanisms that have shaped complex beha- viors and cognitive abilities. A strategy to investigate the change of behavior across generations is to assume that genetic con- straints have a negligible role in evolution （the phenotypic gambit） and focus on the phenotype as a proxy for genetic evolution. Empirical evidence and technologic advances in genomics question the choice of neglecting the genetic underlying the dynamics of behavioral evolution. I first discuss the relevance of genetic factors - e.g. genetic variability, genetic linkage, gene interactions - in shaping evolution, showing the importance of taking genetic factors into account when dealing with evolutionary dynamics. I subsequently describe the recent advancements in genetics and genomics that make the investigation of the ongoing evolutionary process of behavioral traits finally attainable. In particular, by applying genomic resequencing to experimental evolution - a me- thod called Evolve ＆ Resequence - it is possible to monitor at the same time phenotypic and genomie changes in populations exposed to controlled selective pressures. Experimental evolution of associative learning, a well-known trait that promptly re- sponds to selection, is a convenient model to illustrate this approach applied to behavior and cognition. Taking into account the recent achievements of the field, I discuss how to design and conduct an effective Evolve ＆ Resequence study on associative learning in Drosophila. By integrating phenotypic and genomic data in the investigation of evolutionary dynamics, new insights can be gained on longstanding questions such as the modularity of mind and its evolution .展开更多
基金Supported by the National Key Research and Development Program of China(2017YFB0602500)National Natural Science Foundation of China(91634203 and91434121)Chinese Academy of Sciences(122111KYSB20150003)
文摘The coupled models of LBM （Lattice Boltzmann Method） and RANS （Reynolds-Averaged Navier-Stokes） are more practical for the transient simulation of mixing processes at large spatial and temporal scales such as crude oil mixing in large-diameter storage tanks. To keep the efficiency of parallel computation of LBM, the RANS model should also be explicitly solved; whereas to keep the numerical stability the implicit method should be better for PANS model. This article explores the numerical stability of explicit methods in 2D cases on one hand, and on the other hand how to accelerate the computation of the coupled model of LBM and an implicitly solved RANS model in 3D cases. To ensure the numerical stability and meanwhile avoid the use of empirical artificial lim- itations on turbulent quantities in 2D cases, we investigated the impacts of collision models in LBM （LBGK, MRT） and the numerical schemes for convection terms （WENO, TVD） and production terms （FDM, NEQM） in an explic- itly solved standard k-e model. The combination of MRT and TVD or MRT and NEQM can be screened out for the 2D simulation of backward-facing step flow even at Re = 107. This scheme combination, however, may still not guarantee the numerical stability in 3D cases and hence much finer grids are required, which is not suitable for the simulation of industrial-scale processes.Then we proposed a new method to accelerate the coupled model of LBM with RANS （implicitly solved）. When implemented on multiple GPUs, this new method can achieve 13.5-fold accelera- tion relative to the original coupled model and 40-fold acceleration compared to the traditional CFD simulation based on Finite Volume （FV） method accelerated by multiple CPUs. This study provides the basis for the transient flow simulation of larger spatial and temporal scales in industrial applications with LBM-RANS methods.
文摘Tandem learning via email and computer-mediated communication activities flanked standard frontal English lessons for Italian adult learners in Italy. It is here suggested that the combination of tandem learning, various communication activities performed on-line, and standard courses help mature students to boost their self-esteem, give them more autonomy, and enhance their motivation. Tandem learning, which is based on the principles of reciprocity and autonomy, is ideally suited for adult students. It helps overcome the problems connected to the affective reactions of adults, and at the same time, being content driven, offers a more natural setting to learn an L2 （second language） on a mutual supportive basis.
文摘31 cases of atherosclerosis (AS) were treated with Jiang Zhi Tong Mai Fang ([symbol: see text], formula of JZTMF), and its effect was compared with 30 cases treated with lovastatin in the control group. Clinically, the JZTMF formula showed an effect of regulating blood lipids, and therefore it was antiatherosclerotic. The mechanism is, probably, restoration of the function of endothelial cells (EC) by increasing the synthesis of 6-keto-PGF1 alpha and decreasing the release of endothelin (ET) as evidenced in the experimental study.
文摘The evolutionary dynamics of behavioral traits reflect phenotypic and genetic changes. Methodological difficulties in analyzing the genetic dynamics of complex traits have left open questions on the mechanisms that have shaped complex beha- viors and cognitive abilities. A strategy to investigate the change of behavior across generations is to assume that genetic con- straints have a negligible role in evolution （the phenotypic gambit） and focus on the phenotype as a proxy for genetic evolution. Empirical evidence and technologic advances in genomics question the choice of neglecting the genetic underlying the dynamics of behavioral evolution. I first discuss the relevance of genetic factors - e.g. genetic variability, genetic linkage, gene interactions - in shaping evolution, showing the importance of taking genetic factors into account when dealing with evolutionary dynamics. I subsequently describe the recent advancements in genetics and genomics that make the investigation of the ongoing evolutionary process of behavioral traits finally attainable. In particular, by applying genomic resequencing to experimental evolution - a me- thod called Evolve ＆ Resequence - it is possible to monitor at the same time phenotypic and genomie changes in populations exposed to controlled selective pressures. Experimental evolution of associative learning, a well-known trait that promptly re- sponds to selection, is a convenient model to illustrate this approach applied to behavior and cognition. Taking into account the recent achievements of the field, I discuss how to design and conduct an effective Evolve ＆ Resequence study on associative learning in Drosophila. By integrating phenotypic and genomic data in the investigation of evolutionary dynamics, new insights can be gained on longstanding questions such as the modularity of mind and its evolution .
