A simple quasi-geostrophic barotropic vorticity equation model is used as the dynamic frame of the model in this paper.Considering that there are many random errors in model's initial values of meteorolo- gical da...A simple quasi-geostrophic barotropic vorticity equation model is used as the dynamic frame of the model in this paper.Considering that there are many random errors in model's initial values of meteorolo- gical data,and that it is not perfectly complete about model's physical processes (for example,take no ac- count of the interaction between atmosphere and underlying surface,radiation,etc.),we add the random for- ced term to the model and use the Monte-Carlo method with random initial values.A statistical-dynamic integrated model is thus built up,and a numerical forecasting experiment of 500hPa monthly mean height field of January 1983 has been carried out.The experiment result proves that the forecasting result of the model, considering random forcing and random initial values at the same time,is better than that by the pure dynamic model,the random initial value model and the random forced model.展开更多
In 1974, in order to overcome the difficulty in the moment method, Leith put forward a statistical-dynamic integrated Monte-Carlo (M-C) approximate forecasting method, and used a homogeneity and turbulence two-dimensi...In 1974, in order to overcome the difficulty in the moment method, Leith put forward a statistical-dynamic integrated Monte-Carlo (M-C) approximate forecasting method, and used a homogeneity and turbulence two-dimensional model to prove that in minimum展开更多
A stochastic discrete model is proposed for simulating the dynamic behaviors of gas-solid systems. In the model, the motions of solid phase are obtained by calcu-lating individual particle motions while gas flow is ob...A stochastic discrete model is proposed for simulating the dynamic behaviors of gas-solid systems. In the model, the motions of solid phase are obtained by calcu-lating individual particle motions while gas flow is obtained by solving the Navier-Stokes equation including two-phase interaction. For the calculation of solid phase,the motion process of each particle is decomposed into the collision process and suspension process. Momentum conservation of collision mechanics controls the interaction between colliding particles, while the state of each suspended particle is fully dominated by the equation of force balance over that particle. Inaddition to gravity, drag force and pressure, other unclear factors are described as random force in the suspension process. As a result, the proposed model has given some nu-merical simulations of gas-solid systems, in which different random forces are used.It indicates that the stochastic discrete model can be used to simulate qualitatively the dynamic behaviors of gas-solid two-phase flow.展开更多
This paper is devoted to exploring approaches to understanding the stochastic characteristics of particle-fluid two-phase flow. By quantifying the forces dominating the particle motion and modelling the less important...This paper is devoted to exploring approaches to understanding the stochastic characteristics of particle-fluid two-phase flow. By quantifying the forces dominating the particle motion and modelling the less important and/or unclear forces as random forces, a stochastic differential equation is proposed to describe the complex behavior of a particle motion. An exploratory simulation has shown satisfactory agreement with phase doppler particle analyzer (PDPA) measurements, which indicates that stochastic analysis is a potential approach for revealing the details of particle-fluid flow phenomena.展开更多
文摘A simple quasi-geostrophic barotropic vorticity equation model is used as the dynamic frame of the model in this paper.Considering that there are many random errors in model's initial values of meteorolo- gical data,and that it is not perfectly complete about model's physical processes (for example,take no ac- count of the interaction between atmosphere and underlying surface,radiation,etc.),we add the random for- ced term to the model and use the Monte-Carlo method with random initial values.A statistical-dynamic integrated model is thus built up,and a numerical forecasting experiment of 500hPa monthly mean height field of January 1983 has been carried out.The experiment result proves that the forecasting result of the model, considering random forcing and random initial values at the same time,is better than that by the pure dynamic model,the random initial value model and the random forced model.
文摘In 1974, in order to overcome the difficulty in the moment method, Leith put forward a statistical-dynamic integrated Monte-Carlo (M-C) approximate forecasting method, and used a homogeneity and turbulence two-dimensional model to prove that in minimum
基金National Natural Science Foundation of China for Young Scientists of China(Grant No.11701592)the Joint Funds of the National Natural Science Foundation of China(Grant No.U1811263)Guangdong Key Laboratory of Big Data Analysis and Processing~~
文摘A stochastic discrete model is proposed for simulating the dynamic behaviors of gas-solid systems. In the model, the motions of solid phase are obtained by calcu-lating individual particle motions while gas flow is obtained by solving the Navier-Stokes equation including two-phase interaction. For the calculation of solid phase,the motion process of each particle is decomposed into the collision process and suspension process. Momentum conservation of collision mechanics controls the interaction between colliding particles, while the state of each suspended particle is fully dominated by the equation of force balance over that particle. Inaddition to gravity, drag force and pressure, other unclear factors are described as random force in the suspension process. As a result, the proposed model has given some nu-merical simulations of gas-solid systems, in which different random forces are used.It indicates that the stochastic discrete model can be used to simulate qualitatively the dynamic behaviors of gas-solid two-phase flow.
文摘This paper is devoted to exploring approaches to understanding the stochastic characteristics of particle-fluid two-phase flow. By quantifying the forces dominating the particle motion and modelling the less important and/or unclear forces as random forces, a stochastic differential equation is proposed to describe the complex behavior of a particle motion. An exploratory simulation has shown satisfactory agreement with phase doppler particle analyzer (PDPA) measurements, which indicates that stochastic analysis is a potential approach for revealing the details of particle-fluid flow phenomena.
