scholarly journals High Order Moment Model for Polydisperse Evaporating Sprays towards Interfacial Geometry Description

2018 ◽  
Vol 78 (4) ◽  
pp. 2003-2027 ◽  
Author(s):  
Mohamed Essadki ◽  
Stephane de Chaisemartin ◽  
Frédérique Laurent ◽  
Marc Massot
Keyword(s):  
High Order ◽  
Order Moment ◽  
High Order Moment

A high-order moment simulation model

1993 ◽  
Author(s):  
K. T. Tsang ◽  
C. Kostas ◽  
A. Mondelli
Keyword(s):  
Simulation Model ◽  
High Order ◽  
Order Moment ◽  
High Order Moment

Dynamic financial economic fluctuation model based on non-normal distribution

2020 ◽  
pp. 1-10
Author(s):  
Li Wang
Keyword(s):  
Normal Distribution ◽  
High Frequency ◽  
Extreme Value ◽  
High Order ◽  
High Frequency Data ◽  
Order Moment ◽  
Frequency Data ◽  
High Order Moment ◽  
Model Based ◽  
Skewness And Kurtosis

This paper discusses the modeling of financial volatility under the condition of non-normal distribution. In order to solve the problem that the traditional central moment cannot estimate the thick-tailed distribution, the L-moment which is widely used in the hydrological field is introduced, and the autoregressive conditional moment model is used for static and dynamic fitting based on the generalized Pareto distribution. In order to solve the dimension disaster of multidimensional conditional skewness and kurtosis modeling, the multidimensional skewness and kurtosis model based on distribution is established, and the high-order moment model is deduced. Finally, the problems existing in the traditional investment portfolio are discussed, and on this basis, the high-order moment portfolio is further studied. The results show that the key lies in the selection of the model and the assumption of asset probability distribution. Financial risk analysis can be effective only with a large sample. High-frequency data contain more information and can provide rich data resources. The conditional generalized extreme value distribution can well describe the time-varying characteristics of scale parameters and shape parameters and capture the conditional heteroscedasticity in the high-frequency extreme value time series. Better describe the persistence and aggregation of the extreme value of high frequency data as well as the peak and thick tail characteristics of its distribution.

scholarly journals Inverse parametric uncertainty identification using polynomial chaos and high-order moment matching benchmarked on a wet friction clutch

Mechatronics ◽  
2020 ◽  
Vol 65 ◽  
pp. 102320
Author(s):  
Wannes De Groote ◽  
Tom Lefebvre ◽  
Georges Tod ◽  
Nele De Geeter ◽  
Bruno Depraetere ◽  
...  
Keyword(s):  
Polynomial Chaos ◽  
Parametric Uncertainty ◽  
High Order ◽  
Order Moment ◽  
Moment Matching ◽  
High Order Moment ◽  
Wet Friction ◽  
Friction Clutch

Simulation of thermal transpiration flow using a high-order moment method

2014 ◽  
Vol 25 (11) ◽  
pp. 1450061 ◽  
Author(s):  
Qiang Sheng ◽  
Gui-Hua Tang ◽  
Xiao-Jun Gu ◽  
David R. Emerson ◽  
Yong-Hao Zhang
Keyword(s):  
Moment Method ◽  
Direct Simulation Monte Carlo ◽  
High Order ◽  
Navier Stokes ◽  
Order Moment ◽  
Surface Area Ratio ◽  
High Order Moment ◽  
Thermal Transpiration ◽  
Extended Thermodynamic ◽  
Knudsen Pump

Nonequilibrium thermal transpiration flow is numerically analyzed by an extended thermodynamic approach, a high-order moment method. The captured velocity profiles of temperature-driven flow in a parallel microchannel and in a micro-chamber are compared with available kinetic data or direct simulation Monte Carlo (DSMC) results. The advantages of the high-order moment method are shown as a combination of more accuracy than the Navier–Stokes–Fourier (NSF) equations and less computation cost than the DSMC method. In addition, the high-order moment method is also employed to simulate the thermal transpiration flow in complex geometries in two types of Knudsen pumps. One is based on micro-mechanized channels, where the effect of different wall temperature distributions on thermal transpiration flow is studied. The other relies on porous structures, where the variation of flow rate with a changing porosity or pore surface area ratio is investigated. These simulations can help to optimize the design of a real Knudsen pump.

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