Solution of general dynamic equation for nanoparticles in turbulent flow considering fluctuating coagulation

2016 ◽  
Vol 37 (10) ◽  
pp. 1275-1288 ◽  
Author(s):  
Jianzhong Lin ◽  
Xiaojun Pan ◽  
Zhaoqin Yin ◽  
Xiaoke Ku
Keyword(s):  
Turbulent Flow ◽  
Dynamic Equation ◽  
General Dynamic

A New Method and Automatic Generation for Dynamic Analysis of Complex Planar Mechanisms Based on the Ordered Single-Opened-Chains

1994 ◽  
Author(s):  
Jian-Qing Zhang ◽  
Ting-Li Yang
Keyword(s):  
Dynamic Analysis ◽  
Dynamic Equation ◽  
Automatic Generation ◽  
The Other ◽  
New Method ◽  
Complex Mechanism ◽  
Planar Mechanisms ◽  
Spatial Mechanisms ◽  
General Dynamic

Abstract This work presents a new method for kinetostatic analysis and dynamic analysis of complex planar mechanisms, i.e. the ordered single-opened-chains method. This method makes use of the ordered single-opened chains (in short, SOC,) along with the properties of SOC, and the network constraints relationship between SOC,. By this method, any planar complex mechanism can be automatically decomposed into a series of the ordered single-opened chains and the optimal structural decomposition route (s) can be automatically selected for dynamic analysis, the paper present the dynamic equation which can be used to solve both the kinetostatic problem and the general dynamic problem. The main advantage of the proposed approach is the possibility to reduce the number of equations to be solved simultaneously to the minimum, and its high automation as well. The other advantage is the simplification of the determination of the coefficients in the equations, and thus it maybe result in a much less time-consuming algorthem. The proposed approach is illustrated with three examples. The presented method can be easily extended to the dynamic analysis of spatial mechanisms.

scholarly journals Retrieval of process rate parameters in the general dynamic equation for aerosols using Bayesian state estimation: BAYROSOL1.0

2021 ◽  
Vol 14 (6) ◽  
pp. 3715-3739
Author(s):  
Matthew Ozon ◽  
Aku Seppänen ◽  
Jari P. Kaipio ◽  
Kari E. J. Lehtinen
Keyword(s):  
State Estimation ◽  
Size Distribution ◽  
Dynamic Equation ◽  
Radiative Forcing ◽  
Quality Data ◽  
Growth Processes ◽  
Computational Framework ◽  
High Quality Data ◽  
Aerosol Process ◽  
General Dynamic

Abstract. The uncertainty in the radiative forcing caused by aerosols and its effect on climate change calls for research to improve knowledge of the aerosol particle formation and growth processes. While experimental research has provided a large amount of high-quality data on aerosols over the last 2 decades, the inference of the process rates is still inadequate, mainly due to limitations in the analysis of data. This paper focuses on developing computational methods to infer aerosol process rates from size distribution measurements. In the proposed approach, the temporal evolution of aerosol size distributions is modeled with the general dynamic equation (GDE) equipped with stochastic terms that account for the uncertainties of the process rates. The time-dependent particle size distribution and the rates of the underlying formation and growth processes are reconstructed based on time series of particle analyzer data using Bayesian state estimation – which not only provides (point) estimates for the process rates but also enables quantification of their uncertainties. The feasibility of the proposed computational framework is demonstrated by a set of numerical simulation studies.

Sign in / Sign up

Export Citation Format

Share Document