Optimal discretization of grounding systems applying Maxwell’s subareas method

2018 ◽  
Author(s):  
Pasquale Montegiglio ◽  
Giuseppe Cafaro ◽  
Francesco Torelli ◽  
Pietro Colella ◽  
Enrico Pons
Keyword(s):  
Optimal Discretization

A fast algorithm for the adaptive discretization of 3D parametric curves

2019 ◽  
Vol 37 (5) ◽  
pp. 1663-1682
Author(s):  
Jianming Zhang ◽  
Chuanming Ju ◽  
Baotao Chi
Keyword(s):  
Mesh Generation ◽  
Fast Algorithm ◽  
Design Methodology ◽  
Three Dimensional ◽  
Parametric Curves ◽  
Content Type ◽  
Gaussian Integration ◽  
Optimal Discretization ◽  
Adaptive Discretization ◽  
Conventional Algorithm

Purpose The purpose of this paper is to present a fast algorithm for the adaptive discretization of three-dimensional parametric curves. Design/methodology/approach The proposed algorithm computes the parametric increments of all segments to obtain the parametric coordinates of all discrete nodes. This process is recursively applied until the optimal discretization of curves is obtained. The parametric increment of a segment is inversely proportional to the number of sub-segments, which can be subdivided, and the sum of parametric increments of all segments is constant. Thus, a new expression for parametric increment of a segment can be obtained. In addition, the number of sub-segments, which a segment can be subdivided is calculated approximately, thus avoiding Gaussian integration. Findings The proposed method can use less CPU time to perform the optimal discretization of three-dimensional curves. The results of curves discretization can also meet requirements for mesh generation used in the preprocessing of numerical simulation. Originality/value Several numerical examples presented have verified the robustness and efficiency of the proposed algorithm. Compared with the conventional algorithm, the more complex the model, the more time the algorithm saves in the process of curve discretization.

Optimal Discretization of Random Fields

1993 ◽  
Vol 119 (6) ◽  
pp. 1136-1154 ◽  
Author(s):  
Chun‐Ching Li ◽  
A. Der Kiureghian
Keyword(s):  
Random Fields ◽  
Optimal Discretization

Optimal discretization for geographical detectors-based risk assessment

2013 ◽  
Vol 50 (1) ◽  
pp. 78-92 ◽  
Author(s):  
Feng Cao ◽  
Yong Ge ◽  
Jin-Feng Wang
Keyword(s):  
Risk Assessment ◽  
Optimal Discretization

A Study Toward Minimum Spatial Discretization of a Fuel Cell Dynamics Model

2006 ◽  
Author(s):  
B. A. McCain ◽  
A. G. Stefanopoulou ◽  
K. R. Butts
Keyword(s):  
Fuel Cell ◽  
Cell Model ◽  
Minimum Degree ◽  
Water Dynamics ◽  
Vapor Concentration ◽  
Dynamics Model ◽  
Final Time ◽  
Cell Dynamics ◽  
Model Order ◽  
Optimal Discretization

The 24-state fuel cell water dynamics model from [1] is cast into a Dymola™ icon-based formulation, with flexible library sub-models specific for this application. Through and across (flow and effort in bond graph terminology) variables are identified and analyzed for all relevant energy-using components. The objective is to establish the necessary model order for the fuel cell model using an energy-based measure called activity [2]. Additionally, we analyze the effect that input variation (duration, initial/final time) has on calculation and implementation of the activity. Explanation of the importance of accurate water vapor concentration gradient modeling is covered. Finally, we show that the minimum degree of discretization for each constituent within the model should be determined separately in order to generate the simplest model representation, and that the optimal discretization can be different for each species.

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