Particle-Based Device Simulation Methods

2017 ◽  
pp. 241-334
Author(s):  
Dragica Vasileska ◽  
Stephen M. Goodnick ◽  
Gerhard Klimeck
2011 ◽  
Vol 63-64 ◽  
pp. 209-213 ◽  
Author(s):  
Limin Chen

Dynamic performance of belt conveyor has become increasingly prominent, so accurate design techniques should be adopted, thus the dynamic simulation methods are required to research precisely the starting performances of. This paper selects the viscoelasticity model suiting to the belt by comparing dynamic performance of some kinds of models, and develops simulation model of driving device with closed-loop controlling. The simulation model with simple structure, the high stability, the reliable working and the convenient adjustment indicates that rotation speed and output moment of motor change with load and working time, therefore really reflectes dynamic work process of the driving device. Simulation results are more close to practice.


Author(s):  
Yoshimichi YAMAMOTO ◽  
Maiki HAYAKAWA ◽  
S Masihullah AHMADI

Author(s):  
Vasily Bulatov ◽  
Wei Cai

This book presents a broad collection of models and computational methods - from atomistic to continuum - applied to crystal dislocations. Its purpose is to help students and researchers in computational materials sciences to acquire practical knowledge of relevant simulation methods. Because their behavior spans multiple length and time scales, crystal dislocations present a common ground for an in-depth discussion of a variety of computational approaches, including their relative strengths, weaknesses and inter-connections. The details of the covered methods are presented in the form of "numerical recipes" and illustrated by case studies. A suite of simulation codes and data files is made available on the book's website to help the reader "to learn-by-doing" through solving the exercise problems offered in the book.


2004 ◽  
Vol 19 (2) ◽  
pp. 93-132 ◽  
Author(s):  
HIDDE DE JONG

Methods for qualitative simulation allow predictions on the dynamics of a system to be made in the absence of quantitative information, by inferring the range of possible qualitative behaviors compatible with the structure of the system. This article reviews QSIM and other qualitative simulation methods. It discusses two problems that have seriously compromised the application of these methods to realistic problems in science and engineering: the occurrence of spurious behavior predictions and the combinatorial explosion of the number of behavior predictions. In response to these problems, related approaches for the qualitative analysis of dynamic systems have emerged: qualitative phase-space analysis and semi-quantitative simulation. The article argues for a synthesis of these approaches in order to obtain a computational framework for the qualitative analysis of dynamic systems. This should provide a solid basis for further upscaling and for the development of model-based reasoning applications of a wider scope.


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