Modeling and simulation

About: Modeling and simulation is a research topic. Over the lifetime, 10273 publications have been published within this topic receiving 111550 citations.

Computer Aided Design and Analysis of Continuous Pharmaceutical Manufacturing Processes

Abstract: Dynamic flowsheet modeling and simulation is a pre-requisite for the design, analysis, control and optimization of an integrated process. Whilst several integrated modeling and simulation tools (commercial and non-commercial) have proven to be effective for fluids processes, their use has been fairly limited for solids processes. The objective of this study is to build a dynamic flowsheet simulation of an integrated continuous downstream pharmaceutical process, using a combination of fundamental and empirical models. Using two cases,the results elucidate (i) the evolution of key particle properties during the transient state (start-up and shutdown), (ii) the effect of changes in process parameters and/or material properties which typically can vary during continuous manufacturing and (iii) the dynamic response and recycle dynamics of an integrated blender and a recirculation tank. Simulation results lend credence to developing a dynamic flowsheet simulation of a fully integrated downstream pharmaceutical process which can be further extended to the general class of solids processes.

Modeling, Simulation, and Validation of a Power SiC BJT

Abstract: This paper presents a physics-based model of a silicon carbide bipolar junction transistor and verification of its validity through experimental testing. The Fourier series solution is used to solve the ambipolar diffusion equation in the transistor collector region. The model is realized using MATLAB and Simulink. The experimental results of static operation and also the simulated and experimental results of switching waveforms are given.

Object Oriented Simulation: A Modeling and Programming Perspective

Abstract: This tutorial is based on the book with the same title, published by Springer, 2009. The tutorial presents an introduction to object oriented simulation modeling and using the OOSimL simulation language for implementing the simulation models. The emphasis is on early introduction to simulation because simulation is considered an important area in the computing curricula.An overview of relevant object-oriented modeling principles and then an introduction to object-oriented modeling and simulation using the process interaction approach with the new language, OOSimL. In addition to the simulation concepts, a brief exposure is included to developing models with the Eclipse integrated development environment. The simulation language was designed and developed for teaching OO simulation early in undergraduate programs in computing. Object orientation enhances the modeling and implementation of various families of systems. The OOSimL compiler generates Java code and C++ code. This simulation language was designed and developed for teaching OO simulation to students of computing (Computer Science, Software Engineering, and related disciplines). The language supports and promotes standard object-oriented (and software engineering) concepts and principles.

Methodology for characterizing modeling and discretization uncertainties in computational simulation

Abstract: This research effort focuses on methodology for quantifying the effects of model uncertainty and discretization error on computational modeling and simulation. The work is directed towards developing methodologies which treat model form assumptions within an overall framework for uncertainty quantification, for the purpose of developing estimates of total prediction uncertainty. The present effort consists of work in three areas: framework development for sources of uncertainty and error in the modeling and simulation process which impact model structure; model uncertainty assessment and propagation through Bayesian inference methods; and discretization error estimation within the context of non-deterministic analysis.

Criteria for Decomposing Systems Into Components in Modeling and Simulation: Lessons Learned with Military Simulations

Abstract: A crucial decision within component-based modeling and simulation is the choice of the criterion or criteria to be used in decomposing the system Many experiences with combat simulation systems indicate, first, that the optimal choice depends on the driving forces behind the decomposition or the goals that should be achieved by using components instead of monolithic systems Second, the common criteria for decomposition in software design, information hiding, and “object picking”are sometimes inappropriate for model decomposition because they neglect the importance of so-called hidden assumptions of model abstraction and idealization, which are paramount to the modeling of every complex system