Simulation framework

Establishing a Dynamic Model Simulation Framework for the Drug... 

Bioinorganic systems often contain heavy elements that need to be treated with an explicit relativistic method. It is now possible to carry out an explicit relativistic electronic structure calculation on the fly (152). The scalar-relativistic Douglas - Kroll - Hess method was implemented by us recently in the BOMD simulation framework (152). To use the relativistic densities in a non-relativistic gradient calculations turned out to be a valid approximation of relativistic gradients. An excellent agreement between optimized structures and geometries obtained from numerical gradients was observed with an error smaller than 0.02 pm. 

There is much to commend NIRM It is conceptually simple it can be implemented with only a modest extension of the simulation framework already needed for standard MC sampling and it is versatile. It has been applied successfully in free-energy measurements of (inter alia) crystalline solids [28],... 

The simulator framework CHEOPS couples different simulators at runtime. CHEOPS requires an input file which describes the simulation models and simulators for the components of the overall process, as well as their dependencies. Based on this information, CHEOPS starts the simulators in the correct order. In the case of feedback loops. CHEOPS iterates the simulation runs until a steady state is readied. The input file for CHEOPS is created by an integrator tool which operates on multiple data sources (product management database, integration documents, and the flow diagram). 

These setup files can also be exported from an existing model specification in ModKit+. In this case ModKit + acts as a mediator between the user, the model repository ROME, and the simulation framework CHEOPS. ModKit+ then provides specifications of the problem, the simulation setup, and correct references to the models which must be imported as files (cf. Subsect. 5.3.3). 

As a second step, ModKit- - is used to define the flowsheet model of the -caprolactam process, using the imported models from Aspen Plus, gPROMS, and MOREX as building blocks. The flowsheet topology is defined according to Fig. 5.20. The next step concerns the specification of a simulation experiment to be executed by the CHEOPS simulation framework. ModKit-f supports this step by generating a template of an input file for CHEOPS, which contains the necessary information except for the actual values of the feed streams and the parameters of the simulation. These have to be filled in by the user before he finally launches the simulation in CHEOPS. 

First, there is always the risk that some characteristics causing differences in worker productivity are not included as independent variables (or are unobservable). Hwang et al. (1992) have analysed this issue within a simulation framework and concluded that... 

As an alternative, the current paper presents an approach for analysis of aviation incidents that takes a multi-agent perspective, and is based on formal methods. The approach is an extension of the approach introduced in the work of Bosse and Mogles [4], which was in turn inspired by Blom, Bakker, Blanker, Daams, Everdij and Klompstra [1]. Whereas this approach mainly focuses on the analysis of existing accidents (also called accident analysis or retrospective analysis), the current paper also addresses analysis of potential future accidents (called risk analysis or prospective analysis). This is done by means of a multi-agent simulation framework that addresses both the behaviour of individual agents (operators, pilots) as well as their mutual communication, and interaction with technical systems. By manipulating various parameters in the model, different scenarios can be explored. Moreover, by means of automated checks of dynamic properties, these scenarios can be assessed with respect to their likelihood of the occurrence of accidents. The approach is illustrated by a case study on a runway incursion incident at a large European airport in 1995. 

In this respect, the model has been included in a Monte Carlo simulation framework, with parameters ... 

Finally, ofher solutions to the radiation transfer equation have been proposed using simulation to calculate the absorption and scattering coefficients. An example of simulation framework has been proposed by Wang et al. [26]. 

Baumgart, I., Keep, B., Krause, S. OverSim A Flexible Overlay Network Simulation Framework. In Proceedings of 10th IEEE Global Internet Symposium (GI 2007) in conjunction with IEEE INFOCOM 2007, pp. 79-84 (2007)... 

Kinetic MC models reaction kinetics within a molecular simulation framework by stochastic transitions using rate constants (Gillespie 1968, 1976). Several reviews of application areas in which kinetic MC has been used have been reported recently (Levi and Kotrla 1997 Martin 1998 Stoltze 2000). 

Kraft, J. (2009). RTSSim - a simulation framework for complex embedded systems. Technical Report, Malardalen University. http //www.mrtc.mdh.se/index. php choice=publications id=1629. 

Storch, M. Liu, J.-S. (1996). DRTSS A Simulation Framework for Complex Real-Time Systems, 2nd IEEE Real-Time Technology and Applications Symposium (RTAS 96), pp. 160-169. 

Liu, J. 2006. Parallel Real-time Immersive Modelling Environment (PRIME), Scalable Simulation Framework (SSF), User s manual, Colorado School of Mines Department of Mathematical and Computer Sciences. [Available online http //prime.mines.edu/ ... 

The remainder of this chapter is structured as follows. Methods, standards, and other works related to this chapter are described in Sect. 10.2. Section 10.3 defines the basic view of the simulation task and specifies the important terms and concepts of the simulation domain as well as the simulation framework architecture. Sect. 10.4 presents the architecture of the simulation domain, which is the one of the most important parts of the knowledge base (KB). The structure of the knowledge base is then described in Sect. 10.5. Section 10.6 shows the usage of the knowledge base for the configuration of the simulation framework. Sect. 10.7 presents an example utilizing our approach for simulation model design. Conclusions in Sect. 10.8 summarize the experience with the prototypical implementation. 

The goals mentioned in Sect. 10.1 require a general definition of simulation tasks applicable in a wide variety of usage. In this section, basic concepts and terms describing a simulation process and building the knowledge base are defined. At the end of this section a basic description of the simulation framework is given. 

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