Simulation, distinct from model

Models begin conceptually for example, the concept that a blood vessel behaves as a fluid-filled pipe. Concepts may be developed into physical models, for example, using a latex tube to describe a blood vessel, upon which experiments are performed. Often, concepts are realized as mathematical models, whereby the concept is described by physical laws, transformed into a set of mathematical equations, and solved via computer. Simulations, distinct from models, are descriptions that mimic the physiological system. The quantitative nature of physiological models allows them to be employed as components of systems for the study of physiological control, illustrated in several of this section s chapters. 

If a solvent is to be considered as explicitly present in a simulation, obviously there must be some atomistic manner in which it is represented in the energy expression - this being the fundamental distinction from a continuum solvation model. However, since the solvent molecules greatly outnumber the solute molecule(s), there are advantages of efficiency that accrue from adopting as simple a representation as possible, and that is reflected in many of the solvent models in common use. 

The MD simulations show that second shell water molecules exist and are distinct from freely diffusing bulk water. Freed s analytical force-free model can only be applied to water molecules without interacting force relative to the Gd-complex, it should therefore be restricted to water molecules without hydrogen bonds formed. Freed s general model [91,92] allows the calculation of NMRD profiles if the radial distribution function g(r) is known and if the fluctuation of the water-proton - Gd vector can be described by a translational motion. The potential of mean force in Eq. 24 is obtained from U(r) = -kBT In [g(r)] and the spectral density functions have to be calculated numerically [91,97]. 

This complicated situation is fundamentally different from small linear plasma devices, in which the MAR chain has been seen to lead to enhanced plasma recombination In a tokamak divertor, distinct from these divertor-simulation experiments, the molecular pressure is not an externally controlled parameter but must be established by the recycling plasma itself. Detailed and consistent modeling, verified by dedicated spectroscopy, shows that processes that lead to a reduction of the molecular density (such as MAR and MAD), therefore, can have entirely different effects on plasma dynamics in real divertors and in divertor simulators. 

The subject of multiplicative fluctuations (in linear and especially nonlinear systems) is still deeply fraught with ambiguity. The authors of Chapter X set up an experiment that simulates the corresponding nonlinear stochastic equations by means of electric circuits. This allows them to shed light on several aspects of external multiplicative fluctuation. The results of Chapter X clearly illustrate the advantages resulting from the introduction of auxiliary variables, as recommended by the reduced model theory. It is shown that external multiplicative fluctuations keep the system in a stationary state distinct from canonical equilibrium, thereby opening new perspectives for the interpretation of phenomena that can be identified as due to the influence of multiplicative fluctuations. 

The distinctly different behavior of Model III is further clarified in Fig. 27, where the Hugoniots calculated from piston simulations for both Models II and III are shown. The Hugoniot for Model II, which has the classic ZDN behavior, is shown in the top panel of Fig. 27. The Hugoniot for Model III in the lower panel shows much different behavior. In this case, the system proceeds from the initial state I shown in Fig. 27 to the dissociative state B via the intermediate state A. It then proceeds from the dissociative phase to a product phase beginning with a C through a rarefaction shock. The position of the point A is determined by the properties of the phase transition. If A had occurred at a somewhat lower pressure, the system would have been able to proceed directly from I to B and the leading compressional shockfront would have been overrun by the dissociative... 

There have been several other methods proposed for the statistical mechanical modeling of chemical reactions. We review these techniques and explain their relationship to RCMC in this section. These simulation efforts are distinct from the many quantum mechanical studies of chemical reactions. The goal of the statistical mechanical simulations is to find the equilibrium concentration of reactants and products for chemically reactive fluid systems, taking into account temperature, pressure, and solvent effects. The goals of the quantum mechanics computations are typically to find transition states, reaction barrier heights, and reaction pathways within chemical accuracy. The quantum studies are usually performed at absolute zero temperature in the gas phase. Quantum mechanical methods are confined to the study of very small systems, so are inappropriate for the assessment of solvent effects, for example. 

On articulated vehicle modeling and SMC application, many researchers have revealed approaches. More analytically, in Ridley and Corke (2003), Lee and Yoo (2009), Nayl et al. (2012), kinematics model of articulated vehicle and error model between real and reference path are presented, and the path tracking simulation with model predictive control is applied, while in Petrov and Chakyrski (2009) the feedback controller based on Lyapunov approach is designed. The simulations in these literatures are non-real-time. The real-time feature is not verified. Moreover, in Korayem et al. (2012), the spatial cable robot path planning is presented, while in Aslam et al. (2014) the fuzzy SMC is used for path tracking of the four-wheel skid steer vehicle. Both the literatures have designed the controller of SMC. However, the models of the plants are distinct from the articulated vehicle. 

Surface hopping dynamics simulations of PSB3 with the semiempirical OM2 method (Keal et al. 2009) show a picture very distinct from the CASSCF simulations. Depending on the choice of active space, the excited state relaxation shows a bi-exponential decay profile of the Si population, with a fast sub-picosecond time constant and a picosecond time constant. Overall, the relaxation process is predicted to be larger than 600 fs, much slower than the 100 fs predicted by CASSCF. Similar multi-exponential decay has also been described in wave packet propagation on a two-dimensional surface model for RPSB (Santoro et al. 2007b). 

A theoretic study and computer simulation of model and control strategy for visual guidance of robot motion in dynamic environment is presented. Characteristics that make the visual guidance and motion control in dynamic environment distinct from that in static environment are discussed. The paper inspects the system attributes that must be taken account of and explores the inter-relations between these attributes. A conformable model of vision processing with the motion control of robot is developed. Computer simulation verifies the model and provides valuable insight toward the optimization of the process. 

Fig. 8 Li pathways in the cubic high-temperature phase of Li7La3Zr20i2 superimposed on the crystal structure. In the left-hand side part of the graph, the pathways are shown as derived fiom the BVSE analysis of the static structure model, while in the right-hand side part, the paths are based on the Li distribution density averaged over 500 ps of a ctmstant volume molecular dynamics simulation run at 7 = 1,000 K. On both sides, pathways in the back half of the unit cell are shown in pale colors to facilitate distinction from the paths in the front half of the unit cells. Numbers indicate the two types of Li sites...

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