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Characteristics of the simulation

The main characteristics of the simulation tools used for the sizing and performance evaluation of autonomous power systems in the design stage, include the way of representing the system, the definition of the APS control strategy, the simulation time step and methodology and finally the output of the simulation process. [Pg.20]

In this introductory chapter we discuss in Sec.2 the formulation of the simulated annealing approach to optimization, computations with the algorithm and their termination and we illustrate the method with an example. In Sec. 3 we present attempts to model and to analyze the performance of the algorithm, in particular, the dependence of the computational effort on the dimensionality of the problem and the termination criterion. We combine the results presented in this section with observations of the results of many applications and discuss in Sec. 4 some of the characteristics of the simulated annealing method. Results of calculations that minimize the total energy of molecular conformation for several compounds and a summary conclude the chapter. [Pg.4]

The development in this section arises from the generalization of an idea due to Kendall (1950) for a simple birth-and-death process. This generalization, accomplished by Shah et al (1977) was significant in that it provides a route to the simulation of a particulate system of arbitrary complexity and is limited only by the amount of computational power available. It is statistically exact in that the random numbers to be generated satisfy exactly calculated distribution functions from the model for particle behavior, thus allowing not only the calculation of the average system behavior but also the fluctuations about it. Furthermore, we shall see that it is free from arbitrary discretizations of time (or any other governing evolutionary coordinate) that were characteristic of the simulations of Section 4.6.1. [Pg.172]

A major characteristic of the simulation hospital is its ability to train all segments of healthcare givers under one roof. The ideal exercise would entail multidisciplinary simulation training however, specific healthcare category training is required frequently. Hence, simulation hospital is ideal for the whole spectrum of simulation training for all categories of hospital staff. [Pg.131]

The main component masses are shown in Table 6 and Table 7 lists the main characteristics of the simulated midsize car. [Pg.213]

Note that Lmincaf and Lmax help in defining some characteristics of the simulated artificial word, without changing in significant way its dynamics. A different role is played by p, as we discuss in the following. [Pg.94]

For the units for which a calculation is made to simulate operations, or for which a calculation is made for sizing purposes, the compositions are known it is necessary then to calculate the distillation curves starting from the characteristics of the components. [Pg.164]

Because the datay are random, the statistics based on y, S(y), are also random. For all possible data y (usually simulated) that can be predicted from H, calculate p(S(ysim) H), the probability distribution of the statistic S on simulated data y ii given the truth of the hypothesis H. If H is the statement that 6 = 0, then y i might be generated by averaging samples of size N (a characteristic of the actual data) with variance G- = G- (yacmai) (yet another characteristic of the data). [Pg.319]

For the two explosive loading systems used, the initial pressure wave into the powder is relatively low, varying from perhaps 1.5-4 GPa. In such cases the most relevant compression characteristic of the powder compact is its crush strength , i.e., the pressure required to compress the porous compact to solid density. In the simulations, this strength can be varied over a wide range with the P-a model. The wavespeed of the initial waves was modeled on the basis of shock-compression data on rutile at densities from 44% to 61% of solid density [74T02]. [Pg.154]

The advantage of the simulations compared to the experiments is that the correspondence between the tracer diffusion coefficient and the internal states of the chains can be investigated without additional assumptions. In order to perform a more complete analysis of the data one has to look at the quench-rate and chain-length dependence of the glass transition temperature for a given density [43]. A detailed discussion of these effects is far beyond the scope of this review. Here we just want to discuss a characteristic quantity which one can analyze in this context. [Pg.502]

The electrical characteristics of the cell and electrode will comprise both capacitative and resistive components, but for simplicity the former may be neglected and the system can be represented by resistances in series (Fig. 19.36 > and c). The resistance simulates the effective series resistance of the auxiliary electrode A.E. and cell solution, whilst the potential developed across by the flow of current between the working electrode W.E. and A.E. simulates the controlled potential W.E. with respect to R.E. [Pg.1108]

The model for a filled system is different. The filler is, as before, represented by a cube with side a. The cube is coated with a polymer film of thickness d it is assumed that d is independent of the filler concentration. The filler modulus is much higher than that of the d-thick coat. A third layer of thickness c overlies the previous one and simulates the polymeric matrix. The characteristics of the layers d and c are prescribed as before, and the calculation is carried out in two steps at first, the characteristics of the filler (a) - interphase (d) system are calculated then this system is treated as an integral whole and, again, as part of the two component system (filler + interphase) — matrix. From geometric... [Pg.15]

Experiments and simulations show that the characteristics of the nanostructures generated by this procedure are basically given by live parameters the distance between the STM and the substrate, the quantity of material loaded on the tip, the maximum ion current density for the dissolution of the material on the tip, the potential of the substrate, and the diameter of the STM apex. The controlled variation of these five parameters allows tailoring of the diameter and height of the clusters. [Pg.686]


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Characteristics simulation

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