Virtual experiments

When you are done with this virtual experiment hit the button below to create a report -will be automabcolly included in the report, which you can save or print... 

Once the reactant pools have been filtered, the next step in product-based designs is usually to enumerate the full virtual library. This can be a very time-consuming step and hence a useful precursor can be to enumerate carefully chosen subsets that will give an indication of the success or otherwise of the full virtual experiment. Thus, in a two component reaction it can be useful to take the first reactant in the first pool and combine it with all the reactants in the second pool (to generate 1 x nB products). This should then be followed by the enumeration of one reactant in the second pool with all reactants in the first pool to give nA x 1 products. If either of these two partial enumeration steps fail, then the full enumeration will also fail. Thus, troublesome reactants can be identified early. 

The difference arises because the identification of which of the data element is the random variable differs between the 2 designs. It is r, the number of heads, in the first case and n, the number of tosses, in the second. The p values compare the actually observed data with the data from an infinite number of virtual experiments (the fre-quentist approach). In the first case, all these experiments have 12 tosses and varying numbers of heads in the second, they all have 3 tails and varying numbers of tosses. 

To make a more formal assessment of the possible errors (a combination of numerical modeling errors) in the calculations of DREAM-SOFC, here we treat the other eight results as outcomes from eight repetitions of a virtual experiment, the differences being a result of experimental uncertainty. With this assumption, we can calculate the 90% confidence interval for the true error in the DREAM results using the t-distribution as ... 

Molecular interactions at the north pole A virtual experiment... 

What does it mean to look at the problem from the north pole Let us try to understand this by the following virtual experiment, in which we consider a liquid mixture of molecules. In all the figures in this chapter we just take a mixture of water and C02 as a simple example. 

Recently, a substantial effort has been made to optimize column internals for reactive separations and to reduce the number of expensive hydrodynamics experiments via the CFD simulations (67,119,122). Such simulations can be regarded as virtual experiments carried out in order to predict the performance of the internals by varying geometrical and structural parameters, thus reducing the optimization time. 

The collaboration is still going on. The full-loop, 3D simulations of MIP reactors are being performed to help further scale-up. To some extent, the multi-scale CFD is beginning to take the place of virtual experiment for solving industrial problems, and it is emerging as a paradigm beneficial to both industry and academia. 

The biosimulation models need not to be large and comprehensive. Large models are difficult to validate, because small changes in the model structure can lead to different outcomes. The models in the following sections are all of the type which could be called virtual experiments because the setup resembles an experimental setup. The advantage is that the biosimulation models easily can analyze hundreds of what if situations within a second or two. 

Counter-current gas/vapor-liquid film flows in SP above the load conditions are extremely complicated. For this reason, it appears improbable that the CFD-based virtual experiments replace real experiments entirely in the near future. However, even single-phase CFD simulations can improve predictivity of pressure drop models, since all correlations pressure drop - gas load used in practice contain some dry pressure drop correlation as a basic element. Replacing this correlation by the rigorous CFD analysis helps to avoid heuristic assumptions on possible correlation structure, which are inevitable both in conventional mechanistic models (Rocha et ah, 1993) and in more sophisticated considerations (Olujic, 1997). 

Real experiments for the determination of external mass transfer coefficients are used as an example for virtual experiments with CFD. Here experimental studies (Williamson et al., 1963 Wilson and Geankopolis, 1966) on the flow of two liquids, namely water and a propylene glycol-water mixture, through a packed bed of spherical particles made from solid benzoic acid are... 

Recently, a combination of CFD and rate-based process simulation has been proposed as a way to link different scales. In the rate-based approach, the influence of the column internals on hydrodynamics and mass transfer is directly accounted via relevant hydrodynamic and mass transfer correlations. These correlations can be now obtained not only from real experiments, but also by application of CFD simulations, thus reducing the number of necessary hydrodynamic experiments. Such virtual experiments allow the optimization of column internals, even without really manufactured internals. 

The analysis of virtual experiments given with a stochastic model [4.65]. 

SIA SFU will also provide ongoing support for students and teachers through the SIA SFU web site (www.siasfu.ca), which will grow to contain links to interesting and age-appropriate web sites for chemistry, biology and physics, including virtual experiments. The web site will contain helpful information for science teachers and experiments that can be conducted at home or the classroom with commonly available materials. 

Use the Interactive Virtual Experiment Separate the Substances found on Disc 1 of the CD-ROM. 

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