Simulator verified

The coarsening process (growth of dominant wavelength) takes place during the approach to equilibrium as well. We carried out simulations verifying this. We started the process with 2 dimensional sine wave initial conditions, and with no incident flux, so that the surface relaxed towards a plane. After a short transient (during which the... 

Simulations verify that in the absence of strict packing, there is a transient formation of a hot and compressed cluster. This suggests that such a cluster provides an interesting medium within which unusual chemical reactions can be induced. Recent experiments (e.g. Ref. 320) are providing evidence that this is the case, thereby showing that even extreme conditions can be studied using clusters. 

The same strategy was applied in the derivation of rate equations for w-step nucleation according to a power law (cf. Eq. (21)) [133, 134], the combination of nucleation laws with anisotropic growth regimes [153], as well as truncated nucleation due to time-dependent concentration gradients of monomers [136]. MC simulations verified that the Avrami theorem is valid for instantaneous [184], progressive [185], and n-step nucleation according to a power law [184-187]. 

This simulation verifies that the closed-loop bid adjustment mechanism can stably reduce the discrepancies between the bidding prices and the actual costs, even with some dynamic situations during operation. 

The nature of the helical conformation of poly(phenylacetylene) has been studied in detail (74). The stabihty of the helical conformation of poly(phenylacetylenes) was estimated by the chiroptical properties of the copolymers from chiral and achiral phenylacetylenes. When the monomer possesses sterically less bulky ring substitnents, a clear cooperative nature on the copolymerization is not observed. A chiral amplification phenomenon is attainable only when the monomers have bulky ring snbstitnents. This result coincides with the poor chiroptical property of poly(42) (73) and also with the very intense CD effects of poly(44) having bulky chiral silyl groups (256). Computational simulations verified that, unlike polyisocyanates which have a long persistence length of helical structure because of their stiff main chain, the main chain of poly(phenylacetylene) is quite fiexible and that, unless bulky substituents are incorporated, poly(phenylacetylene) exists in essentially randomly coiled conformation or in a helical conformation with very short persistence length. 

Further stochastic simulation studies now in progress are concerned with fluctuation and nucleation in evolving chemical systems (e.g., limit cycle oscillations, combustion and explosions) and at the transition to spatial dissipative structure (cf.. Figure 10). In the latter case, for example, stochastic simulations verify the existence of critical long-range spatial correlations predicted in a stochastic theoretical study of the model (cf.. Ref. 17). 

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. 

To verify the modelling of the data eolleetion process, calculations of SAT 4, in the entrance window of the XRII was compared to measurements of RNR p oj in stored data as function of tube potential. The images object was a steel cylinder 5-mm) with a glass rod 1-mm) as defect. X-ray spectra were filtered with 0.6-mm copper. Tube current and exposure time were varied so that the signal beside the object. So, was kept constant for all tube potentials. Figure 8 shows measured and simulated SNR oproj, where both point out 100 kV as the tube potential that gives a maximum. Due to overestimation of the noise in calculations the maximum in the simulated values are normalised to the maximum in the measured values. Once the model was verified it was used to calculate optimal choice of filter materials and tube potentials, see figure 9. 

Measurements have been made in a static laboratory set-up. A simulation model for generating supplementary data has been developed and verified. A statistical data treatment method has been applied to estimate tracer concentration from detector measurements. Accuracy in parameter estimation in the range of 5-10% has been obtained. 

The entropically driven disorder-order transition in hard-sphere fluids was originally discovered in computer simulations [58, 59]. The development of colloidal suspensions behaving as hard spheres (i.e., having negligible Hamaker constants, see Section VI-3) provided the means to experimentally verify the transition. Experimental data on the nucleation of hard-sphere colloidal crystals [60] allows one to extract the hard-sphere solid-liquid interfacial tension, 7 = 0.55 0.02k T/o, where a is the hard-sphere diameter [61]. This value agrees well with that found from density functional theory, 7 = 0.6 0.02k r/a 2 [21] (Section IX-2A). 

Calculations at increasingly longer simulation times, are done to verify convergence [13]. In the slow change method, the integral is approximated in a simulation in which s is changed by a small amount, 5s after each integration step. 

It is important to verify that the simulation describes the chemical system correctly. Any given property of the system should show a normal (Gaussian) distribution around the average value. If a normal distribution is not obtained, then a systematic error in the calculation is indicated. Comparing computed values to the experimental results will indicate the reasonableness of the force field, number of solvent molecules, and other aspects of the model system. 

Because mesoscale methods are so new, it is very important to validate the results as much as possible. One of the best forms of validation is to compare the computational results to experimental results. Often, experimental results are not available for the system of interest, so an initial validation calculation is done for a similar system for which experimental results are available. Results may also be compared to any other applicable theoretical results. The researcher can verify that a sulficiently long simulation was run by seeing that the same end results are obtained after starting from several different initial configurations. 

Ah initio calculations of polymer properties are either simulations of oligomers or band-structure calculations. Properties often computed with ah initio methods are conformational energies, polarizability, hyperpolarizability, optical properties, dielectric properties, and charge distributions. Ah initio calculations are also used as a spot check to verify the accuracy of molecular mechanics methods for the polymer of interest. Such calculations are used to parameterize molecular mechanics force fields when existing methods are insulficient, which does not happen too often. 

Some toll processes lend themselves to test runs in the pre-startup phase. Actual materials for the toll may be used in the test or substitute materials, typically with low hazard potential, are often used to simulate the charging, reaction, and physical changes to be accomplished in the toll. Flow control, temperature control, pressure control, mixing and transferring efficiency can be measured. Mechanical integrity can be verified in regard to pumps, seals, vessels, heat exchangers, and safety devices. 

During testing, all alarm and shutdown funetions must be simulated to verify proper operation of the annuneiator display. The test teehni-eian must plaee his initials next to the tabulated set points as verifi-eation of eompletion. 

The most difficult part of a field test is the flow meter, if it wasn t planned in the construction phase. There is no way to simulate a meter run if you don t have the proper pipe length. Figure 10-8 is an example of the requirements. An ASME long radius flow nozzle is preferred by the author, though a short throat venturi will do. The probability is that an orifice is all that will be available. It should be examined before and after the test to verify not only the bore diameter, but the finish. The bore should... 

For a typical assembly, first part qualification begins with a rigorous dimensional check and painstaking prefit of all details on the bond tool. The assembly details are placed on the tool without adhesive, close contact between bond surfaces is verified and any detail or tool interference is corrected prior to proceeding. This is followed by fabrication of a verification film , or a simulated bond cure cycle of the assembly to allow measurement of the adhesive bondline thickness. 

Ideally, experiments and simulations should be performed simultaneously, and the results obtained should complement each other. The computer model for a basic configuration can be verified by comparison with experiment and then applied to more complex configurations. 

When a coarse grid is used, wall functions are used for imposing boundary conditions near the walls (Section 11.2.3.3). The nondimensional wall distance should be 30 < y < ]Q0, where y = u,y/p. We cannot compute the friction velocity u. before doing the CFD simulation, because the friction velocity is dependent on the flow. However, we would like to have an estimation of y" to be able to locate the first grid node near the wall at 30 < y < 100. If we can estimate the maximum velocity in the boundary layer, the friction velocity can be estimated as n, — 0.04rj, . . After the computation has been carried out, we can verify that 30 nodes adjacent to the walls. 

The shock-compression events are so extreme in intensity and duration, and remote from direct evaluation and from other environments, that experiment plays a crucial role in verifying and grounding the various theoretical descriptions. Indeed, the material models developed and advances in realistic numerical simulation are a direct result of advances in experimental methods. Furthermore, the experimental capabilities available to a particular scientist strongly control the problems pursued and the resulting descriptions of shock-compressed matter. Given the decisive role that experimental methods play, it is essential that careful consideration be given to their characteristics. 

The design verification plan should be constructed so that every design requirement is verified and the simplest way of confirming this is to produce a verification matrix of requirement against verification methods. You need to cover all the requirements, those that can be verified by test, by inspection, by analysis, by simulation or demonstration, or simply by validation of product records. For those requirements to be verified by test, a test specification will need to be produced. The test specification should specify which characteristics are to be measured in terms of parameters and limits and the conditions under which they are to be measured. 

However, these requirements go further than merely controlling the devices used for measurement. They address the measurements themselves, the selection of the devices for measurement and also apply to devices which create product features, if they are used for product verification purposes. If you rely on jigs, tools, fixtures, templates, patterns, etc. to form shapes or other characteristics and have no other means of verifying the shape achieved, these devices become a means of verification. If you use software to control equipment, simulate the environment or operational conditions, or carry out tests and you rely on that software doing what it is supposed to do, without any separate means of checking the result, the quality of such software becomes critical to product verification. In fact the requirements apply to metrology as a whole rather than being limited to the equipment that is used to obtain the measurement and therefore a more appropriate title of the section would be Control of measurements . 

According to the simulation of the process, the purity of the raffinate product was 98.0 % and the extract was slightly below 97.3 %. These results are quite close to the target purity of 99 %. To verify these constraints, the operating conditions were adjusted. 

These, such as the black box that was the receptor at the turn of the century, usually are simple input/output functions with no mechanistic description (i.e., the drug interacts with the receptor and a response ensues). Another type, termed the Parsimonious model, is also simple but has a greater number of estimatable parameters. These do not completely characterize the experimental situation completely but do offer insights into mechanism. Models can be more complex as well. For example, complex models with a large number of estimatable parameters can be used to simulate behavior under a variety of conditions (simulation models). Similarly, complex models for which the number of independently verifiable parameters is low (termed heuristic models) can still be used to describe complex behaviors not apparent by simple inspection of the system. 

---