Sample computation results

Sample Computation Results. The Lagrangian scheme of Equation (9) to (15) was used to compute the steady state response to sinusoidal excitation of an anechoic coating glued on a steel plate shown in Figure 1. Symmetry considerations permit the calculations to be limited to the regions between the dotted lines, the unit cell. 

In Section 9.2 below, a summary of the nomenclature used in the chapter is given. In Section 9.3, a summary of fuel cell stack geometry, and a discussion of the dimensional reductions used in the model is given. In Section 9.4, the model of 1-D MEA transport is presented, followed by Section 9.5 on the model of channel flow for a unit cell and Section 9.6 on the electrical and thermal coupling in a stack environment. In Section 9.7, a summary of the stack model is given followed by its discretization. In Section 9.8, the iterative solution strategy for the discrete system is presented, followed by sample computational results in Section 9.9. The current state of stack modeling in this framework and future directions are summarized in the final section. 

In Fig. 3.26, sample computational results of the temperature profile in the thickness direction are presented at different instants (Lee, 1981). As the precharge material, which is initially at a low temperature, is placed... 

Fig. 63 Sample computational results for pressure contours in a human abdominal aorta (Ahmadi and Joseph )...

Ultrasonic pulses are generated within the sample on the transducer s resonance frequency, pass through sample and are received by the wideband receiver. After received pulse processing in the computer, results analysis is carried out. 

The application was a model test only and no attempt was made to acquire or validate site-specific source data. Specific and prototype point sources and area sources were included most risk came from the nonspecific, hypothetical sources. Risk was computed using unvalldated, hypothetical unit risk factors. Sample graphical results are presented in Figures 3, 4, and 5. 

Computed results from this model are compared to actual kiln performance in Table VI and the operating conditions taken from kiln samples are given in Table VII. There are no unit factors or adjustable parameters in this model. As with the explicit model, all kinetic data are determined from laboratory experiments. Values of the frequency factors and activation energies are given in Table VIII. Diffusivity values are also included. The amount of fast coke was determined from Eq. (49). With the exception of the T-B (5/12) survey, the agreement between observed and computed values of CO, CO2, and O2 is very good considering that there are no adjustable parameters used to fit the model to each kiln. In the kiln survey T-212/10, the CO conversion activity of the catalyst has been considerably deactivated and a different frequency factor was used in this simulation. 

This section presents computational results of the models and the sampling scheme proposed in this chapter. The refinery examples considered represent industrial-scale size refineries and an actual configuration that can be found in many industrial sites around the world. In the presentation of the results, we focus on demonstrating the sample average approximation computational results as we vary the sample sizes and compare their solution accuracy and the CPU time required for solving the models. 

Analytical considerations applied to the full dynamics (where it is H rather than T that determines the time evolution) and computational results [45f] suggest that even when the coupling is allowed for, remnants of the zero-order dichotomy between promptly decaying and stable states do survive, that K states can either decay promptly, without much sampling of the bound space of N states, and that N - K states have a delayed decay. The larger is the density of bound states, the slower is the decay of the states that, in first order, are trapped. Figure 6 is the schematic case while Fig. 8 shows explicit computational results. 

Fig. 6. Proton Tj relaxation data for crosslinked polybutadiene samples with average number of repeat units per network chain 44 (A) and 14 (O), compared with the computed results from the modified BPP equation (assuming Gaussian distribution and the model based bn spin diffusion to locations of rapid spin-lattice relaxation) (reprinted from Ref.541 with permission)...

In addition to the functions outlined above, the computer is also used to generate requests for new samples, validate results, perform statistical analyses of experimental data, and finally produce a report sheet setting out all the relevant information in tabular form. The fact that the computer is required to handle such a diversity of different tasks, many of which, must be performed simultaneously, necessitates the use of sophisticated software. The relationship between the analytical procedures, the computer files, and the control programme BRANDER is shown in Fig. 23. 

The most direct information concerning compliance with Dempsey s rules is found in Figure 5 where the experimental x2 values are compared with computed results. Again, the dealuminated x=.17 sample (i) exhibits total randomization under Loewenstein s restrictions, while the other samples indicate limited compliance with Dempsey s rules. 

The methods used here to give the phase equilibria are reviewed, and the Azeotropic Distillation Program ADP/ADPLLE is described. Application of the program to calculate an azeotropic distillation problem is shown and discussed, and a sample computer output is given and is briefly discussed. Finally, calculated azeotropic distillation results are compared for dehydrating aqueous ethanol for the three entrainers, n-pentane, benzene, and diethyl ether. 

The objective function values of K-means algorithm with 50 iterative cycles are listed in Table 2, the lowest value is 168.7852. One notices that the behavior of K-means algorithm is influenced by the choice of initial cluster centers, the order in which the samples were taken, and, of course, the geometrical properties of the data. The tendency of sinking into local optima is obvious. Clustering by SA can provide more stable computational results. 

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