Sweep Gaussian distribution

The simulations assuming a Gaussian distribution of sweep flow are based on mass balances for each fibre in the module. Key assumptions in the theoretical analysis are as follows. 

Figures 16.12 and 16.13 illustrate the performance predictions for the different configurations. The locations of the inlet and outlet regions for the sweep have little effect on performance. Dry gas flow rate and recovery decrease by less than 5% over the dew point range considered. The change in performance increases as dew point increases. Such results are consistent with the results obtained assuming a Gaussian distribution of the sweep around each fibre - variations in sweep flow rate do not significantly affect module performance.

This work reports simulations of sweep distribution within the shell and its effect on module performance. Two types of simulations are considered (1) simulations that assume the sweep flow around each fibre is distributed in a Gaussian manner and (2) simulations that explicitly predict flow fields within the shell based on how the sweep gas is introduced. 

Predictions based on explicit calculation of the shell flow are in good agreement with those based a Gaussian sweep distribution using a standard deviation in sweep flow equal... 

Results obtained for a Gaussian sweep distribution are compared to results obtained for a Gaussian fibre inner radius distribution. Previous work [30,31] has shown that variation in fibre inner radius has the largest impact on module performance when the variability in fibre properties is included in module performance simulations. 

The effect of non-uniform sweep distribution is examined by (1) assuming a Gaussian variation in the sweep flow around each fibre and (2) explicitly calculating the sweep distribution within the bundle for specified sweep inlet and outlet locations. In both cases, non-uniform sweep flows has little effect on module performance. The explicit sweep distribution calculations indicate large radial concentration gradients are present in the module. Surprisingly, these concentration gradients are not detrimental to performance. 

Figure 4.5 Illustration of phase point trajectories for an equilibrium distribution in the canonical ensemble. The trajectories sweep out the entire phase space randomly. Each trajectory corresponds to a particular total energy. The probability of finding a particular phase point follows the gaussian pattern shown. Note that for a microcanonical ensemble (not shown) the probability surface is uniform or flat and each phase point trajectory is at the same fixed total energy (cf Fig. 4.1 and Fig. 4.2).

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