 
 
 
 
 
   
We have shown in the previous sections of this chapter that multiple 
 Ks can be
calculated from a single Monte Carlo simulation with good accuracy using the
CSFM approach.  In this section we will utilize this to calculate multiple Ks
and show the applicability of this method for design calculations.  Our
purpose is to apply the CSFM method to evaluate eigenvalues of multiple
systems that closely resemble each other, i.e., all the different systems are
slightly perturbed versions of a single system.  This single system can be
referred to as the unperturbed system.  We assume that the eigenvalue of the
unperturbed system is determined with arbitrary accuracy from either a SN or a
Monte Carlo calculation or perhaps an analytical solution if the unperturbed system
is simple enough.  The CSFM technique can then be applied to compute
multiple
Ks can be
calculated from a single Monte Carlo simulation with good accuracy using the
CSFM approach.  In this section we will utilize this to calculate multiple Ks
and show the applicability of this method for design calculations.  Our
purpose is to apply the CSFM method to evaluate eigenvalues of multiple
systems that closely resemble each other, i.e., all the different systems are
slightly perturbed versions of a single system.  This single system can be
referred to as the unperturbed system.  We assume that the eigenvalue of the
unperturbed system is determined with arbitrary accuracy from either a SN or a
Monte Carlo calculation or perhaps an analytical solution if the unperturbed system
is simple enough.  The CSFM technique can then be applied to compute
multiple  Ks relative to the unperturbed system's eigenvalue.  Using
the known K of the unperturbed system and the multiple
Ks relative to the unperturbed system's eigenvalue.  Using
the known K of the unperturbed system and the multiple  Ks computed by
the CSFM method, we can evaluate the absolute Ks of the multiple perturbed systems.
Ks computed by
the CSFM method, we can evaluate the absolute Ks of the multiple perturbed systems.
The test problem is a 5cm X 5cm square region with one group cross
sections and vacuum boundaries.  The square region is divided into 25
square cells of dimension 1cm X 1cm.  The 16 cells along the 
boundaries represent fuel
cells, and the center cell represents poison.  Rest of the 8 cells
represent moderator.  The objective of this design problem is to observe the
effect of varying the scattering ratio of the poison material on the
eigenvalue of the system.  The geometric configuration and cross sections for 
the problem
are shown in figure 4.1.  
 Ks corresponding to the three different
scattering ratios of the poison material at the center cell of the system. 
Using these three
Ks corresponding to the three different
scattering ratios of the poison material at the center cell of the system. 
Using these three  Ks and the known K of the unperturbed system, we
evaluate the three Ks.  These results are shown in table 4.9.  The Monte
Carlo runs utilize 100 inactive batches, 260 active batches, and 4000 neutrons
per batch.  We have also
calculated the three eigenvalues corresponding to the three 
different scattering ratios of the poison material
using fine mesh S16 TWODANT simulations.  These TWODANT results are also
shown in table 4.9 along with relative errors
Ks and the known K of the unperturbed system, we
evaluate the three Ks.  These results are shown in table 4.9.  The Monte
Carlo runs utilize 100 inactive batches, 260 active batches, and 4000 neutrons
per batch.  We have also
calculated the three eigenvalues corresponding to the three 
different scattering ratios of the poison material
using fine mesh S16 TWODANT simulations.  These TWODANT results are also
shown in table 4.9 along with relative errors 
 .  
The Monte Carlo eigenvalues agree within one tenth of a percent with the 
fine mesh TWODANT eigenvalues.  This example shows application of the
CSFM approach for design problems.
.  
The Monte Carlo eigenvalues agree within one tenth of a percent with the 
fine mesh TWODANT eigenvalues.  This example shows application of the
CSFM approach for design problems.  
| 1|c|Cases | 1|c|TWODANT K | 1|c| Monte Carlo K | 1|c|error (%) | 
| Case 0: poison | |||
| scattering ratio =  | 1.03006 | 1.03006 | |
| Case 1: poison | |||
| scattering ratio =  | 1.031031 | 1.031005  .13E-4 | 0.003 | 
| Case 2: poison |  | ||
| scattering ratio =  | 1.032291 | 1.032231  .27E-4 | 0.006 | 
| Case 3: poison |  | ||
| scattering ratio =  | 1.034017 | 1.033905  .41E-4 | 0.01 | 
 
 
 
 
