### Variance reduction in energy domain

Posted:

**Fri Jul 12, 2013 9:55 pm**Hi Jaakko,

We have been experimenting with generation of homogenized cross sections for various reactor types.

One of the problems we encountered was that sometimes the energy group structure is not well suited for the spectrum of the reactor being modeled.

e.g. ECCO, a fast reactor lattice code in ERANOS, has 10 or so thermal energy groups.

If you use ECCO group structure in Serpent to model fast reactor, you will naturally get very poor statistics with unrealistic means and large errors for homogenized parameters.

The problem is that we then use these means in full core diffusion calculations and get results that can be quite bit off.

A short term solution to this could be adjusting the energy group structure to correspond to the problem's spectrum.

Such that the homogenized parameters errors are more or less the same for every group.

I am proposing potentially more useful and flexible solution which should not be too difficult to implement.

Recently, you have already introduced the source biasing in spatial domain (UFS) to force uniform distribution of errors in space.

How about using similar approach in energy domain so that the events (statistics) also get evenly distributed among energy groups.

The source energy distribution is of course fixed so biasing it would not necessarily help.

But perhaps there are other variance reduction techniques (weight window for each group?) that can be easily automated to solve this problem.

I wonder what you and other participants in this forum think about the idea.

Cheers!

Eugene

We have been experimenting with generation of homogenized cross sections for various reactor types.

One of the problems we encountered was that sometimes the energy group structure is not well suited for the spectrum of the reactor being modeled.

e.g. ECCO, a fast reactor lattice code in ERANOS, has 10 or so thermal energy groups.

If you use ECCO group structure in Serpent to model fast reactor, you will naturally get very poor statistics with unrealistic means and large errors for homogenized parameters.

The problem is that we then use these means in full core diffusion calculations and get results that can be quite bit off.

A short term solution to this could be adjusting the energy group structure to correspond to the problem's spectrum.

Such that the homogenized parameters errors are more or less the same for every group.

I am proposing potentially more useful and flexible solution which should not be too difficult to implement.

Recently, you have already introduced the source biasing in spatial domain (UFS) to force uniform distribution of errors in space.

How about using similar approach in energy domain so that the events (statistics) also get evenly distributed among energy groups.

The source energy distribution is of course fixed so biasing it would not necessarily help.

But perhaps there are other variance reduction techniques (weight window for each group?) that can be easily automated to solve this problem.

I wonder what you and other participants in this forum think about the idea.

Cheers!

Eugene