13. Variance Reduction¶
13.1. Introduction¶
Transport problems can sometimes involve a significant degree of attenuation between the source and a detector (tally) region, which can result in a flux differential of ten orders of magnitude (or more) throughout the simulation domain. As Monte Carlo uncertainties tend to be inversely proportional to the physical flux density, it can be extremely difficult to accurately resolve tallies in locations that are optically far from the source. This issue is particularly common in fixed source simulations, where some tally locations may not experience a single scoring event, even after billions of analog histories.
Variance reduction techniques aim to either flatten the global uncertainty distribution, such that all regions of phase space have a fairly similar uncertainty, or to reduce the uncertainty in specific locations (such as a detector). There are two strategies available in OpenMC for variance reduction: the Monte Carlo MAGIC method and the FW-CADIS method. Both strategies work by developing a weight window mesh that can be utilized by subsequent Monte Carlo solves to split particles heading towards areas of lower flux densities while terminating particles in higher flux regions—all while maintaining a fair game.
13.2. MAGIC Method¶
The Method of Automatic Generation of Importances by Calculation, or MAGIC method, is an iterative technique that uses spatial flux information \(\phi(r)\) obtained from a normal Monte Carlo solve to produce weight windows \(w(r)\) that can be utilized by a subsequent iteration of Monte Carlo. While the first generation of weight windows produced may only help to reduce variance slightly, use of these weights to generate another set of weight windows results in a progressively improving iterative scheme.
Equation (1) defines how the lower bound of weight windows \(w_{\ell}(r)\) are generated with MAGIC using forward flux information. Here, we can see that the flux at location \(r\) is normalized by the maximum flux in any group at that location. We can also see that the weights are divided by a factor of two, which accounts for the typical \(5\times\) factor separating the lower and upper weight window bounds in OpenMC.
A major advantage of this technique is that it does not require any special transport machinery; it simply uses multiple Monte Carlo simulations to iteratively improve a set of weight windows (which are typically defined on a mesh covering the simulation domain). The downside to this method is that as the flux differential increases between areas near and far from the source, it requires more outer Monte Carlo iterations, each of which can be expensive in itself. Additionally, computation of weight windows based on regular (forward) neutron flux tally information does not produce the most numerically effective set of weight windows. Nonetheless, MAGIC remains a simple and effective technique for generating weight windows.
13.3. FW-CADIS¶
As discussed in the previous section, computation of weight windows based on regular (forward) neutron flux tally information does not produce the most numerically efficient set of weight windows. It is highly preferable to generate weight windows based on spatial adjoint flux \(\phi^{\dag}(r)\) information. The adjoint flux is essentially the “reverse” simulation problem, where we sample a random point and assume this is where a particle was absorbed, and then trace it backwards (upscattering in energy), until we sample the point where it was born from.
The Forward-Weighted Consistent Adjoint Driven Importance Sampling method, or FW-CADIS method, produces weight windows for global variance reduction given adjoint flux information throughout the entire domain. The weight window lower bound is defined in Equation (2), and also involves a normalization step not shown here.
While the algorithm itself is quite simple, it requires estimates of the global adjoint flux distribution, which is difficult to generate directly with Monte Carlo transport. Thus, FW-CADIS typically uses an alternative solver (often deterministic) that can be more readily adapted for generating adjoint flux information, and which is often much cheaper than Monte Carlo given that a rough solution is often sufficient for weight window generation.
The FW-CADIS implementation in OpenMC utilizes its own internal random ray multigroup transport solver to generate the adjoint source distribution. No coupling to any external transport is solver is necessary. The random ray solver operates on the same geometry as the Monte Carlo solver, so no redefinition of the simulation geometry is required. More details on how the adjoint flux is computed are given in the adjoint methods section.
More information on the workflow is available in the user guide, but generally production of weight windows with FW-CADIS involves several stages (some of which are highly automated). These tasks include generation of approximate multigroup cross section data for use by the random ray solver, running of the random ray solver in normal (forward flux) mode to generate a source for the adjoint solver, running of the random ray solver in adjoint mode to generate adjoint flux tallies, and finally the production of weight windows via the FW-CADIS method. As is discussed in the user guide, most of these steps are automated together, making the additional burden on the user fairly small.
The major advantage of this technique is that it typically produces much more numerically efficient weight windows as compared to those generated with MAGIC, sometimes with an order-of-magnitude improvement in the figure of merit (Equation (3)), which accounts for both the variance and the execution time. Another major advantage is that the cost of the random ray solver is typically negligible compared to the cost of the subsequent Monte Carlo solve itself, making it a very cheap method to deploy. The downside to this method is that it introduces a second transport method into the mix (random ray), such that there are more free input parameters for the user to know about and adjust, potentially making the method more complex to use. However, as many of the parameters have natural choices, much of this parameterization can be handled automatically behind the scenes without the need for the user to be aware of this.