@@ -22,12 +22,14 @@ not experience a single scoring event, even after billions of analog histories.
2222Variance reduction techniques aim to either flatten the global uncertainty
2323distribution, such that all regions of phase space have a fairly similar
2424uncertainty, or to reduce the uncertainty in specific locations (such as a
25- detector). There are two strategies available in OpenMC for variance reduction:
26- the Monte Carlo MAGIC method and the FW-CADIS method. Both strategies work by
27- developing a weight window mesh that can be utilized by subsequent Monte Carlo
28- solves to split particles heading towards areas of lower flux densities while
29- terminating particles in higher flux regions---all while maintaining a fair
30- game.
25+ detector). There are three strategies available in OpenMC for variance
26+ reduction: weight windows generated via the MAGIC method or the FW-CADIS method,
27+ and source biasing. Both weight windowing strategies work by developing a mesh
28+ that can be utilized by subsequent Monte Carlo solves to split particles heading
29+ towards areas of lower flux densities while terminating particles in higher flux
30+ regions. In contrast, source biasing modifies source site sampling behavior to
31+ preferentially track particles more likely to reach phase space regions of
32+ interest.
3133
3234------------
3335MAGIC Method
@@ -132,3 +134,71 @@ aware of this.
132134 :label: variance_fom
133135
134136 \text {FOM} = \frac {1 }{\text {Time} \times \sigma ^2 }
137+
138+ .. _methods_source_biasing :
139+
140+ --------------
141+ Source Biasing
142+ --------------
143+
144+ In contrast to the previous two methods that introduce population controls
145+ during transport, source biasing modifies the sampling of the external source
146+ distribution. The basic premise of the technique is that for each spatial,
147+ angular, energy, or time distribution of a source, an additional distribution
148+ can be specified provided that the two share a common support (set of points
149+ where the distribution is nonzero). Samples are then drawn from this "bias"
150+ distribution, which can be chosen to preferentially direct particles towards
151+ phase space regions of interest. In order to avoid biasing the tally results,
152+ however, a weight adjustment is applied to each sampled site as described below.
153+
154+ Assume that the unbiased probability density function of a random variable
155+ :math: `X:x \rightarrow \mathbb {R}` is given by :math: `f(x)`, but that using the
156+ biased distribution :math: `g(x)` will result in a greater number of particle
157+ trajectories reaching some phase space region of interest. Then a sample
158+ :math: `x_0 ` may be drawn from :math: `g(x)` while maintaining a fair game,
159+ provided that its weight is adjusted as:
160+
161+ .. math ::
162+ :label: source_bias
163+
164+ w = w_0 \times \frac {f(x_0 )}{g(x_0 )}
165+
166+ where :math: `w_0 ` is the weight of an unbiased sample from :math: `f(x)`,
167+ typically unity.
168+
169+ Returning now to Equation :eq: `source_bias `, the requirement for common support
170+ becomes evident. If :math: `\mathrm {supp} (g)` fully contains but is not
171+ identical to :math: `\mathrm {supp} (f)`, then some samples from :math: `g(x)` will
172+ correspond to points where :math: `f(x) = 0 `. Thus these source sites would be
173+ assigned a starting weight of 0, meaning the particles would be killed
174+ immediately upon transport, effectively wasting computation time. Conversely, if
175+ :math: `\mathrm {supp} (g)` is fully contained by but not identical to
176+ :math: `\mathrm {supp} (f)`, the contributions of some regions outside
177+ :math: `\mathrm {supp} (g)` will not be counted towards the integral, potentially
178+ biasing the tally. The weight assigned to such points would be undefined since
179+ :math: `g(x) = \mathbf {0 }` at these points.
180+
181+ When an independent source is sampled in OpenMC, the particle's coordinate in
182+ each variable of phase space :math: `(\mathbf {r},\mathbf {\Omega },E,t)` is
183+ successively drawn from an independent probability distribution. Multiple
184+ variables can be biased, in which case the resultant weight :math: `w` applied to
185+ the particle is the product of the weights assigned from all sampled
186+ distributions: space, angle, energy, and time, as shown in Equation
187+ :eq: `tot_wgt `.
188+
189+ .. math ::
190+ :label: tot_wgt
191+
192+ w = w_r \times w_{\Omega } \times w_E \times w_t
193+
194+ Finally, source biasing and weight windows serve different purposes. Source
195+ biasing changes how particles are born, allowing the initial source sites to be
196+ sampled preferentially from important regions of phase space (space, angle,
197+ energy, and time) with an accompanying weight adjustment. Weight windows, by
198+ contrast, apply population control during transport (splitting and Russian
199+ roulette) to help particles reach and contribute in important regions as they
200+ move through the system. Because particle transport proceeds as usual after a
201+ biased source is sampled, particle attenuation in optically thick regions
202+ outside the source volume will not be affected by source biasing; in such
203+ scenarios, transport biasing techniques such as weight windows are often more
204+ effective.
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