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In this technical note, an analysis of an integral experiment of the Advanced Heavy Water Reactor (AHWR) Critical Facility (CF) with a diffusion-based Monte Carlo (MC) method is discussed. In this method, the diffusion kernel is converted into probabilities per unit time for tracking the particle in the problem domain. The diffusion-based MC method is coupled with a time-dependent MC algorithm developed earlier and has been used for space-time simulations in neutron multiplication assemblies. Kinetics simulations are best solved using a transport MC route, but this requires long computational time. The diffusion-based MC method provides a faster solution in such space-time simulations. Most of the space-time kinetics studies and benchmarks are based on diffusion theory, and there are very few transport theory or MC benchmarks. Thus, the diffusion-based MC facilitates exact comparison with the large number of diffusion theory benchmarks. The efficacy of this method was tested earlier by comparison with the results of realistic space-time kinetics benchmarks based on diffusion theory methods involving multiregion reactors and detailed energy dependence. Comparison of our results with these benchmarks has shown satisfactory agreement. As a step toward more detailed benchmarking, the ability and accuracy of this method are tested on the recent experiment done in the AHWR CF. The integral experiments with one thoria-based mixed oxide experimental fuel assembly in the core of the AHWR CF were analyzed with this method and were compared with the observed experimental values. The experiments consisted of measurement of the critical height and worth of shut-off rods (SORs) with the experimental fuel assembly placed at different lattice locations. Neutron count rates as a function of time after reactor trip for estimation of the worth of the SORs were also simulated, and the results are found to be in good agreement with the observed values.

Analysis of Integral Experiment of the AHWR Critical Facility with Diffusion-Based Monte Carlo Method