TY - JOUR

T1 - Performance of the Automated Adjoint Accelerated MCNP (A3MCNP) for Simulation of a BWR Core Shroud Problem

AU - Wagner, John

PY - 1999/9

Y1 - 1999/9

N2 - This paper discusses the recently developed version of MCNP, A 3 MCNP, that automatically prepares variance reduction parameters based on the CADIS (Consistent Adjoint Driven Importance Sampling) methodology. A 3 MCNP prepares necessary information for performing 3-D deterministic adjoint transport calculations. This automation includes (1) generation of a 3-D mesh distribution, (2) preparation of input files for cross-section generation and the adjoint transport calculation, calculation of a biased source, and (3) calculation of weights for the weight-window space and energy splitting/roulette. A 3 MCNP has been used to analyze deep-penetration shielding applications. Here, we discuss its use for determining neutron-induced displacement per atom (DPA) at BWR core shroud welds. We have obtained DPA values with 1% (1-σ) uncertainties in less than 5 CPU hours, whereas an analog Monte Carlo simulation we estimate would require about one month of CPU time. Furthermore, performance of the code has been measured for different discrete deterministic adjoint models.

AB - This paper discusses the recently developed version of MCNP, A 3 MCNP, that automatically prepares variance reduction parameters based on the CADIS (Consistent Adjoint Driven Importance Sampling) methodology. A 3 MCNP prepares necessary information for performing 3-D deterministic adjoint transport calculations. This automation includes (1) generation of a 3-D mesh distribution, (2) preparation of input files for cross-section generation and the adjoint transport calculation, calculation of a biased source, and (3) calculation of weights for the weight-window space and energy splitting/roulette. A 3 MCNP has been used to analyze deep-penetration shielding applications. Here, we discuss its use for determining neutron-induced displacement per atom (DPA) at BWR core shroud welds. We have obtained DPA values with 1% (1-σ) uncertainties in less than 5 CPU hours, whereas an analog Monte Carlo simulation we estimate would require about one month of CPU time. Furthermore, performance of the code has been measured for different discrete deterministic adjoint models.

UR - https://www.researchgate.net/publication/229015684_Performance_of_the_Automated_Adjoint_Accelerated_MCNP_A3MCNP_for_Simulation_of_a_BWR_Core_Shroud_Problem

M3 - Article

JO - Proceedings of the International Conference on Mathematics and Computation, Reactor Physics, and Environmental Analysis in Nuclear Applications

JF - Proceedings of the International Conference on Mathematics and Computation, Reactor Physics, and Environmental Analysis in Nuclear Applications

ER -