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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) generat...
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... In the Sub-GVR method, T is the sum of total DENOVO execution time and MCNP5 execution time. The ratio of the FOM from Sub-GVR and the one-step analog calculation can be defined as speedup [20] and is used to quantify the efficiency improvements obtained by using the Sub-GVR method. Table 3 gives detailed information of the two calculation cases. ...
The solid fuel thorium molten salt reactor (TMSR-SF1) is a 10-MWth fluoride-cooled pebble bed reactor. As a new reactor concept, one of the major limiting factors to reactor lifetime is radiation-induced material damage. The fast neutron flux (E > 0.1 MeV) can be used to assess possible radiation damage. Hence, a method for calculating high-resolution fast neutron flux distribution of the full-scale TMSR-SF1 reactor is required. In this study, a two-step subsection approach based on MCNP5 involving a global variance reduction method, referred to as forward-weighted consistent adjoint-driven importance sampling, was implemented to provide fast neutron flux distribution throughout the TMSR-SF1 facility. In addition, instead of using the general source specification cards, the user-provided SOURCE subroutine in MCNP5 source code was employed to implement a source biasing technique specialized for TMSR-SF1. In contrast to the one-step analog approach, the two-step subsection approach eliminates zero-scored mesh tally cells and obtains tally results with extremely uniform and low relative uncertainties. Furthermore, the maximum fast neutron fluxes of the main components in TMSR-SF1 are provided, which can be used for radiation damage assessment of the structural materials.
... See Refs. [4][5][6][7][8][9][10] for examples. The basis of the FW-CADIS method is the development of a function that represents the importance of particles to the objective of uniform MC particle density in the desired tally regions; the details of this method are described in Ref. 1). ...
... Based on our experience with fixed-source problems, where speed-ups of O(10 2-4 ) are common, [4][5][6][7][8][9][10] we expect the speed-up to be larger for problems that are computationally more difficult. Hence, for problems with a larger variation in Fig. 1 Visual representation of the steps for calculating the FW-CADIS weight windows for the 2-D PWR quarter core model ...
... It is not the intention of the adjoint calculation to solve ,the problem exactly, thus a compromise between accuracy and efficiency is required to achieve optimum overall efficiency (i.e., minimize total CPU time, which is a combination of the CPU time required for the TORT adjoint and Monte Carlo calculations). A number of studies (Wagner, 1997;Haghighat et al., 1999) have demonstrated that the effectiveness of adjoint functions for variance reduction is not overly sensitive to the accuracy of the adjoint solution. Further discussion on A3MCNP functions and features is provided in the references (Wagner, 1997). ...
... The performance of A3MCNP for different importance functions, corresponding to different spatial mesh distributions used for the deterministic SN calculations, has been examined. We have tested numerous cases (Haghighat et al., 1999), but for brevity, we will discuss only five cases with uniform meshes. As indicated in Table 1, the mesh sizes for these cases vary from 5-cm to 60-cm. ...
Recent trends in Monte Carlo code development have reflected a recognition of the benefits of using deterministic importance functions for Monte Carlo variance reduction. This paper offers a review of the use of deterministic importance functions for variance reduction of Monte Carlo simulations. Adjoint methodology and the concept of “importance” are presented, along with an explanation of their use for variance reduction. Relevant works from a number of different researchers are briefly reviewed. The authors' CADIS methodology for calculating consistent source biasing and weight window parameters based on deterministic importance functions is presented. Efforts to automate the generation and use of deterministic importance functions are briefly described, including an overview of the A3MCNP code. Finally, aspects of interest, including computational benefits, associated with using deterministic importance functions for Monte Carlo simulation of real-world problems are demonstrated.
... The spatial mesh and energy group boundaries for the TORT calculation are taken directly from the MCNP WWG MESH and WWGE cards, 3 and materials are assigned to meshes based on mesh center coordinates. Although previous studies 8,16,17 have confirmed that the effectiveness of the adjoint function for variance reduction is not overly sensitivity to mesh fidelity, the capability to generate 2-D color plots of the spatial mesh and material specifications for any (and all) axial plane(s) is available. ...
As part of a larger project to upgrade the shielding capabilities in the SCALE code system, efforts have been initiated to independently develop and demonstrate generalized automated variance reduction. Initial efforts have developed a prototypic utility code for automated variance reduction based on existing Monte Carlo and discrete ordinates codes and proven methodologies. This prototypic utility code represents the first step in this effort and will be used for testing and refining the implementation of existing methodologies and investigating alternative methodologies, before attempting to integrate an automated variance reduction capability into SCALE. This paper describes the utility code and a demonstration of its effectiveness on a standard nuclear well-logging problem. The computational efficiencies achieved are very encouraging when compared to both stochastic and manual approaches for developing variance reduction, and the process seems to be well-suited for automation in a future SCALE shielding analysis sequence.
... A 3 MCNP™ has been used for a PWR cavity dosimetry calculation [6], for determination of the DPA (Displacement Per Atom) at a BWR core shroud [10], and for simulation of a shipping cask [5]. In this paper, we utilize A 3 MCNP™ to calculate flux at various sets of locations in a medium containing pure absorber and void region. ...
This paper analyzes the performance of A 3 MCNP™ (Automated Adjoint Accelerated MCNP) for a purely absorbing medium with void region. A 3 MCNP™ is based on the CADIS (Consistent Adjoint Driven Importance Sampling) methodology, which uses a deterministic 3-D importance function, to determine variance reduction parameters. A 3 MCNP™ automatically prepares necessary input files for adjoint transport calculation using the TORT discrete ordinates (S N) code. In a purely absorbing medium with void region, the S N method suffers from the ray effects. The impact of the ray effects on the performance of A 3 MCNP™ is investigated using different uniform mesh distributions. It is demonstrated that for an adjoint function distribution with no oscillation (ray effects), A 3 MCNP™ can yield significant speedups over an analog Monte Carlo calculation, for a medium containing pure absorber and void regions.
This paper provides a review of the hybrid (Monte Carlo/deterministic) radiation transport methods and codes used at the Oak Ridge National Laboratory and examples of their application for increasing the efficiency of real-world, fixed-source Monte Carlo analyses. The two principal hybrid methods are (1) Consistent Adjoint Driven Importance Sampling (CADIS) for optimization of a localized detector (tally) region (e.g., flux, dose, or reaction rate at a particular location) and (2) Forward Weighted CADIS (FW-CADIS) for optimizing distributions (e.g., mesh tallies over all or part of the problem space) or multiple localized detector regions (e.g., simultaneous optimization of two or more localized tally regions). The two methods have been implemented and automated in both the MAVRIC sequence of SCALE 6 and ADVANTG, a code that works with the MCNP code. As implemented, the methods utilize the results of approximate, fast-running 3-D discrete ordinates transport calculations (with the Denovo code) to generate consistent space-and energy-dependent source and transport (weight windows) biasing parameters. These methods and codes have been applied to many relevant and challenging problems, including calculations of PWR ex-core thermal detector response, dose rates throughout an entire PWR facility, site boundary dose from arrays of commercial spent fuel storage casks, radiation fields for criticality accident alarm system placement, and detector response for special nuclear material detection scenarios and nuclear well-logging tools. Substantial computational speed-ups, generally O(10 2-4), have been realized for all applications to date. This paper provides a brief review of the methods, their implementation, results of their application, and current development activities, as well as a considerable list of references for readers seeking more information about the methods and/or their applications.
This paper describes code and methods development at the Oak Ridge National Laboratory focused on enabling high-fidelity, large-scale reactor analyses with Monte Carlo (MC). Current state-of-the-art tools and methods used to perform "real" commercial reactor analyses have several undesirable features, the most significant of which is the non-rigorous spatial decomposition scheme. Monte Carlo methods, which allow detailed and accurate modeling of the full geometry and are considered the "gold standard" for radiation transport solutions, are playing an ever-increasing role in correcting and/or verifying the deterministic, multi-level spatial decomposition methodology in current practice. However, the prohibitive computational requirements associated with obtaining fully converged, system-wide solutions restrict the role of MC to benchmarking deterministic results at a limited number of state-points for a limited number of relevant quantities. The goal of this research is to change this paradigm by enabling direct use of MC for full-core reactor analyses. The most significant of the many technical challenges that must be overcome are the slow, non-uniform convergence of system-wide MC estimates and the memory requirements associated with detailed solutions throughout a reactor (problems involving hundreds of millions of different material and tally regions due to fuel irradiation, temperature distributions, and the needs associated with multi-physics code coupling). To address these challenges, our research has focused on the development and implementation of (1) a novel hybrid deterministic/MC method for determining high-precision fluxes throughout the problem space in k-eigenvalue problems and (2) an efficient MC domain-decomposition (DD) algorithm that partitions the problem phase space onto multiple processors for massively parallel systems, with statistical uncertainty estimation. The hybrid method development is based on an extension of the FW-CADIS method, which attempts to achieve uniform statistical uncertainty throughout a designated problem space. The MC DD development is being implemented in conjunction with the Denovo deterministic radiation transport package to have direct access to the 3-D, massively parallel discrete-ordinates solver (to support the hybrid method) and the associated parallel routines and structure. This paper describes the hybrid method, its implementation, and initial testing results for a realistic 2-D quarter core pressurized-water reactor model and also describes the MC DD algorithm and its implementation.
Thesis (Ph. D.)--University of Texas at Austin, 2003. Vita. Includes bibliographical references. Requires PDF file reader.
