Engineering topics

Hamilton, Steven

Publications and source records attributed to Hamilton, Steven.

7 records

DOE OSTI2018

Assessment of the Lagrange Discrete Ordinates Equations for Three-Dimensional Neutron Transport

The Lagrange Discrete Ordinates (LDO) equations, developed by Ahrens as an alternative to the traditional discrete ordinates formulation, have been implemented in Denovo, a threedimensional radiation transport code developed by Oak Ridge National Laboratory. The LDO equations retain the formal structure of the classical discrete ordinates equations but treat particle scattering in a different way. Solutions of the LDO equations have an interpolatory structure such that the angular flux can be naturally evaluated at directions other than the discrete ordinates used in arriving at the solutions, and the ordinates themselves may be chosen in a strategic way for the problem under consideration. Of particular interest is that the LDO equations have been shown to mitigate ray effects at increased angular resolutions. In this paper we present scalar flux solutions of the LDO equations for a small number of test cases of interest and compare the results against flux solutions generated using standard quadrature types. The LDO equations’ flux solutions were found to be comparable to those resultant from the standard quadrature types in value; results from the LDO equations were also found to be commensurate with those of standard quadrature types when comparing the flux solutions in the context of the experimental benchmark test case examined.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2018

Eigenvalue Solvers for Modeling Nuclear Reactors on Leadership Class Machines

In this paper, three complementary methods have been implemented in the code Denovo that accelerate neutral particle transport calculations with methods that use leadership-class computers fully and effectively: a multigroup block (MG) Krylov solver, a Rayleigh quotient iteration (RQI) eigenvalue solver, and a multigrid in energy (MGE) preconditioner. The MG Krylov solver converges more quickly than Gauss Seidel and enables energy decomposition such that Denovo can scale to hundreds of thousands of cores. RQI should converge in fewer iterations than power iteration (PI) for large and challenging problems. RQI creates shifted systems that would not be tractable without the MG Krylov solver. It also creates ill-conditioned matrices. The MGE preconditioner reduces iteration count significantly when used with RQI and takes advantage of the new energy decomposition such that it can scale efficiently. Each individual method has been described before, but this is the first time they have been demonstrated to work together effectively. The combination of solvers enables the RQI eigenvalue solver to work better than the other available solvers for large reactors problems on leadership-class machines. Using these methods together, RQI converged in fewer iterations and in less time than PI for a full pressurized water reactor core. These solvers also performed better than an Arnoldi eigenvalue solver for a reactor benchmark problem when energy decomposition is needed. The MG Krylov, MGE preconditioner, and RQI solver combination also scales well in energy. Finally, this solver set is a strong choice for very large and challenging problems.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2017

Local Improvement Results for Anderson Acceleration with Inaccurate Function Evaluations

Here, we analyze the convergence of Anderson acceleration when the fixed point map is corrupted with errors. We also consider uniformly bounded errors and stochastic errors with infinite tails. We prove local improvement results which describe the performance of the iteration up to the point where the accuracy of the function evaluation causes the iteration to stagnate. We illustrate the results with examples from neutronics.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2016

Summary of VERA Core Simulator Performance Improvements

This report describes the performance improvements made to the VERA Core Simulator (VERA CS) during FY2016. At the beginning of the year, a timing study was performed to identify areas of the code where the run-times could be improved. This study identified six broad areas for improvement: • Cross section lookup and CMFD stabilization, • CTF parallel partitioning, • MOC methodology, • Subgroup methodology, • MOC parallel partitioning, and • CMFD Krylov solution techniques. The goal of this work was to reduce the VERA-CS run-times so a complete cycle depletion could be run with 1,000 cores overnight (i.e., the “1,000 core depletion” criterion). This goal was set by our industry partners so they could achieve reasonable turnaround on medium-sized computer clusters. The results presented in this report show a code speed-up by a factor of 5, which successfully satisfies the goal set by industry.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2016

An assessment of coupling algorithms for nuclear reactor core physics simulations

This paper evaluates the performance of multiphysics coupling algorithms applied to a light water nuclear reactor core simulation. The simulation couples the k-eigenvalue form of the neutron transport equation with heat conduction and subchannel flow equations. We compare Picard iteration (block Gauss–Seidel) to Anderson acceleration and multiple variants of preconditioned Jacobian-free Newton–Krylov (JFNK). The performance of the methods are evaluated over a range of energy group structures and core power levels. A novel physics-based approximation to a Jacobian-vector product has been developed to mitigate the impact of expensive on-line cross section processing steps. Numerical simulations demonstrating the efficiency of JFNK and Anderson acceleration relative to standard Picard iteration are performed on a 3D model of a nuclear fuel assembly. Both criticality (k-eigenvalue) and critical boron search problems are considered.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2015

Exnihilo Transport Methods Manuals Revision 0

Exnihilo is a transport code system that contains both deterministic and stochastic (Monte Carlo) solvers. The deterministic solvers are contained in the Denovo package while the Monte Carlo solvers are implemented in the Shift package. This document explains the mathematical and numerical methods employed within Exnihilo. It is not a users guide, nor is it a code developer's manual. Naturally, there is cross-over between the code implementation and mathematical description of any given method, and these are included for the purpose of greater understanding where appropriate. The purpose of this document is to formally describe the equations that are solved by the various packages inside of Exnihilo. In general, linkages are not made between a given numerical method and the Exnihilo code base. This is largely due to the fact that the Exnihilo code base changes far more rapidly than the actual methods that it is solving. Thus, this documentation would go rapidly out of date if cross-references were made between the methods and the code that implements them. Instead, developers are encouraged to look at the Doxygen-produced code documentation that explains what each translation unit in the code is doing. Where necessary we will make distinctions between methods that are implemented in Exnihilo and methods that are documented to provide better understanding of the broader field and provide context for the chosen methods in Exnihilo.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2015

Exnihilo User's Manuals Release 5.3 (Dev)

Exnihilo is a modern radiation transport framework that implements a variety of advanced solvers and solution method- ologies, enabling it to solve a wide variety of nuclear engineering and applications problems with the scalability to run on both desktop machines and leadership-class supercomputers. It supports "stand-alone" execution using its internal front ends, but its components (such as the the Denovo S N solver) are also integrated into other radiation transport codes.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗