Author
Brian C. Kiedrowski
Other affiliations: University of Wisconsin-Madison, Los Alamos National Laboratory
Bio: Brian C. Kiedrowski is an academic researcher from University of Michigan. The author has contributed to research in topics: Monte Carlo method & Dynamic Monte Carlo method. The author has an hindex of 19, co-authored 109 publications receiving 4815 citations. Previous affiliations of Brian C. Kiedrowski include University of Wisconsin-Madison & Los Alamos National Laboratory.
Papers
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Los Alamos National Laboratory1, Brookhaven National Laboratory2, Oak Ridge National Laboratory3, Rensselaer Polytechnic Institute4, Argonne National Laboratory5, Lawrence Livermore National Laboratory6, International Atomic Energy Agency7, National Institute of Standards and Technology8, Japan Atomic Energy Agency9, Idaho National Laboratory10, Jožef Stefan Institute11, Nuclear Research and Consultancy Group12, University of Vienna13
TL;DR: The ENDF/B-VII.1 library as mentioned in this paper is the most widely used data set for nuclear data analysis and has been updated several times over the last five years. But the most recent version of the ENDF-B-VI.0 library is based on the JENDL-4.0 standard.
2,171 citations
Brookhaven National Laboratory1, Los Alamos National Laboratory2, International Atomic Energy Agency3, Rensselaer Polytechnic Institute4, National Institute of Standards and Technology5, Oak Ridge National Laboratory6, Argonne National Laboratory7, Lawrence Livermore National Laboratory8, Lawrence Berkeley National Laboratory9, North Carolina State University10, University of Michigan11, Institut de radioprotection et de sûreté nucléaire12, TRIUMF13, Rosatom14, Chalk River Laboratories15, Paul Scherrer Institute16, Karlsruhe Institute of Technology17, University of Bucharest18, Joint Institute for Nuclear Research19
TL;DR: The new ENDF/B-VIII.0 evaluated nuclear reaction data library as mentioned in this paper includes improved thermal neutron scattering data and uses new evaluated data from the CIELO project for neutron reactions on 1 H, 16 O, 56 Fe, 235 U, 238 U and 239 Pu described in companion papers.
1,249 citations
TL;DR: High confidence in the MCNP6 code is based on its performance with the verification and validation test suites, comparisons to its predecessor codes, the regression test suite, its code development process, and the underlying high-quality nuclear and atomic databases.
Abstract: MCNP6 is simply and accurately described as the merger of MCNP5 and MCNPX capabilities, but it is much more than the sum of those two computer codes. MCNP6 is the result of five years of effort by ...
977 citations
TL;DR: In this article, a Monte Carlo method is developed that performs adjoint-weighted tallies in continuous energy k-eigenvalue calculations, where each contribution to a tally score is weighted by an estimate of the relativized relativistic value.
Abstract: A Monte Carlo method is developed that performs adjoint-weighted tallies in continuous-energy k-eigenvalue calculations. Each contribution to a tally score is weighted by an estimate of the relativ...
126 citations
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01 May 1993
TL;DR: Comparing the results to the fastest reported vectorized Cray Y-MP and C90 algorithm shows that the current generation of parallel machines is competitive with conventional vector supercomputers even for small problems.
Abstract: Three parallel algorithms for classical molecular dynamics are presented. The first assigns each processor a fixed subset of atoms; the second assigns each a fixed subset of inter-atomic forces to compute; the third assigns each a fixed spatial region. The algorithms are suitable for molecular dynamics models which can be difficult to parallelize efficiently—those with short-range forces where the neighbors of each atom change rapidly. They can be implemented on any distributed-memory parallel machine which allows for message-passing of data between independently executing processors. The algorithms are tested on a standard Lennard-Jones benchmark problem for system sizes ranging from 500 to 100,000,000 atoms on several parallel supercomputers--the nCUBE 2, Intel iPSC/860 and Paragon, and Cray T3D. Comparing the results to the fastest reported vectorized Cray Y-MP and C90 algorithm shows that the current generation of parallel machines is competitive with conventional vector supercomputers even for small problems. For large problems, the spatial algorithm achieves parallel efficiencies of 90% and a 1840-node Intel Paragon performs up to 165 faster than a single Cray C9O processor. Trade-offs between the three algorithms and guidelines for adapting them to more complex molecular dynamics simulations are also discussed.
29,323 citations
University of Manchester1, KEK2, CERN3, Complutense University of Madrid4, SLAC National Accelerator Laboratory5, Toyama College6, Lebedev Physical Institute7, Fermilab8, University of Paris-Sud9, Lawrence Livermore National Laboratory10, National Research Nuclear University MEPhI11, Queen's University Belfast12, Korea Institute of Science and Technology Information13, Istituto Nazionale di Fisica Nucleare14, Northeastern University15, University of Seville16, National University of Cordoba17, Saint Joseph University18, Joint Institute for Nuclear Research19, University of Wollongong20, Illawarra Health & Medical Research Institute21, Hampton University22, TRIUMF23, ETH Zurich24, Centre national de la recherche scientifique25, University of Bordeaux26, University of Helsinki27, Johns Hopkins University School of Medicine28, National Technical University of Athens29, University of Notre Dame30, Ashikaga Institute of Technology31, Kobe University32, Intelligence and National Security Alliance33, University of Trieste34, University of Warwick35, University of Belgrade36, Instituto Superior Técnico37, European Space Agency38, Varian Medical Systems39, George Washington University40, Ritsumeikan University41, Ton Duc Thang University42, Université Paris-Saclay43, Idaho State University44, Naruto University of Education45
01 Nov 2016-Nuclear Instruments & Methods in Physics Research Section A-accelerators Spectrometers Detectors and Associated Equipment
TL;DR: Geant4 as discussed by the authors is a software toolkit for the simulation of the passage of particles through matter, which is used by a large number of experiments and projects in a variety of application domains, including high energy physics, astrophysics and space science, medical physics and radiation protection.
Abstract: Geant4 is a software toolkit for the simulation of the passage of particles through matter. It is used by a large number of experiments and projects in a variety of application domains, including high energy physics, astrophysics and space science, medical physics and radiation protection. Over the past several years, major changes have been made to the toolkit in order to accommodate the needs of these user communities, and to efficiently exploit the growth of computing power made available by advances in technology. The adaptation of Geant4 to multithreading, advances in physics, detector modeling and visualization, extensions to the toolkit, including biasing and reverse Monte Carlo, and tools for physics and release validation are discussed here.
2,260 citations
Los Alamos National Laboratory1, Brookhaven National Laboratory2, Oak Ridge National Laboratory3, Rensselaer Polytechnic Institute4, Argonne National Laboratory5, Lawrence Livermore National Laboratory6, International Atomic Energy Agency7, National Institute of Standards and Technology8, Japan Atomic Energy Agency9, Idaho National Laboratory10, Jožef Stefan Institute11, Nuclear Research and Consultancy Group12, University of Vienna13
TL;DR: The ENDF/B-VII.1 library as mentioned in this paper is the most widely used data set for nuclear data analysis and has been updated several times over the last five years. But the most recent version of the ENDF-B-VI.0 library is based on the JENDL-4.0 standard.
2,171 citations
Brookhaven National Laboratory1, Los Alamos National Laboratory2, International Atomic Energy Agency3, Rensselaer Polytechnic Institute4, National Institute of Standards and Technology5, Oak Ridge National Laboratory6, Argonne National Laboratory7, Lawrence Livermore National Laboratory8, Lawrence Berkeley National Laboratory9, North Carolina State University10, University of Michigan11, Institut de radioprotection et de sûreté nucléaire12, TRIUMF13, Rosatom14, Chalk River Laboratories15, Paul Scherrer Institute16, Karlsruhe Institute of Technology17, University of Bucharest18, Joint Institute for Nuclear Research19
TL;DR: The new ENDF/B-VIII.0 evaluated nuclear reaction data library as mentioned in this paper includes improved thermal neutron scattering data and uses new evaluated data from the CIELO project for neutron reactions on 1 H, 16 O, 56 Fe, 235 U, 238 U and 239 Pu described in companion papers.
1,249 citations
TL;DR: High confidence in the MCNP6 code is based on its performance with the verification and validation test suites, comparisons to its predecessor codes, the regression test suite, its code development process, and the underlying high-quality nuclear and atomic databases.
Abstract: MCNP6 is simply and accurately described as the merger of MCNP5 and MCNPX capabilities, but it is much more than the sum of those two computer codes. MCNP6 is the result of five years of effort by ...
977 citations