Skip to main content
SpringerLink
Log in
SpringerLink
Log in

Computational Methods in Transport

Granlibakken 2004

  • Conference proceedings
  • © 2006

Overview

Editors:
  1. Frank Graziani

    1. Lawrence Livermore National Laboratory, Livermore, U.S.A.

    View editor publications

    You can also search for this editor in PubMed   Google Scholar

Part of the book series: Lecture Notes in Computational Science and Engineering (LNCSE, volume 48)

This is a preview of subscription content, log in via an institution to check access.

Access this book

Log in via an institution
eBook USD 139.00
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever

Tax calculation will be finalised at checkout

Other ways to access

Licence this eBook for your library

Institutional subscriptions

Table of contents (26 papers)

  1. Mathematics and Computer Science

    1. Front Matter

      Pages I-XVII
      Download chapter PDF

    2. Transport Approximations in Partially Diffusive Media

      • Guillaume Bal
      Pages 373-400

    3. High Order Finite Volume Nonlinear Schemes for the Boltzmann Transport Equation

      • Barna L. Bihari, Peter N. Brown
      Pages 401-422

    4. Obtaining Identical Results on Varying Numbers of Processors in Domain Decomposed Particle Monte Carlo Simulations

      • N.A. Gentile, Malvin Kalos, Thomas A. Brunner
      Pages 423-433

    5. KM-Method of Iteration Convergence Acceleration for Solving a 2D Time-Dependent Multiple-Group Transport Equation and its Modifications

      • A.V. Gichuk, L.P. Fedotova, R.M. Shagaliev
      Pages 435-443

    6. A Regularized Boltzmann Scattering Operator for Highly Forward Peaked Scattering

      • Anil K. Prinja, Brian C. Franke
      Pages 445-455

    7. Implicit Riemann Solvers for the Pn Equations

      • Ryan McClarren, James Paul Holloway, Thomas Brunner, Thomas Mehlhorn
      Pages 457-467

    8. The Solution of the Time–Dependent SN Equations on Parallel Architectures

      • F. Douglas Swesty
      Pages 469-486

    9. Different Algorithms of 2D Transport Equation Parallelization on Random Non-Orthogonal Grids

      • R.M. Shagaliev, A.V. Alekseev, I.M. Beliakov, A.V. Gichuk, A.A. Nuzhdin, V.Yu. Rezchikov
      Pages 487-496

  2. Neutron Transport

    1. Front Matter

      Pages I-XVII
      Download chapter PDF

    2. Parallel Deterministic Neutron Transport with AMR

      • C.J. Clouse
      Pages 499-512

    3. An Overview of Neutron Transport Problems and Simulation Techniques

      • Edward W. Larsen
      Pages 513-534

  3. Back Matter

    Pages 535-539
    Download chapter PDF
Back to top

Keywords

About this book

Thereexistawiderangeofapplicationswhereasigni?cantfractionofthe- mentum and energy present in a physical problem is carried by the transport of particles. Depending on the speci?capplication, the particles involved may be photons, neutrons, neutrinos, or charged particles. Regardless of which phenomena is being described, at the heart of each application is the fact that a Boltzmann like transport equation has to be solved. The complexity, and hence expense, involved in solving the transport problem can be understood by realizing that the general solution to the 3D Boltzmann transport equation is in fact really seven dimensional: 3 spatial coordinates, 2 angles, 1 time, and 1 for speed or energy. Low-order appro- mations to the transport equation are frequently used due in part to physical justi?cation but many in cases, simply because a solution to the full tra- port problem is too computationally expensive. An example is the di?usion equation, which e?ectively drops the two angles in phase space by assuming that a linear representation in angle is adequate. Another approximation is the grey approximation, which drops the energy variable by averaging over it. If the grey approximation is applied to the di?usion equation, the expense of solving what amounts to the simplest possible description of transport is roughly equal to the cost of implicit computational ?uid dynamics. It is clear therefore, that for those application areas needing some form of transport, fast, accurate and robust transport algorithms can lead to an increase in overall code performance and a decrease in time to solution.

Editors and Affiliations

  • Lawrence Livermore National Laboratory, Livermore, U.S.A.

    Frank Graziani

Bibliographic Information

Publish with us

Policies and ethics

Back to top

Search

Navigation