Computational Treatments for Neutron Resonance Elastic Scattering in Monte Carlo Nuclear Simulations


Simulations are vital to the safe design and operation of nuclear reactors. It is therefore important that they accurately treat the physics of nuclear interactions. This work investigates the phenomenon of neutron resonance scattering with a moving target, which can affect the post-collision properties of the neutron and macroscopic values such as temperature reactivity coefficients. First, this research validates a faster computational treatment for resonance scattering--the accelerated resonance elastic scattering (ARES) kernel sampling method--against the already verified Doppler broadening rejection correction (DBRC) treatment in the open-source OpenMC Monte Carlo neutron transport code being developed at the Massachusetts Institute of Technology. In an effort to improve computational efficiency, the optimal energy limits where this phenomenon should be treated are determined and compared to the less costly but inaccurate approximation of assuming a constant constant cross section for determining the reaction kinematics. To reduce memory requirements and facilitate coupling with heat transfer, a new data representation was recently adopted in OpenMC based on the multipole formalism. However, this new approach invalidates the previous implementations of DBRC and ARES. This thesis thus developed a modified DBRC algorithm compatible with the new data representation. This new method is also validated against the previous DBRC method. While more computationally costly, the use of the multipole representation in treating resonance scattering reduces memory requirements by a hundred-fold and facilitates the representation of temperature dependent cross sections. Thesis Supervisor: Benoit Forget Title: Assistant Professor of Nuclear Science and Engineering

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@inproceedings{Tran2016ComputationalTF, title={Computational Treatments for Neutron Resonance Elastic Scattering in Monte Carlo Nuclear Simulations}, author={Vivian Y. Tran}, year={2016} }