Optimizing the implementation of the target motion sampling temperature treatment technique

How fast can it get?

Tuomas Viitanen, Jaakko Leppänen

Research output: Chapter in Book/Report/Conference proceedingConference article in proceedingsScientificpeer-review

1 Citation (Scopus)

Abstract

This article discusses the optimization of the target motion sampling (TMS) temperature treatment method, previously implemented in the Monte Carlo reactor physics code Serpent 2. The TMS method was introduced in [1] and first practical results were presented at the PHYSOR 2012 conference [2]. The method is a stochastic method for taking the effect of thermal motion into account on-the-fly in a Monte Carlo neutron transport calculation. It is based on sampling the target velocities at collision sites and then utilizing the 0 K cross sections at target-at-rest frame for reaction sampling. The fact that the total cross section becomes a distributed quantity is handled using rejection sampling techniques. The original implementation of the TMS requires 2.0 times more CPU time in a PWR pin-cell case than a conventional Monte Carlo calculation relying on pre-broadened effective cross sections. In a HTGR case examined in this paper the overhead factor is as high as 3.6. By first changing from a multi-group to a continuous-energy implementation and then fine-tuning a parameter affecting the conservativity of the majorant cross section, it is possible to decrease the overhead factors to 1.4 and 2.3, respectively. Preliminary calculations are also made using a new and yet incomplete optimization method in which the temperature of the basis cross section is increased above 0 K. It seems that with the new approach it may be possible to decrease the factors even as low as 1.06 and 1.33, respectively, but its functionality has not yet been proven. Therefore, these performance measures should be considered preliminary.
Original languageEnglish
Title of host publicationProceedings
Subtitle of host publicationInternational Conference on Mathematics and Computational Methods Applied to Nuclear Science & Engineering, M&C 2013
Pages950-961
Publication statusPublished - 2013
MoE publication typeNot Eligible
EventInternational Conference on Mathematics and Computational Methods Applied to Nuclear Science and Engineering, M&C 2013 - Sun Valley, ID, United States
Duration: 5 May 20139 May 2013

Publication series

Name
PublisherIdaho National Laboratory
Volume2

Conference

ConferenceInternational Conference on Mathematics and Computational Methods Applied to Nuclear Science and Engineering, M&C 2013
Abbreviated titleM&C 2013
CountryUnited States
CitySun Valley, ID
Period5/05/139/05/13

Fingerprint

Sampling
Temperature
Program processors
Neutrons
Physics
Tuning

Keywords

  • doppler
  • Monte Carlo
  • neutron tracking
  • on-the-fly
  • tempereature

Cite this

Viitanen, T., & Leppänen, J. (2013). Optimizing the implementation of the target motion sampling temperature treatment technique: How fast can it get? In Proceedings: International Conference on Mathematics and Computational Methods Applied to Nuclear Science & Engineering, M&C 2013 (pp. 950-961)
Viitanen, Tuomas ; Leppänen, Jaakko. / Optimizing the implementation of the target motion sampling temperature treatment technique : How fast can it get?. Proceedings: International Conference on Mathematics and Computational Methods Applied to Nuclear Science & Engineering, M&C 2013. 2013. pp. 950-961
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Viitanen, T & Leppänen, J 2013, Optimizing the implementation of the target motion sampling temperature treatment technique: How fast can it get? in Proceedings: International Conference on Mathematics and Computational Methods Applied to Nuclear Science & Engineering, M&C 2013. pp. 950-961, International Conference on Mathematics and Computational Methods Applied to Nuclear Science and Engineering, M&C 2013, Sun Valley, ID, United States, 5/05/13.

Optimizing the implementation of the target motion sampling temperature treatment technique : How fast can it get? / Viitanen, Tuomas; Leppänen, Jaakko.

Proceedings: International Conference on Mathematics and Computational Methods Applied to Nuclear Science & Engineering, M&C 2013. 2013. p. 950-961.

Research output: Chapter in Book/Report/Conference proceedingConference article in proceedingsScientificpeer-review

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T1 - Optimizing the implementation of the target motion sampling temperature treatment technique

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AU - Viitanen, Tuomas

AU - Leppänen, Jaakko

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N2 - This article discusses the optimization of the target motion sampling (TMS) temperature treatment method, previously implemented in the Monte Carlo reactor physics code Serpent 2. The TMS method was introduced in [1] and first practical results were presented at the PHYSOR 2012 conference [2]. The method is a stochastic method for taking the effect of thermal motion into account on-the-fly in a Monte Carlo neutron transport calculation. It is based on sampling the target velocities at collision sites and then utilizing the 0 K cross sections at target-at-rest frame for reaction sampling. The fact that the total cross section becomes a distributed quantity is handled using rejection sampling techniques. The original implementation of the TMS requires 2.0 times more CPU time in a PWR pin-cell case than a conventional Monte Carlo calculation relying on pre-broadened effective cross sections. In a HTGR case examined in this paper the overhead factor is as high as 3.6. By first changing from a multi-group to a continuous-energy implementation and then fine-tuning a parameter affecting the conservativity of the majorant cross section, it is possible to decrease the overhead factors to 1.4 and 2.3, respectively. Preliminary calculations are also made using a new and yet incomplete optimization method in which the temperature of the basis cross section is increased above 0 K. It seems that with the new approach it may be possible to decrease the factors even as low as 1.06 and 1.33, respectively, but its functionality has not yet been proven. Therefore, these performance measures should be considered preliminary.

AB - This article discusses the optimization of the target motion sampling (TMS) temperature treatment method, previously implemented in the Monte Carlo reactor physics code Serpent 2. The TMS method was introduced in [1] and first practical results were presented at the PHYSOR 2012 conference [2]. The method is a stochastic method for taking the effect of thermal motion into account on-the-fly in a Monte Carlo neutron transport calculation. It is based on sampling the target velocities at collision sites and then utilizing the 0 K cross sections at target-at-rest frame for reaction sampling. The fact that the total cross section becomes a distributed quantity is handled using rejection sampling techniques. The original implementation of the TMS requires 2.0 times more CPU time in a PWR pin-cell case than a conventional Monte Carlo calculation relying on pre-broadened effective cross sections. In a HTGR case examined in this paper the overhead factor is as high as 3.6. By first changing from a multi-group to a continuous-energy implementation and then fine-tuning a parameter affecting the conservativity of the majorant cross section, it is possible to decrease the overhead factors to 1.4 and 2.3, respectively. Preliminary calculations are also made using a new and yet incomplete optimization method in which the temperature of the basis cross section is increased above 0 K. It seems that with the new approach it may be possible to decrease the factors even as low as 1.06 and 1.33, respectively, but its functionality has not yet been proven. Therefore, these performance measures should be considered preliminary.

KW - doppler

KW - Monte Carlo

KW - neutron tracking

KW - on-the-fly

KW - tempereature

M3 - Conference article in proceedings

SN - 978-162748643-9

SP - 950

EP - 961

BT - Proceedings

ER -

Viitanen T, Leppänen J. Optimizing the implementation of the target motion sampling temperature treatment technique: How fast can it get? In Proceedings: International Conference on Mathematics and Computational Methods Applied to Nuclear Science & Engineering, M&C 2013. 2013. p. 950-961