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Implementation of meso-scale radioactive dispersion model for GPU

  • Sunarko and Z. Suud
Published/Copyright: February 28, 2022
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Abstract

Lagrangian Particle Dispersion Method (LPDM) is applied to model atmospheric dispersion of radioactive material in a meso-scale of a few tens of kilometers for site study purpose. Empirical relationships are used to determine the dispersion coefficient for various atmospheric stabilities. Diagnostic 3-D wind-field is solved based on data from one meteorological station using mass-conservation principle. Particles representing radioactive pollutant are dispersed in the wind-field as a point source. Time-integrated air concentration is calculated using kernel density estimator (KDE) in the lowest layer of the atmosphere. Parallel code is developed for GTX-660Ti GPU with a total of 1 344 scalar processors using CUDA. A test of 1-hour release discovers that linear speedup is achieved starting at 28 800 particles-per-hour (pph) up to about 20 × at 14 4000 pph. Another test simulating 6-hour release with 36 000 pph resulted in a speedup of about 60 ×. Statistical analysis reveals that resulting grid doses are nearly identical in both CPU and GPU versions of the code.

Abstract

Das Lagrangesche Partikel-Dispersions-Modell (LPDM) simuliert die Ausbreitung und Deposition radioaktiver Luftbeimengungen im mesoskaligen Bereich für standortbezogene Untersuchungen. Mit Hilfe empirischer Beziehungen wird der Dispersionskoeffizient für verschiedene athmosphärische Schichtungen bestimmt. Diagnostische 3-D-Windfelder wurden auf Grundlage der Datenlage einer meteorologischen Station mit Hilfe des Prinzips der Massenkonservierung simuliert. Partikel, die radioaktive Verunreinigungen simulieren breiten sich im Windfeld als Punktquelle aus. Die zeitintegrierte Luftkonzentration wurde mit Hilfe der Kerndichteschätzung (KDE) in der niedrigsten Schicht der Atmosphäre berechnet. Dazu wurde ein Parallel-Code für GTX-660Ti-GPU mit insgesamt 1 344 Skalarprozessoren unter Verwendung von CUDA entwickelt. Ein Test mit einstündiger Freisetzung zeigt, dass eine lineare Beschleunigung erreicht wird mit anfangs 28 800 Partikel-pro-Stunde (pph) bis hin zu etwa 20 × bei 144 000 pph. Ein weiterer Test mit 6-stündiger Freissetzung mit 36 000 pph ergab eine Beschleunigung von etwa 60 ×. Die statistische Analyse zeigt, dass die resultierenden Dosen nahezu identisch sind bei den CPU und GPU-Versionen des Codes.

Acknowledgements

The authors acknowledge the Ministry of Research and Technology and Nuclear Physics Laboratory of Institute Teknologi Bandung for supporting this research.

References

1 Caughey, S. J.; Wyngaard, J. C.; Kaimal, J. C.: Turbulence in the Evolving Stable Boundary Layer. J. Atmos. Sci. 36 (1979) 1041, DOI:10.1175/1520-0469(1979)036<1041:TITESB>2.0.CO;210.1175/1520-0469(1979)036<1041:TITESB>2.0.CO,2Search in Google Scholar

2 Diehl, S. R.; Smith, D. T.; Sydor M.: Random-Walk Simulation of Gradient-Transfer Processes Applied to Dispersion of Stack Emission from Coal-Fired Power Plants. Journal of Applied Meteorology 21 (1982) 69, DOI:10.1175/1520-0450(1982)021<0069:RWSOGT>2.0.CO;210.1175/1520-0450(1982)021<0069:RWSOGT>2.0.CO,2Search in Google Scholar

3 Hanna, S. R.; Chang, J. C.; Strimaitis, D. G.: Hazardous gas model evaluation with field observations. Atmos. Environ. 27A (1993) 2265, DOI:10.1016/0960-1686(93)90397-H10.1016/0960-1686(93)90397-HSearch in Google Scholar

4 International Atomic Energy Agency: Safety Guide NS-G-3.2, Dispersion of Radioactive Material in Air and Water and Consideration of Population Distribution in Site Evaluation for Nuclear Power Plants. (2002), ViennaSearch in Google Scholar

5 International Atomic Energy Agency: Safety Requirements NS-R-3, Site Evaluation for Nuclear Installations. (2003), ViennaSearch in Google Scholar

6 Januszewski, M.; Kostur, M.: Accelerating numerical solution of stochastic differential equations with CUDA. Computer Physics Communications 181 (2010) 183, DOI:10.1016/j.cpc.2009.09.00910.1016/j.cpc.2009.09.009Search in Google Scholar

7 Jimenez, J.; Ruiz de Miras, J.: Fast box-counting algorithm on GPU. Computer Methods and Programs in Biomedicine 108 (2012) 1229, PMid:22917763; DOI:10.1016/j.cmpb.2012.07.00510.1016/j.cmpb.2012.07.005Search in Google Scholar

8 Kalantzis, G.; Tachibana, H.: Accelerated event-by-event Monte Carlo microdosimetric calculations of electrons and proton tracks on a multi-core CPU and a CUDA-enabled GPU. Computer Methods and Programs in Biomedicine 113 (2014) 116, PMid:24113420; DOI:10.1016/j.cmpb.2013.09.00910.1016/j.cmpb.2013.09.009Search in Google Scholar

9 Matsumoto, M.; Nishimura, T.: http://www.math.sci.hiroshima-u.ac.jp/~m-mat/MT/emt.htmlSearch in Google Scholar

10 Molnar Jr., F.; Szakaly, T.; Meszaros, R.; Lagzi, L.: Air pollution modelling using a Graphics Processing Unit with CUDA. Computer Physics Communication 181 (2010) 105, DOI:10.1016/j.cpc.2009.09.00810.1016/j.cpc.2009.09.008Search in Google Scholar

11 NVIDIA corporation: NVIDIACUDAC programming Guide Version 4.2, 2010.Search in Google Scholar

12 Sherman, Christine A.: A Mass-Consistent Model for Wind Fields over Complex Terrain. Journal of Applied Meteorology 17 (1977) 312, DOI:10.1175/1520-0450(1978)017<0312:AMCMFW>2.0.CO;210.1175/1520-0450(1978)017<0312:AMCMFW>2.0.CO,2Search in Google Scholar

13 Sunarko: Model dispersi partikel untuk perhitungan lepasan radio-aktif (Studi kasus semenanjung Muria-Jawa Tengah), Master-Thesis, Institut Teknologi Bandung, 2009Search in Google Scholar

14 Uliasz, M.: Lagrangian Particle Dispersion Modeling in Meso-scale Applications. Environmental Modelling II. ed. P. Zannetti, Computational Mechanics Publication, Southampton, Boston, (1994), 71Search in Google Scholar

15 Zeng J.; Matsunaga T.; Mukai, H .: Using NVIDIA GPU for Modelling the Lagrangan Particle Dispersion in the Atmosphere, International Environmental Modelling and Software Society (iEMSs), 2010 International Congress on Environmental Modelling and Software Modelling for Environment’s Sake, Fifth Biennial Meeting (2010), Ottawa, CanadaSearch in Google Scholar

Books · Bücher

Development and Implementation of a Process Based Management System. IAEA Nuclear Energy Series No. NG-T-1.3, Published by the International Atomic Energy Agency 2015, ISBN 978-92-0-103215-7, 57 pp., 34.00 EUR.

A process based management system enhances traditional quality programmes, and, when properly implemented, enables the organization to satisfy external agencies and registrars for certification of management systems such as ISO 9001, ISO 14001, OHSAS 18001, and regulatory acceptance of security and safeguards programmes. It also ensures knowledge retention and the retention of all important aspects of existing programmes. As part of implementation, and to facilitate the same, organizations can develop maps, descriptions and other documents demonstrating how the certified quality assurance and quality management programmes have been addressed in the process based management system documents. Guidance comparing the requirements of GS-R-3 with ISO 9001, or with ASME NQA-1, may be used to help create such maps or documents.

A vendor provided management system delivered with a nuclear power plant to ensure safe operation is often a typical quality management system for operations and maintenance, which may, to a certain extent, integrate aspects related to safety and environmental protection. GS-R-3, however, requires the organization to identify processes of the management system needed to achieve all its objectives in all life cycle phases (siting, design, construction, commissioning, and operation and decommissioning), meet all requirements and deliver the outcomes of the organization. Furthermore, such processes must be planned, implemented, assessed and continually improved.

A management system complying with GS-R-3 must be tailored to meet the objectives and requirements of the organization. As a consequence, a quality assurance or quality management system needs to undergo the kind of transition indicated earlier. The guidance provided in this publication should be used in conjunction with IAEA guidance on continual improvement and on Managing Organizational Change in Nuclear Organizations. This publication contains information on how to make the transition from a quality management system to one aligned with IAEA requirements for management systems, thereby providing Member States and organizations wishing to adopt the new standards with guidance that facilitates such an undertaking. The guidance contained in this publication will enable Member States and organizations to introduce, apply and meet the new requirements in a planned and systematic manner, without negating any gains in safety performance or operational efficiency and effectiveness derived from their existing quality management system(s).

The publication focuses on the steps an organization can take to make the transition from quality assurance, quality control (QA, QC) and quality management (QM) to a management system meeting IAEA GS-R-3 requirements.

The objective of this Nuclear Energy Series Report is to provide good practice, practical examples, and methods that can be used to help organizations implement a process based management system as defined by IAEA Safety Standard GS-R-3.

Guidance provided here, describing good practices, represents expert opinion but does not constitute recommendations made on the basis of a consensus of Member States.

This publication provides guidance on implementing a process based management system to all life cycle stages of nuclear facilities and activities, including siting, design, construction, commissioning, and operation and decommissioning. As such, it can be applied to organizations implementing a management system for the first time, as well as to organizations wishing to make the transition from legacy management systems such as those based on QA and QM approaches.

This publication first presents general characteristics of implementing a process based management system in Section 2. Section 3 discusses the general starting point of management system development: evaluating business needs and preparing an implementation strategy. In Section 4, developing and managing the implementation plan for the management system is discussed, and Section 4 also presents the manner in which detailed processes can be developed. Finally, monitoring and follow-up are discussed in Section 5.

Appendices I–III and Annexes I–X provide further information on concrete aspects of the implementation process, and various examples.

Received: 2016-03-08
Published Online: 2022-02-28

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