Journal Home Online First Current Issue Archive For Authors Journal Information 中文版

Engineering >> 2017, Volume 3, Issue 4 doi: 10.1016/J.ENG.2017.04.016

Computational Tools for the Integrated Design of Advanced Nuclear Reactors

TerraPower, LLC, Bellevue, WA 98005, USA

Accepted: 2017-06-23 Available online: 2017-08-30

Next Previous

Abstract

Advanced nuclear reactors offer safe, clean, and reliable energy at the global scale. The development of such devices relies heavily upon computational models, from the pre-conceptual stages through detailed design, licensing, and operation. An integrated reactor modeling framework that enables seamless communication, coupling, automation, and continuous development brings significant new capabilities and efficiencies to the practice of reactor design. In such a system, key performance metrics (e.g., optimal fuel management, peak cladding temperature in design-basis accidents, levelized cost of electricity) can be explicitly linked to design inputs (e.g., assembly duct thickness, tolerances), enabling an exceptional level of design consistency. Coupled with high-performance computing, thousands of integrated cases can be executed simultaneously to analyze the full system, perform complete sensitivity studies, and efficiently and robustly evaluate various design tradeoffs. TerraPower has developed such a tool—the Advanced Reactor Modeling Interface (ARMI) code system—and has deployed it to support the TerraPower Traveling Wave Reactor design and other innovative energy products currently under development. The ARMI code system employs pre-existing tools with strong pedigrees alongside many new physics and data management modules necessary for innovative design. Verification and validation against previous and new physical measurements, which remain an essential element of any sound design, are being carried out. This paper summarizes the integrated core engineering tools and practices in production at TerraPower.

Figures

Fig. 1

Fig. 2

Fig. 3

Fig. 4

Fig. 5

Fig. 6

Fig. 7

References

[ 1 ] Haigh T, Priestley M, Rope C. ENIAC in action: Making and remaking the modern computer. Cambridge: MIT Press; 2016.

[ 2 ] Greenspan H, Kelber CN, Okrent D, editors. Computing methods in reactor physics. New York: Gordon and Breach Science Publishers; 1968.

[ 3 ] Gilleland J, Petroski R, Weaver K. The traveling wave reactor: Design and development. Engineering 2016;2(1):88–96 link1

[ 4 ] Gamma E, Helm R, Johnson R, Vlissides H. Design patterns: Elements of reusable object-oriented software. Boston: Addison-Wesley Longman Publishing Co., Inc; 1995.

[ 5 ] Lee C, Yang WS. MC2-3: Multigroup cross section generation code for fast reactor analysis. Nuclear Sci Eng. Epub 2017 Jun 30 link1

[ 6 ] Derstine KL. DIF3D: A code to solve one-, two-, and three-dimensional finite-difference diffusion theory problems. Report.Illinois: Argonne National Laboratory; 1984 Apr. Report No.: ANL-82-64.

[ 7 ] Olson A. A users guide for the REBUS-PC code, version 1.4. Illinois: Argonne National Laboratory ; 2001 Dec. Report No.: ANL/RERTR/TM-32. Contract No.: W-31-109-ENG-38.

[ 8 ] Hosteny RP. ARC system fuel cycle analysis capability, REBUS-2, Report. Illinois: Argonne National Laboratory; 1978 Jan. Report No.: ANL-772-1.

[ 9 ] Chadwick MB, Herman M, Oblo?insky P, Dunn ME, Danon Y, Kahler AC, et al.ENDF/B-VII. 1. Nuclear data for science and technology: Cross sections, covariances, fission product yields and decay data. Nucl Data Sheets 2011;112(12):2887–996 link1

[10] Salvatores M, Palmiotti G, Aliberti G, Archier P, De Saint Jean C, Dupont E, et al.Methods and issues for the combined use of integral experiments and covariance data: Results of a NEA international collaborative study. Nucl Data Sheets 2014;118:38–71 link1

[11] Pelowitz DB. MCNPX user’s manual version 2.5.0. Los Alamos: Los Alamos National Laboratory; 2005 Apr. Report No.: LA-CP-05-0369.

[12] Ellis T, Petroski R, Hejzlar P, Zimmerman G, McAlees D, Whitmer C, et al.Traveling-wave reactors: A truly sustainable and full-scale resource for global energy needs. In: Proceedings of ICAPP’10 ; 2010 Jun 13–17; San Diego, USA. Illinois: American Nuclear Society; 2010. p. 10189.

[13] Miller SJ, Latta R. Fuel performance modeling of traveling wave reactor fuel elements [poster session]. In: Materials Modeling and Simulation for Nuclear Fuels 2013 Workshop; 2013 Oct 14–16; Chicago, IL, USA. 2013.

[14] Cohen M, Werner M, Johns C. Mechanical model of a TerraPower, traveling wave reactor fuel assembly duct. In: 22nd International Conference on Structural Mechanics in Reactor Technology; 2013 Aug 18–23; San Francisco, CA, USA. Red Hook: Curran Associates, Inc.; 2014.

[15] Dunn FE, Prohammer FG. The SASSYS LMFBR systems analysis code. Math Comput Simul 1984;26(1):23–6 link1

[16] Breiman L, Friedman JH. Estimating optimal transformations for multiple regression and correlation. J Am Stat Assoc 1985;80(391):580–98 link1

[17] Messac A. Physical programming-effective optimization for computational design. AIAA J 1996;34(1):149–58 link1

[18] American Society of Mechanical Engineers.Quality assurance requirements for nuclear facility applications. NQA-1-2008. New York: American Society of Mechanical Engineers; 2008.

[19] American Society of Mechanical Engineers.Addenda to ASME NQA-1-2008, Quality assurance requirements for nuclear facility applications. NQA-1a-2009. New York: American Society of Mechanical; 2009.

[20] OECD Nuclear Energy Agency.International handbook of evaluated reactor physics benchmark experiments. Paris: OECD Nuclear Energy Agency; 2009.

[21] Xu Z, Onufer M, Touran N. Reactor physics benchmark suite development for nuclear analysis codes and methods validations in travelling wave reactor applications. In: Proceedings of the Physics of Reactors Conference: Unifying Theory and Experiments in the 21st Century (PHYSOR 2016); 2016 May 1–5; Sun Valley, ID, USA. Illinois: American Nuclear Society; 2016. p. 362–76.

Related Research