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Research Papers

Backbone: A Multiphysics Framework for Coupling Nuclear Codes Based on CORBA and MPI

[+] Author and Article Information
Yu Liu

Fuel and Fuel Channel Safety Branch,
Canadian Nuclear Laboratories,
Chalk River, ON, Canada K0J 1J0
e-mail: yu.liu@cnl.ca

Michael Nishimura

Department of Computer Science,
University of Waterloo,
Waterloo, ON, Canada N2L 3G1
e-mail: mjnishim@uwaterloo.ca

Marat Seydaliev

Fuel and Fuel Channel Safety Branch,
Canadian Nuclear Laboratories,
Chalk River, ON, Canada K0J 1J0
e-mail: marat.seydaliev@cnl.ca

Markus Piro

Fuel and Fuel Channel Safety Branch,
Canadian Nuclear Laboratories,
Chalk River, ON, Canada K0J 1J0
e-mail: markus.piro@cnl.ca

Manuscript received January 5, 2016; final manuscript received May 10, 2016; published online December 20, 2016. Assoc. Editor: Juan-Luis Francois.This work was prepared while under employment by the Government of Canada as part of the official duties of the author(s) indicated above, as such copyright is owned by that Government, which reserves its own copyright under national law.

ASME J of Nuclear Rad Sci 3(1), 011020 (Dec 20, 2016) (10 pages) Paper No: NERS-16-1002; doi: 10.1115/1.4034061 History: Received January 05, 2016; Accepted May 12, 2016

Recent trends in nuclear reactor performance and safety analyses increasingly rely on multiscale multiphysics computer simulations to enhance predictive capabilities by replacing conventional methods that are largely empirically based with a more scientifically based methodology. Through this approach, one addresses the issue of traditionally employing a suite of stand-alone codes that independently simulate various physical phenomena that were previously disconnected. Multiple computer simulations of different phenomena must exchange data during runtime to address these interdependencies. Previously, recommendations have been made regarding various approaches for piloting different design options of data coupling for multiphysics systems (Seydaliev and Caswell, 2014, “CORBA and MPI Based “Backbone” for Coupling Advanced Simulation Tools,” AECL Nucl. Rev., 3(2), pp. 83–90). This paper describes progress since the initial pilot study that outlined the implementation and execution of a new distribution framework, referred to as “Backbone,” to provide the necessary runtime exchange of data between different codes. The Backbone, currently under development at the Canadian Nuclear Laboratories (CNL), is a hybrid design using both common object request broker architecture (CORBA) and message passing interface (MPI) systems. This paper also presents two preliminary cases for coupling existing nuclear performance and safety analysis codes used for simulating fuel behavior, fission product release, thermal hydraulics, and neutron transport through the Backbone. Additionally, a pilot study presents a few strategies of a new time step controller (TSC) to synchronize the codes coupled through the Backbone. A performance and fidelity comparison is presented between a simple heuristic method for determining time step length and a more advanced third-order method, which was selected to maximize configurability and effectiveness of temporal integration, saving time steps and reducing wasted computation. The net effect of the foregoing features of the Backbone is to provide a practical toolset to couple existing and newly developed codes—which may be written in different programming languages and used on different operating systems—with minimal programming effort to enhance predictions of nuclear reactor performance and safety.

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Figures

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Fig. 1

Reactor performance and safety analysis codes [3]

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Fig. 2

Infrastructure of the Backbone for coupling advanced nuclear codes

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Fig. 3

Data package format of the Backbone communication protocol

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Fig. 4

Solution of integrating the Backbone with a cluster

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Fig. 5

Simulation results comparison between the coupling case and the ELOCA only case

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Fig. 6

Evolution of sheath and coolant temperatures at the simulated fuel bundle, bundle power from 3D-Solver

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Fig. 7

Sinusoidal function (Eq. (2)) against time, and the size of time step against time, using third-order controller 1 (H312b configuration, 0.05 tolerance, 1/18 error weight, 1/9 last error weight, and 1/18 second last error weight)

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Fig. 8

Sinusoidal function against time, and the size of time step against time, over 50 points

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Fig. 9

Delayed logistic function f against time, as output by the simulation, and the time step size against time, with steps selected using simple strategy (1.01 step-up, 0.9 step-down); 116 time steps, 9 rejections

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Fig. 10

Delayed logistic function f against time, as output by the simulation, and the time step size against time, with steps selected using the elementary controller (0.05 tolerance, 0.2 error weight); 93 time steps, 12 rejections

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Fig. 11

Delayed logistic function f against time, as output by the simulation, and the time step size against time, with steps selected using third-order controller (H312b configuration, 0.05 tolerance, 1/18 error weight, 1/9 last error weight, and 1/18 second last error weight); 121 time steps, 20 rejections

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