Backbone: A Multiphysics Framework for Coupling Nuclear Codes Based on CORBA and MPI
Bibliographic record
Abstract
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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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.006 | 0.005 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.009 | 0.004 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".