CORBA AND MPI-BASED “BACKBONE” FOR COUPLING ADVANCED SIMULATION TOOLS
Bibliographic record
Abstract
There is a growing international interest in using coupled, multidisciplinary computer simulations for a variety of purposes, including nuclear reactor safety analysis. Reactor behaviour can be modeled using a suite of computer programs simulating phenomena or predicting parameters that can be categorized into disciplines such as Thermalhydraulics, Neutronics, Fuel, Fuel Channels, Fission Product Release and Transport, Containment and Atmospheric Dispersion, and Severe Accident Analysis. Traditionally, simulations used for safety analysis individually addressed only the behaviour within a single discipline, based upon static input data from other simulation programs. The limitation of using a suite of stand-alone simulations is that phenomenological interdependencies or temporal feedback between the parameters calculated within individual simulations cannot be adequately captured. To remove this shortcoming, multiple computer simulations for different disciplines must exchange data during runtime to address these interdependencies. This article describes the concept of a new framework, which we refer to as the “Backbone,” to provide the necessary runtime exchange of data. The Backbone, currently under development at AECL for a preliminary feasibility study, is a hybrid design using features taken from the Common Object Request Broker Architecture (CORBA), a standard defined by the Object Management Group, and the Message Passing Interface (MPI), a standard developed by a group of researchers from academia and industry. Both have well-tested and efficient implementations, including some that are freely available under the GNU public licenses. The CORBA component enables individual programs written in different languages and running on different platforms within a network to exchange data with each other, thus behaving like a single application. MPI provides the process-to-process intercommunication between these programs. This paper outlines the different CORBA and MPI configurations examined to date, as well as the preliminary configuration selected for coupling 2 existing safety analysis programs used for modeling thermal–mechanical fuel behavior and fission product behavior respectively. In addition, preliminary work in hosting both the Backbone and the associated safety analysis programs in a cluster environment are discussed.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.012 | 0.007 |
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".