Solving Three-Dimensional Large-Scale Neutron Transport Problems Using Hybrid Shared-Distributed Parallelism and Characteristics Method
Bibliographic record
Abstract
The design of new generations of nuclear reactors will involve fine representations of the theoretical models. Advanced computational methods capable of solving large-scale problems dealing with large and complex systems are required. Therefore, the solution to challenging large-scale neutron transport problems is becoming more and more pressing in nuclear engineering applications. The increase in high-performance computing resources have made possible direct application of transport methods to large-scale computational models. However, many numerical acceleration techniques common to lattice transport codes are not applicable to three-dimensional geometries with heterogeneous material zones, especially for the eigenvalue problems with high-dominance scattering ratio. Consequently, large heterogeneous reactor problems have remained computationally intensive and impractical for routine engineering applications. One of the alternatives is to use high-performance computing methods to solve such problems in reasonable time.In this context, we propose an approach based on high-performance computing techniques to solve large-scale neutron transport problems using a three-dimensional characteristics method. A performance model is then introduced to analyze the three-dimensional characteristics solvers in the context of hybrid shared/distributed memory modern architectures. Several numerical results and discussions are presented including a scalability analysis done to predict the performance on a large number of processors.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".