Bibliographic record
Abstract
This paper aims to increase the understanding of high level Nuclear Power Plant (NPP) licensing processes in Finland, France, the UK, Canada and the USA. These countries have been selected for this study because of their different licensing processes and recent actions in new NPP construction. After discussing their similarities and differences, suitable features for Small Modular Reactor licensing can be emphasized and suggested. Some of the studied licensing processes have elements that are already quite well suited for application to SMRs, but all of these different national processes can benefit from studying and implementing lessons learned from SMR specific licensing needs. The main SMR features to take into account in licensing are standardization of the design, modularity, mass production and serial construction. Modularity can be divided into two different categories: the first category is simply a single unit facility constructed of independently engineered modules (e.g., construction process for Westinghouse AP-1000 NPP) and the second is a facility structure composed of many reactor modules where modules are manufactured in factories and installed into the facility as needed (e.g., NuScale Power SMR design). Short construction schedules will not be fully benefitted from if the long licensing process prolongs the commissioning and approach to full-power operation. The focus area of this study is to better understand the possibility of SMR deployment in small nuclear countries, such as Finland, which currently has four operating NPPs. The licensing process needs to be simple and clear to make SMR deployment feasible from an economical point of view. This paper uses public information and interviews with experts to establish the overview of the different licensing processes and their main steps. A high-level comparison of the licensing steps has been carried out. Certain aspects of the aviation industry licensing process have also been studied and certain practices have been investigated as possibly suitable for use in nuclear licensing. All of the current licensing processes were found to be quite heavy and time-consuming and further streamlining could be possible without compromising safety or the need for public participation in the licensing process. Some examples of the modification possibilities for SMR applications are discussed. A profound discussion on SMR-specific licensing models, and on ways to simplify and harmonize them, will be needed in the near future in Europe too. This would be a natural continuation to the harmonization efforts underway for existing and new large reactors.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.046 | 0.075 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.006 | 0.008 |
| Scholarly communication | 0.015 | 0.021 |
| Open science | 0.007 | 0.009 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.010 | 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".