Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,046 | 0,075 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,003 | 0,003 |
| Études des sciences et des technologies | 0,006 | 0,008 |
| Communication savante | 0,015 | 0,021 |
| Science ouverte | 0,007 | 0,009 |
| Intégrité de la recherche | 0,006 | 0,005 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,010 | 0,004 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».