MétaCan
Menu
Retour à la cohorte
Enregistrement W3094444321 · doi:10.1115/icone2020-16633

Simulation of Severe Accident Progression Using ROSHNI: A New Integrated Simulation Code for PHWR Severe Accidents

2020· article· en· W3094444321 sur OpenAlexaboutno aff
Sunil Nijhawan, Yong-Mann Song

Notice bibliographique

Revuenon disponible
Typearticle
Langueen
DomaineMaterials Science
ThématiqueNuclear Materials and Properties
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésAccident (philosophy)Accident managementGuard (computer science)Code (set theory)Nuclear engineeringNuclear reactor coreNuclear powerComputer scienceMileForensic engineeringEngineeringOperations researchNuclear physicsPhysics

Résumé

récupéré en direct d'OpenAlex

Abstract As analysts still grapple with understanding core damage accident progression at Three Mile Island and Fukushima that caught the nuclear industry off-guard once too many times, one notices the very limited detail with which the large reactor cores of these subject reactors have been modelled in their severe accident simulation code packages. At the same time, modelling of CANDU severe accidents have largely borrowed from and suffered from the limitations of the same LWR codes (see IAEA TECDOC 1727) whose applications to PHWRs have poorly caught critical PHWR design specifics and vulnerabilities. As a result, accident management measures that have been instituted at CANDU PHWRs, while meeting the important industry objective of publically seeming to be doing something about lessons learnt from say Fukushima and showing that the reactor designs are oh so close to perfect and the off-site consequences of severe accidents happily benign. Integrated PHWR severe accident progression and consequence assessment code ROSHNI can make a significant contribution to actual, practical understanding of severe accident progression in CANDU PHWRs, improving significantly on the other PHWR specific computer codes developed three decades ago when modeling decisions were constrained by limited computing power and poor understanding of and interest in severe core damage accidents. These codes force gross simplifications in reactor core modelling and do not adequately represent all the right CANDU core details, materials, fluids, vessels or phenomena. But they produce results that are familiar and palatable. They do, however to their credit, also excel in their computational speed, largely because they model and compute so little and with such un-necessary simplifications. ROSHNI sheds most previous modelling simplifications and represents each of the 380 channels, 4560 bundle, 37 elements in four concentric ring, Zircaloy clad fuel geometry, materials and fluids more faithfully in a 2000 MW(Th) CANDU6 reactor. It can be used easily for other PHWRs with different number of fuel channels and bundles per each channel. Each of horizontal PHWR reactor channels with all their bundles, fuel rings, sheaths, appendages, end fittings and feeders are modelled and in detail that reflects large across core differences. While other codes model at best a few hundred core fuel entities, thermo-chemical transient behaviour of about 73,000 different fuel channel entities within the core is considered by ROSHNI simultaneously along with other 15,000 or so other flow path segments. At each location all known thermo-chemical and hydraulic phenomena are computed. With such detail, ROSHNI is able to provide information on their progressive and parallel thermo-chemical contribution to accident progression and a more realistic fission product release source term that would belie the miniscule one (100 TBq of Cs-137 or 0.15% of core inventory) used by EMOs now in Canada on recommendation of our national regulator CNSC. ROSHNI has an advanced, more CANDU specific consideration of each bundle transitioning to a solid debris behaviour in the Calandria vessel without reverting to a simplified molten corium formulation that happily ignores interaction of debris with vessel welds, further vessel failures and energetic interactions. The code is able to follow behaviour of each fuel bundle following its disassembly from the fuel channel and thus demonstrate that the gross assumption of a core collapse made in some analyses is wrong and misleading. It is able to thus demonstrate that PHWR core disassembly is not only gradual, it will be also be incomplete with a large number of low power, peripheral fuel channels never disassembling under most credible scenarios. The code is designed to grow into and use its voluminous results in a severe accident simulator for operator training. It’s phenomenological models are able to examine design inadequacies / issues that affect accident progression and several simple to implement design improvements that have a profound effect on results. For example, an early pressure boundary failure due to inadequacy of heat sinks in a station blackout scenario can be examined along with the effect of improved and adequate over pressure protection. A best effort code such as ROSHNI can be instrumental in identifying the risk reduction benefits of undertaking certain design, operational and accidental management improvements for PHWRs, with some of the multi-unit ones handicapped by poor pressurizer placement and leaky containments with vulnerable materials, poor overpressure protection, ad-hoc mitigation measures and limited instrumentation common to all CANDUs. Case in point is the PSA supported design and installed number of Hydrogen recombiners that are neither for the right gas (designed mysteriously for H2 instead of D2) or its potential release quantity (they are sparse and will cause explosions). The paper presents ROSHNI results of simulations of a postulated station blackout scenario and sheds a light on the challenges ahead in minimizing risk from operation of these otherwise unique power 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 enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,001
score de la tête « metaresearch » (Gemma)0,002
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Simulation ou modélisation · Signal consensuel: Simulation ou modélisation
GenreSignal candidat: Empirique · Signal consensuel: aucune
Score de désaccord entre enseignants0,031
Score d'incertitude au seuil0,062

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0010,002
Méta-épidémiologie (sens strict)0,0010,000
Méta-épidémiologie (sens large)0,0010,001
Bibliométrie0,0010,000
Études des sciences et des technologies0,0010,001
Communication savante0,0010,001
Science ouverte0,0020,001
Intégrité de la recherche0,0010,002
Charge utile insuffisante (le modèle a refusé de juger)0,0090,001

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.

Tête enseignante Opus0,093
Tête enseignante GPT0,346
Écart entre enseignants0,253 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeSimulation ou modélisation
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2020
Routes d'admission1
Résumé présentoui

Explorer davantage

Même sujetNuclear Materials and PropertiesTravaux en français237 207