MétaCan
Menu
Back to cohort
Record 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 on OpenAlexaboutno aff
Sunil Nijhawan, Yong-Mann Song

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldMaterials Science
TopicNuclear Materials and Properties
Canadian institutionsnot available
Fundersnot available
KeywordsAccident (philosophy)Accident managementGuard (computer science)Code (set theory)Nuclear engineeringNuclear reactor coreNuclear powerComputer scienceMileForensic engineeringEngineeringOperations researchNuclear physicsPhysics

Abstract

fetched live from 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.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.031
Threshold uncertainty score0.062

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0020.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0090.001

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.

Opus teacher head0.093
GPT teacher head0.346
Teacher spread0.253 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2020
Admission routes1
Has abstractyes

Explore more

Same topicNuclear Materials and PropertiesFrench-language works237,207