MétaCan
Menu
Back to cohort
Record W1978853368 · doi:10.1115/icone18-29974

Thermal Aspects of Using Uranium Dicarbide Fuel in an SCWR at Maximum Heat-Flux Conditions

2010· article· en· W1978853368 on OpenAlexaff
Caleb Pascoe, Ashley Milner, Hemal Patel, Wargha Peiman, Graham Richards, Lisa Grande, Igor Pioro

Bibliographic record

Venue18th International Conference on Nuclear Engineering: Volume 2 · 2010
Typearticle
Languageen
FieldMaterials Science
TopicNuclear Materials and Properties
Canadian institutionsOntario Tech University
Fundersnot available
KeywordsSupercritical fluidNuclear engineeringCoolantMaterials scienceElectricity generationNuclear reactorCritical heat fluxThermal efficiencyThermal hydraulicsNuclear powerNuclear fuelHeat fluxMechanical engineeringHeat transferThermodynamicsChemistryPower (physics)Nuclear physicsEngineering

Abstract

fetched live from OpenAlex

There are 6 prospective Generation-IV nuclear reactor conceptual designs. SuperCritical Water-cooled nuclear Reactors (SCWRs) are one of these design options. The reactor coolant in SCWRs will be light water operating at 25 MPa and up to 625°C, actually at conditions above the critical point of water (22.1 MPa and 374°C, respectively). Current Nuclear Power Plants (NPPs) around the world operate at sub-critical pressures and temperatures achieving thermal efficiencies within the range of 30–35%. One of the major advantages of SCWRs is increased thermal efficiency up to 45–50% by utilizing the elevated temperatures and pressures. SuperCritical Water (SCW) behaves as a single-phase fluid. This prevents the occurrence of “dryout” phenomena. Additionally, operating at SCW conditions allows for a direct cycle to be utilized, thus simplifying the steam-flow circuit. The components required for steam generation and drying can be eliminated. Also, SCWRs have the ability to support hydrogen co-generation through thermochemical cycles. There are two main types of SCWR concepts being investigated, Pressure-Vessel (PV) and Pressure-Tube (PT) or Pressure-Channel (PCh) reactors. The current study models a single fuel channel from a 1200-MWel generic PT-type reactor with a pressure of 25 MPa, an inlet temperature of 350°C and an outlet temperature of 625°C. Since, SCWRs are presently in the design phase there are many efforts in determining fuel and sheath combinations suited for SCWRs. The design criterion to determine feasible material combinations is restricted by the following constraints: 1) The industry accepted limit for fuel centreline temperature is 1850°C, and 2) sheath-material-temperature design limit is 850°C. The primary candidate fuel is uranium dioxide. However; previous studies have shown that the fuel centreline temperature of an UO2 pellet might exceed the industry accepted limit for the fuel centreline temperature. Therefore, investigation on alternative fuels with higher thermal conductivities is required to respect the fuel centreline temperature limit. Sheath (clad) materials must be able to withstand the aggressive SCW conditions. Ideal sheath properties are a high-corrosion resistance and high-temperature mechanical strength. Uranium dicarbide (UC2) is selected as a choice fuel, because of its high thermal conductivity compared to that of conventional nuclear fuels such as UO2, Mixed OXide (MOX) and Thoria (ThO2). The chosen sheath material is Inconel-600. This Ni-based alloy has high-yield strength and maintains its integrity beyond the design limit of 850°C. This paper utilizes a generic SCWR fuel channel containing a continuous 43-element bundle string. The bulk-fluid, sheath and fuel-centreline temperature profiles together with Heat Transfer Coefficient (HTC) profile were calculated along the heated length of a fuel channel at the maximum Axial Heat Flux Profiles (AHFPs).

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)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.029
GPT teacher head0.256
Teacher spread0.227 · 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 designBench or experimental
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

Citations4
Published2010
Admission routes1
Has abstractyes

Explore more

Same venue18th International Conference on Nuclear Engineering: Volume 2Same topicNuclear Materials and PropertiesFrench-language works237,207