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Record W4306923103 · doi:10.1149/1945-7111/ac9c32

Modeling of Reversible Solid Oxide Cell Stacks with an Open-Source Library

2022· article· en· W4306923103 on OpenAlexaff
Shidong Zhang, Roland Peters, Bob Varghese, Robert Deja, Nicolas Kruse, Steven Beale, L. Blum, Ralf Peters, Rüdiger‐A. Eichel

Bibliographic record

VenueJournal of The Electrochemical Society · 2022
Typearticle
Languageen
FieldMaterials Science
TopicAdvancements in Solid Oxide Fuel Cells
Canadian institutionsQueen's University
Fundersnot available
KeywordsStack (abstract data type)Work (physics)MechanicsDiffusionMass transferHeat transferSteady state (chemistry)OxideNuclear engineeringThermodynamicsMaterials scienceComputer scienceChemistryPhysicsEngineeringPhysical chemistry

Abstract

fetched live from OpenAlex

This work describes a recently-developed numerical model for three-dimensional, steady-state simulations of reversible solid oxide cell (rSOC) stacks, taking into account a heterogeneous temperature field. The model employs a volume-averaged approach, also referred to as the distributed resistance analogy. It considers fluid flow, multi-component species diffusion, as well as heat and mass transfer, including thermal radiation and electrochemical reactions. The implementation of the computational model is based on an open-source library, OpenFOAM. An in-house designed rSOC stack, Mark-H is considered. Simulations are performed for repeating units with a 320 cm2 active area, with both the present stack model and a one-dimensional Simulink model. Both models predict very similar voltages, with a maximum difference of 2% compared to experimental results. The present model shows a temperature distribution closer to the experimental data than the Simulink model, although a slightly longer simulation time is required.

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)0.001
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: Methods · Consensus signal: Methods
Teacher disagreement score0.005
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0020.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.012
GPT teacher head0.248
Teacher spread0.236 · 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
GenreMethods

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

Citations12
Published2022
Admission routes1
Has abstractyes

Explore more

Same venueJournal of The Electrochemical SocietySame topicAdvancements in Solid Oxide Fuel CellsFrench-language works237,207