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Record W2068507974 · doi:10.1115/1.4028158

SOFC Lifetime Assessment in Gas Turbine Hybrid Power Systems

2014· article· en· W2068507974 on OpenAlexaff
David Tucker, María Abreu-Sepúlveda, Nor Farida Harun

Bibliographic record

VenueJournal of Fuel Cell Science and Technology · 2014
Typearticle
Languageen
FieldMaterials Science
TopicAdvancements in Solid Oxide Fuel Cells
Canadian institutionsMcMaster University
Fundersnot available
KeywordsSolid oxide fuel cellStack (abstract data type)TurbineAutomotive engineeringProcess engineeringElectricity generationEngineeringDegradation (telecommunications)Power (physics)Nuclear engineeringEnvironmental scienceComputer scienceMechanical engineeringElectrical engineeringAnode

Abstract

fetched live from OpenAlex

The adoption of solid oxide fuel cell (SOFC) technology in power generation has been limited, in no small part, by material degradation issues affecting the stack lifetime, and hence, the economic viability. A numeric study was conducted to determine if the life of an SOFC could be extended when integrated with a recuperated gas turbine system. Dynamic modeling tools developed at the National Energy Technology Laboratory (NETL) for real-time applications were applied to evaluate life to failure for both a standalone SOFC and a hybrid SOFC gas turbine. These models were modified using empirical relations to experimental degradation data to incorporate degradation as a function of current density and fuel utilization. For the control strategy of shifting power to the turbine as fuel cell voltage degrades, the SOFC life could be extended dramatically, significantly impacting the economic potential of the technology.

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)0.001
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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.006
GPT teacher head0.254
Teacher spread0.248 · 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

Citations30
Published2014
Admission routes1
Has abstractyes

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