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Record W1499705993 · doi:10.1063/1.2711717

TPV Power Generation System Using a High Temperature Metal Radiant Burner

2007· article· en· W1499705993 on OpenAlexaff
K. Qiu, A.C.S. Hayden, Evgueniy Entchev

Bibliographic record

VenueAIP conference proceedings · 2007
Typearticle
Languageen
FieldEngineering
TopicThermal Radiation and Cooling Technologies
Canadian institutionsNatural Resources Canada
Fundersnot available
KeywordsThermophotovoltaicCombustorEmissivityRadiant energyMaterials scienceCommon emitterElectricity generationOptoelectronicsPower densityEnergy transformationCombustionElectric powerNuclear engineeringRadiationPower (physics)OpticsEngineeringChemistry

Abstract

fetched live from OpenAlex

Interest has grown in micro‐combined heat and power (micro‐CHP). Thermophotovoltaic (TPV) generation of electricity in fuel‐fired furnaces is one of the micro‐CHP technologies that are attracting technical attention. Previous investigations have shown that a radiant burner that can efficiently convert fuel chemical energy into radiation energy is crucial to realize a practical TPV power system. In this work, we developed a TPV power generation system using a gas‐fired metal radiant burner. The burner consists of a high temperature alloy emitter, which could have an increased emissivity at short wavelengths and low emissivity at long wavelengths. The metal emitter is capable of bearing high temperatures of interest to fuel‐fired TPV power conversion. GaSb TPV cells were tested in the combustion‐driven radiant source. Electric output characteristics of the TPV cells were investigated at various operating conditions. The electric power output of the TPV cells was demonstrated to be promising. At an emitter temperature of 1185°C, an electric power density of 0.476 W/cm2 was generated by the GaSb cells. It is shown that the metal emitter is attractive and could be applied to practical fuel‐fired TPV power systems.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.540
Threshold uncertainty score0.698

Codex and Gemma teacher scores by category

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 communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.017
GPT teacher head0.218
Teacher spread0.200 · 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 teacher head, 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

Citations1
Published2007
Admission routes1
Has abstractyes

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