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Record W3210665217 · doi:10.1109/jphotov.2021.3116918

Ellipsoidal Optical Cavities for Enhanced Thermophotovoltaics

2021· article· en· W3210665217 on OpenAlexafffund
Nima Talebzadeh, Atousa Pirvaram, Paul G. OaBrien

Bibliographic record

VenueIEEE Journal of Photovoltaics · 2021
Typearticle
Languageen
FieldEngineering
TopicThermal Radiation and Cooling Technologies
Canadian institutionsYork University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsThermophotovoltaicMaterials scienceCommon emitterOpticsPhotovoltaic systemOptoelectronicsPhysics

Abstract

fetched live from OpenAlex

Herein, we present an optical cavity in the form of a prolate ellipsoid that can greatly enhance the performance of solar thermophotovoltaic (STPV) systems. The geometrical parameters of the cavity can be designed to control the degree of photon recycling, the temperature of the emitter within the STPV system, gap distance and effective view factor between the PV cell and the emitter, and to minimize the emission losses. Numerical analysis shows the ellipsoidal optical cavity can be designed to achieve an effective view factor of 88.7% between the emitter and PV cell within an STPV system. Results show an efficiency of 5.62% in an STPV system with a GaSb PV cell and a black-body emitter under solar radiation at a concentration factor of 350X. Further, assuming the surface of the ellipsoidal optical cavity is capable of reflecting 99% of the radiation incident onto its surface, efficiencies of 15.54% can be attained when the solar concentration factor is 1400X. These results are attained for STPV systems without using selective absorbers, emitters or filters. The ellipsoidal optical cavity can be integrated into the design of advanced thermophotovoltaics (TPVs) systems and bring them closer to the high theoretical efficiencies TPV systems are capable of.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.068
Threshold uncertainty score0.619

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 head0.013
GPT teacher head0.231
Teacher spread0.217 · 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

Citations6
Published2021
Admission routes2
Has abstractyes

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