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Record W4399044588 · doi:10.3390/en17112562

Harvesting Electric Energy Using Thermoelectric Generators in a Residential Heating Application

2024· article· en· W4399044588 on OpenAlexafffund
Ugochukwu Chukwurah, Gordon McTaggart-Cowan

Bibliographic record

VenueEnergies · 2024
Typearticle
Languageen
FieldMaterials Science
TopicAdvanced Thermoelectric Materials and Devices
Canadian institutionsSimon Fraser University
FundersMitacs
KeywordsThermoelectric effectThermoelectric generatorElectric energyEnvironmental scienceEnergy (signal processing)Electrical engineeringEngineering physicsMaterials scienceEngineeringPhysicsPower (physics)Thermodynamics

Abstract

fetched live from OpenAlex

Biomass combustors provide space heating by converting chemical energy in woody biomass into low-temperature thermal energy. Thermoelectric generators (TEGs) can generate electricity from the heat flux without significantly reducing heating performance. However, most current TEGs are small (40 mm × 40 mm), requiring many TEG elements to generate useful power from a biomass combustion-based space heater. This work compares the electrical generation potential of an array of small TEGs with a larger (80 mm × 120 mm) TEG in a vertical configuration representative of a residential heating appliance. An experimental facility was developed for various representative cold-side ducts and controllable hot-side temperature and cooling airflows, and the Taguchi method was used to evaluate the impacts of temperature, airspeed, and ducting configurations. The results indicate that temperature and airspeed significantly influence TEG power, while ducting configurations have an insignificant influence. The large TEG achieved more consistent temperatures but produced lower power than an array of smaller TEGs with the same total area. The study emphasizes optimizing TEG design and operating conditions to enhance electricity generation efficiency in space heating combustors.

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 score0.077
Threshold uncertainty score0.732

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.010
GPT teacher head0.258
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 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
Published2024
Admission routes2
Has abstractyes

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