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Record W4310154864 · doi:10.3390/su142315823

Legs Geometry Influence on the Performance of the Thermoelectric Module

2022· article· en· W4310154864 on OpenAlexfundno aff
Abdelkader Rjafallah, Daniel Tudor Cotfas, Petru Adrian Cotfas

Bibliographic record

VenueSustainability · 2022
Typearticle
Languageen
FieldMaterials Science
TopicAdvanced Thermoelectric Materials and Devices
Canadian institutionsnot available
FundersUniversitatea Transilvania din BrasovAgence Universitaire de la Francophonie
KeywordsMultiphysicsThermoelectric generatorSquare (algebra)Thermoelectric effectGeometryFinite element methodVoltageMechanicsMaterials scienceGenerator (circuit theory)Steady state (chemistry)Thermoelectric materialsMechanical engineeringPower (physics)MathematicsPhysicsStructural engineeringEngineeringElectrical engineeringThermodynamicsChemistry

Abstract

fetched live from OpenAlex

The performance of the thermoelectric module highly depends on the geometry of the legs, the module area, and implicitly on the number of the pairs, besides the properties of the materials. The geometry of the legs consists of the shape, the dimensions on three axes, and whether the legs are filled or are hollow. The legs can have one hollow or more, the hole can be from the top to bottom or not. This paper studies and compares the performance of different thermoelectric modules in function of the shape: square, triangular, trapezoid, reverse trapezoid, hourglass, inverse hourglass (filled and with the hollow from the top to the bottom or not), and with different dimensions of the length and width. The simulations are performed using the COMSOL Multiphysics software, where 3D numerical models are developed and solved using the finite element method. The results are compared with others from the specialized literature for a one pair square shape. The current-voltage and power-voltage characteristics have a good matching, which proves the simulations are good and the model can be used for other shapes. A steady-state heating condition is applied to the hot side of the thermoelectric generators, while the cold side is subjected to steady state, natural convection, and forced convection heating conditions. The square shape with an internal hollow is studied first. The best performance when the length and width are 1 mm × 1 mm, 1.5 mm × 1.5 mm, and 2 mm × 2 mm is obtained for the thermoelectric generator with filled square legs. The highest maximum power is obtained for thermoelectric generator with the sizes 2 mm × 2 mm. The gain in power for the square shape in comparison with the worst value of the TEG (Inverse Hourglass for filled and Triangular for hollow) for the three dimensions considered is for those filled 199%, 202%, and 204%, respectively, and for those that are hollow 198%, 232%, and 243%, respectively. The reduction in maximum power is 5%, for the thermoelectric generator with square legs (2 mm × 2 mm) and with hollow legs, in comparison with one filled. The maximum power increases for the thermoelectric generator with square legs which have a hollow interior, in this case 2 mm × 2 mm, with 0.2% and 1% for the thermoelectric generator with sizes of 1 mm × 1 mm. Additionally, the results obtained for the square filled shape are compared with the real ones obtained for a thermoelectric generator with sizes 40 mm × 40 mm × 4 mm. The matching is very good, which confirms that the model can be used for different geometry of the thermoelectric generators in order to help the manufacturers improve their performance.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
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 score0.127
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
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 head0.005
GPT teacher head0.219
Teacher spread0.215 · 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.

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

Citations24
Published2022
Admission routes1
Has abstractyes

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