Energy Conversion by Nanomaterial-Based Trapezoidal-Shaped Leg of Thermoelectric Generator Considering Convection Heat Transfer Effect
Bibliographic record
Abstract
Thermoelectric generators (TEGs) can harvest energy without any negative environmental impact using low potential sources, such as waste heat, and subsequently convert that energy into electricity. Different shaped leg geometries and nanostructured thermoelectric materials have been investigated over the last decades in order to improve the thermal efficiency of the TEGs. In this paper, a numerical study on the performance analysis of a nanomaterial-based (i.e., p-type leg composed of BiSbTe nanostructured bulk alloy and n-type leg composed of Bi2Te3 with 0.1 vol % SiC nanoparticles) trapezoidal-shaped leg geometry has been investigated considering the Seebeck effect, Peltier effect, Thomson effect, Fourier heat conduction, and surface to surrounding irreversible heat transfer loss. Different surface convection heat transfer losses (h) are considered to characterize the current output, power output, and thermal efficiency at various hot surface (TH) and cold surface (TC) temperatures. Good agreement has been achieved between the numerical and analytical results. Moreover, current numerical results are compared with previous related works. The designed nanomaterial-based TEG shows better performance in terms of output current and thermal efficiency with the thermal efficiency increasing from 7.3% to 8.7% using nanomaterial instead of traditional thermoelectric materials at h = 0 W/m2K while TH is 500 K and TC is 300 K.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".