Effect of Convection Heat Transfer on Performance of Waste Heat Thermoelectric Generator
Bibliographic record
Abstract
Efficiency of energy conversion processes can be improved if waste heat is converted to electricity. A thermoelectric generator (TEG) can directly convert waste heat to electricity. The TEG typically suffers from low efficiency due to various reasons, such as ohmic heating, surface-to-surrounding convection losses, and unfavorable material properties. In this work, the effect of surface-to-surrounding convection heat transfer losses on the performance of TEG is studied analytically and numerically. A one-dimensional (1-D) analytical model is developed that includes surface convection, conduction, ohmic heating, and Peltier, Seebeck, and Thomson effects with top and bottom surfaces of TEG exposed to convective boundary conditions. Using the analytical solutions, different performance parameters (e.g., heat input, power output, and efficiency) are calculated and expressed graphically as functions of thermal source and sink temperatures and convection heat transfer coefficient. Finally, a two-dimensional (2-D) mathematical model is solved numerically to observe qualitative results of thermal and electric fields inside the TEG. For all calculations, temperature-dependent thermal/electric properties are considered. Increase in thermal source temperature results in an increase in the power output with adiabatic side wall conditions. A change in boundary condition to convection heat transfer from adiabatic boundary has a large impact on thermal efficiency.
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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 | 0.001 |
| 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 | 0.001 | 0.001 |
| 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".