Using dual mutation particle swarm method to optimize the variable cross-section of a thermoelectric generator based on a comprehensive thermodynamic model
Bibliographic record
Abstract
The shape design for a thermoelectric generator (TEG) plays an important role in its performance. In this paper, a hyperbolic function was introduced to design a variable cross-section TEG module to optimize the configuration for maximum power generation and efficiency was sought. A comprehensive thermodynamic model was applied to establish the governing equations for the newly designed TEG module. The mutation particle swarm optimization (MPSO) method was invoked to solve the thermodynamic model. The thermodynamic model's output results include the temperatures at both ends of the TE element, thus making it possible to evaluate the actual performance of the TEG module. The results indicate that both the power generation and efficiency of the hyperbolic TEG are superior to those obtained based on a traditional design. The studies also disclosed that the hyperbolic structure can increase the thermal resistance of the TE couple making it possible to enlarge the temperature difference. This is the main mechanism to improve the performance of a hyperbolic TEG. Besides, the four non-dimensional parameters (shape parameter (β), area ratio (μ), temperature ratio (θ), and resistance ratio (rx)) related to the geometric structure and working conditions have notable effects on the TEG performance. It is thus worthwhile optimizing the TEG power generation and efficiency in the variable searching space of these parameters. However, differing from the traditional optimization, it is necessary to solve the governing equations in every iteration when searching for an optimal configuration based on the comprehensive model. In order to overcome the challenge, the Dual-MPSO algorithm was used in this research.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".