Novel regenerator design for caloric cycles using artificial neural network — Genetic algorithm method and additive manufacturing
Bibliographic record
Abstract
The regenerator is a key component to determine the performance of the active regenerative caloric cycle. Although the geometry of the regenerator is an important factor, very limited geometries have been used in prototypes due to the limitations of conventional manufacturing. Therefore, this study proposed a new type of regenerator with a high heat transfer rate for the caloric cycle, which is a packed rod bed, using additive manufacturing. In order to ensure a systematic approach for the new design of the regenerator, a new design optimization using an artificial neural network — genetic algorithm with the help of computational fluid dynamics was introduced. Artificial neural network models were used to predict the j and f factors of the packed rod bed and showed a mean relative error of less than 2.0% for the j factor, and a mean relative error of less than 7.5% for the f factor. The accurate results of artificial neural networks contribute to improving the optimization process. The regenerator optimized through the artificial neural network — genetic algorithm method increased the system efficiency by 4.7% and the cooling capacity by 13.0% compared to the baseline caloric cycle using a parallel plate matrix. Considering that caloric cycles are still in the development stage due to the lack of performance of the magnetocaloric cycle, this eventually may contribute to the commercialization and energy saving of the magnetic refrigeration cycle while the design optimization might also help to improve the performance of other caloric cycles.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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) | 0.000 | 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 teacher head, 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".