Optimal design of heat pipe based on clonal selection algorithm
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Heat pipes are used widely in many engineering fields due to their higher rate of heat transfer.However,the design of heat pipes often involves selection of multi-object parameters,and the conventional design algorithms are not efficient in optimizing these parameters simultaneously.Clonal selection algorithm(CSA) is very useful in solving such a problem because of its strong self-learning and self-adaptive abilities.The purpose of the present work is to design a multi-object optimization algorithm for heat pipe based on the CSA.The thermal network model and heat transfer limit model of a typical heat pipe were built.On this basis,the optimal problem of the heat pipe was defined mathematically,and the algorithm was introduced to solve this problem.The results of several case studies based on this algorithm were discussed.Its effectiveness and superiority were clearly demonstrated.Since the models are built on the basic heat pipe analysis theory,the algorithm can be easily extended to solve the optimal problem of many types of heat pipe.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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 it