Optimization of low pressure chemical vapour deposition reactors using hybrid differential evolution
Bibliographic record
Abstract
Abstract In this study, hybrid differential evolution (HDE) was applied to solve four low‐pressure chemical vapour deposition (LPCVD) reactor optimal design problems. The mathematical model for this reactor is described using a two‐point boundary value differential‐algebraic equation (TPBVP‐DAE) problem. HDE is not only applied to solve the optimization problems but also to obtain the solution to TPBVP‐DAE. Under this situation, the HDE subroutine should call itself to evaluate the optimal solution to the optimization problem and the solution to TPBVP‐DAE. In this study, Fortran 90 was used to implement the HDE subroutine to achieve the calling itself requirement. The recursive HDE subroutine can be efficiently applied to solve the four LPCVD reactor optimal design problems. From the computational results, we observed that the combined optimal design obtain the smallest axial uniformity variation. Furthermore, test function maximization problems were used to compare the performance of the HDE with other methods.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".