Optimizing the Performance of Parabolic Trough Solar Collectors Using the Grey Wolf Algorithm
Bibliographic record
Abstract
The current study proposes a Grey Wolf Optimizer method for parabolic trough solar collector (PTSC) design optimization. The hunting and leadership styles of Grey Wolf packs serve as models for the Grey Wolf Optimizer (GWO). Compared to other swarm-based algorithms, GWO has a number of advantages. It does not require derived knowledge during the first search and is very flexible to different optimization situations. Its ability to avoid local optima is further improved by the fact that it requires few initial settings. Thermal and energetic efficiency, which are crucial performance indicators for PTSCs, are the selected objective functions. The PTC receiver tube's inlet and outlet diameters, as well as the inlet temperature, are design factors. To enhance PTC performance without raising expenses, the volume of the PTSC material is maintained constant throughout the optimization process. In this work, mathematical programming models were formulated using a multi-objective optimization strategy. MATLAB was used to create and implement these models. Both thermal and exergetic efficiencies are effectively maximized by the suggested optimization strategy. Researchers in the field who want to examine and improve PTSC performance can easily use the optimization model. Researchers can quickly choose their preferred optimal locations for the study and construction of PTSCs by using the Pareto fronts that are currently in use.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".