Modeling and optimal design of heliostat field based on particle swarm optimization algorithm
Bibliographic record
Abstract
Building a new type of power system with new energy as the main body is the goal of achieving "carbon peak" and "carbon neutrality" in China. An important measure of the target. Tower solar thermal power generation is a new type of clean energy technology with low carbon and environmental protection. The annual average optical efficiency of the fixed sun station is affected by shadow occlusion, cosine efficiency, atmospheric transmittance, collector truncation efficiency, mirror reflectance, size layout of heliostat and so on. In this paper, the heliostat field is modeled based on reflection theorem, solar cone theory and solar motion law, and optimized based on particle swarm optimization algorithm to calculate the annual average optical efficiency, annual average output thermal power, and annual average output thermal power per unit mirror area of heliostat field. To solve this problem, The paper need to consider the height and Angle of the sun, the blocking of sunlight, and the cone model of sunlight and other factors, first calculate the sun's height Angle, azimuth Angle and cosine loss, and then calculate the shadow blocking efficiency through the tower shadow blocking, and then calculate the collector truncation efficiency according to the sun light cone theory, etc. The mathematical model of a single heliostat can be modeled, and the average optical efficiency and output thermal power of the heliostat field can be calculated by traversing the average. Then, this paper adopts single objective optimization model and particle swarm optimization algorithm. Firstly, an optimization model is established with the rated power reaching 60MW as the constraint condition, and the annual average output thermal power per unit mirror area is as large as possible to optimize the target. Then particle swarm optimization algorithm is used to find the maximum output thermal power and the final particle convergence, indicating the rationality of the mathematical model.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".