Modeling and simulation of wind turbine heat recycling system
Bibliographic record
Abstract
The paper aims to discuss the power supply and heat supply system of wind turbine, promote the development of wind energy in heat recycling, and expand the application of renewable energy resources in replacing fossil energy. Starting from the construction of wind power heat storage system model, first, molten salt was selected as the fluid heat storage material. Based on the realization process of two-pot molten salt electrothermal transformation, the model of two-pot molten salt heat storage (MSHS) system was established. Second, based on the model of heat storage system, the high temperature MSHS wind power heating system was simulated by using the numerical simulation analysis method. The results showed that the simulation results of the thermal storage system model were highly consistent with the actual results, and the model was accurate and reliable, which was suitable for the simulation analysis of the thermal storage system. After a day of operation, the utilization rate of wind energy of the MSHS wind power heating system could reach more than 94%. The combination of the MSHS wind power system and regional heating had obvious effect on absorbing wind power, saving resources, and solving the problems of wind curtailment. In the MSHS wind power supply heating system, the configuration of MSHS significantly improved the utilization ratio of wind energy in the wind power generation system, even up to 100% at maximum. To sum up, the configuration of MSHS can absorb most of the wind energy generated on that day, thus improving the energy utilization ratio of the wind power generation system.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".