Numerical simulation and optimization analysis of thermal balance of heavy oil box-type substation louver arrangement
Bibliographic record
Abstract
In the field operation of heavy oil box-type substation, the overall arrangement is compact, which leads to the decrease of heat sink capacity and the "heat island effect", which leads to the increase of the temperature of the box-type substation. In this paper, the thermal equilibrium model of the box-type power station was established and the temperature field distribution and air flow of the box-type power station were simulated. The results showed that the parallel vertical layout program is not conducive to the air flow of the box, resulting in rising of the temperature around the plant, especially the box body temperature. According to the heat dissipation theory of blinds, the box-type power station is rearranged. The distribution of temperature field in tank power station with different container inclination under natural ventilation and no-wind condition were researched. The results show that the shutter layout can significantly reduce the temperature between the containers. When the inclination angle is 45 ° , the heat balance effect is the best. In order to study the comprehensive effect of spacing and angle on the heat dissipation of the box-type power station, the orthogonal test was used to optimize the distance and angle of the container of the box-type power station. The results show that the heat balance effect is the best when the container spacing is 8m and the inclination angle is 45 ° . The calculation results provide a basis for the cooling of the box-type power plant, thereby improving the operation efficiency of the power station and reducing the cost of establishing the station.
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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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".