Optimization Design of Online Mixing Apparatus and Mixing Performance Experiment for Crop Protection Equipment
Bibliographic record
Abstract
At present, the premixing method is used in the operation of plant protection machines. Premixing and dispensing often lead to uneven proportioning and mixing, thereby reducing the utilization rate of pesticides. There is a great potential safety hazard. In order to improve the effective utilization rate of pesticides in spray operations, an online hybrid system is designed. Based on the theory of computational fluid dynamics, the static mixer model with built-in spoiler was established, and the numerical analysis was carried out by using FLUENT software. The simulation results were analyzed by evaluating the coefficient of variation of mixing uniformity and consistency. Under pressure, the coefficient of variation of pesticide solution mixing was 3.2%. Then carmine solution was used to replace pesticide, and UV spectrophotometry was used to test. The results show that the maximum relative error of mixing stability at the nozzle of the mixing system is 4.301% and the maximum coefficient of variation of pesticide solution is 3.989% under pressurized condition. The experimental coefficient of variation is basically consistent with the simulation results. Practice has proved that the designed online hybrid system has good mixing effect and can be used for the later stage variable spray test of plant protection equipment.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".