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液体シート分解時のダイナミックな表面張りは,スプレードロップのサイズを制御します. 漂流の減少とターゲティングの改善のための統一モデル
Zhan Huang1, Zhichong Wang2, Changling Wang3
1College of Science, China Agricultural University, Beijing, China; Institute of Plant Protection, Beijing Academy of Agriculture and Forestry Sciences, Beijing, China; College of Agricultural Unmanned System, China Agricultural University, Beijing, China; Centre for Chemicals Application Technology, China Agricultural University, Beijing, China.
Environmental research
|August 22, 2025
まとめ
新しいモデルは,液体の分解時のダイナミックな表面張力を測定することで,農薬のスプレーの滴の大きさを正確に予測します. 環境汚染を減らし,農業の持続可能性を高める.
科学分野:
- 農業工学
- 物理化学
- 流体力学
背景:
- 農薬のスプレードロップのサイズを正確に制御することは,有効性と環境安全性にとって極めて重要です.
- 現存するモデルでは,スプレーアジュバントを使用すると,ドロップレットの大きさを正確に予測することができません.
- 現在のモデルの不一致は,スプレーの漂流と流出の効率的な管理を妨げています.
研究 の 目的:
- スプレーアドジュバントを考慮した統一されたドロップレットサイズ予測モデルを開発する.
- 重要な予測パラメータとして,液体シート分解時のダイナミックな表面張力を導入します.
- 農薬の噴射モデルの正確性と信頼性を向上させる.
主な方法:
- 液体シート分解時間 (TLSB) で動的表面張力 (DST) を含む統一されたドロップレットサイズ予測モデルを開発した.
- 様々な条件で実験的にTLSBを決定するために粒子画像速度測定 (PIV) を利用した.
- 水と補助剤の溶液を TLSB で測定し,統一モデルを校正する.
主要な成果:
- 統一モデルは,水と補助剤の溶液の予測定数を順調に調整し,偏差を89%削減しました.
- ドロップレットサイズ (R2 > 0. 99) の高い予測精度を達成しました.
- ドロップレットサイズ予測の平方根平均誤差を 13.0% 減少させた.
結論:
- 新しいモデルは,助剤の選択と農薬の適用における操作パラメータを最適化するための強力なツールを提供します.
- 予測の精度が向上し,よりターゲットを絞った殺虫剤の投与が可能になり,非ターゲットの堆積が減少します.
- この進歩は,スプレーの漂流を減らし,化学物質の流れを最小限に抑え,農業における環境の持続可能性を高めるのに寄与しています.
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