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Updated: Feb 14, 2026

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バーチャルレイキャスティングアルゴリズムに基づくポリウレタン固化バルストベッドのメソスコピック異質モデリング方法
Yang Xu1,2, Zhaochuan Sheng3, Jingyu Zhang1,2
1Railway Engineering Research Institute, China Academy of Railway Sciences Corporation Limited, Beijing 100081, China.
Materials (Basel, Switzerland)
|February 13, 2026
まとめ
この研究は,X線コンピュータトモグラフィー (XCT) を回避するポリウレタン固化弾圧床 (PSBB) の新しいメソスケールモデリング方法を提示しています. このアプローチは,弾圧床システムにおける改善された機械的反応のためにスリーパー幅を最適化します.
科学分野:
- 土木工学 土木工学とは
- マテリアルサイエンス 材料科学
- コンピューティング・メカニクス コンピュータ・メカニクス
背景:
- ポリウレタン固化バラストベッド (PSBB) のような複合材料のメソスケールモデリングは,伝統的にX線コンピュータトモグラフィー (XCT) に依存しています.
- 既存の方法は,標本のサイズ,コスト,複雑な後処理の制限に直面しています.
研究 の 目的:
- XCTの必要性を回避するPSBBのための新しいメソスケールモデリング方法論を開発する.
- 粒子結合複合材料の機械的振る舞いを分析するための計算的に効率的で正確なアプローチを確立する.
主な方法:
- 離散要素法 (DEM) と有限要素法 (FEM) の結合.
- 3Dレーザースキャンによる高精度離散要素幾何学の再構築.
- 異質な材料のメソスケール再構築のための仮想レイキャスティングアルゴリズムを使用.
- モデルの精度とメッシュ収束の検証を,実験室での単軸圧縮試験で行います.
主要な成果:
- モデルの精度のために最小粒子のサイズ (0.4 Dmin) の0.4倍の最適なメッシュサイズを決定しました.
- バラストベッドの幅の少なくとも0.73倍 (0.73 Wb) のスリーパー幅は,最適なストレス拡散と位移制御を達成することを特定しました.
- 弾圧床の機械的反応を分析するための方法の有効性を実証した.
結論:
- 開発されたDEM-FEMカップリングメソッドは,PSBBのメソスケールモデリングのためのXCTの効率的な代替案を提供します.
- この発見は,弾圧床の性能を向上させるため,スリーパーサイズを最適化するためのガイドラインを提供します.
- この方法論は,他の多相粒子結合器複合システムにも広く適用できます.
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