日本フィジカルAI新聞

世界のフィジカルAIを、日本語で。

流体解析arXiv:2611.11286

斜め流入下におけるヘリカル撚りケーブルの横力と後流に及ぼす構造の影響

Cable construction effects on the lateral forces and wakes of helically stranded cables under oblique inflow

シェア:XThreadsFacebookLINEはてブBluesky

斜め流れ中の撚りケーブルをLESで解析し、断面形状や溝の深さが横力と後流構造に与える影響を明らかにした。

著者: Tielin Ma, Junlin Zhang, Jingcheng Fu, Chongwen Jiang, Chuan Ye

分類: physics.flu-dyn, cs.CE, cs.RO

原文アブストラクト

Helically stranded cables in aerial towing systems experience oblique inflow, but are often modeled as smooth cylinders, neglecting three-dimensional cross-stream coupling and surface texture. We perform large-eddy simulations and spectral proper orthogonal decomposition for a smooth cylinder and four stranded cables at four inclinations from $22.5^\circ$ to $90^\circ$, all stationary, with $Re \approx 1.0 \times 10^4$. Aerodynamic differences are governed mainly by cross-sectional symmetry and strand-scale groove depth. Force-source decomposition shows that surface pressure contributes 94.9%--98.3% of the mean normal force and dominates the mean lateral force. High-symmetry, quasi-circular envelopes maintain balanced circumferential pressure, whereas deep-grooved, low-symmetry tri-lobed geometries produce asymmetric separation and a persistent lateral pressure imbalance. In the wake, high-symmetry cables sustain spanwise-synchronized Karman shedding at moderate-to-high inclinations, concentrating fluctuation energy in a dominant low-rank mode. Deep grooves disrupt shedding coherence, deflect the recirculation zone laterally, and spread energy across broadband scales. Mean lateral force and lateral-force fluctuations are therefore decoupled: the quasi-circular $(1 \times 19)$ cable has minimal mean lateral force but strong periodic fluctuations, while the deep-grooved $(3 \times 7)$ cable has weak fluctuations but appreciable mean lateral force. These results support construction-dependent cable selection and aerodynamic load modeling.

  1. 1パナソニックHDら、ロボット操作AI「T-Rex」を共同開発 12タスクで平均成功率65%研究・技術
  2. 2Hugging Face、ロボット学習基盤「LeRobot v0.7.0」を公開 対話操作と大規模データ学習に対応新製品・発表
  3. 3NVIDIA、Vera CPUのエージェント処理性能を公表 x86比で速度・電力効率を比較半導体・GPU
  4. 4Agility、SPAC合併でナスダック上場資金調達
  5. 5智元、清華大学などとCELを発表 96台のロボットで群体学習を検証研究・技術
PR本紙発行元 EmplifAI