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Particle-Clustering Algorithms for the Prediction of Brownout Dust Clouds

机译：颗粒聚类算法用于降尘尘云的预测

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摘要

Particle-clustering methods have been explored for application to the problem of predicting brownout conditions, which can occur when a rotorcraft operates over surfaces covered with loose sediment and stirs up large dust clouds that impede the pilot's outward visibility. A significant issue in such simulations is the tracking of the very large number of particles needed to predict with acceptable fidelity the dual-phase flow properties and optical characteristics of the dust clouds. To this end, computationally efficient Lagrangian approaches were explored for the simulation of dilute carrier-particle suspensions at low Reynolds numbers of the relative particle motion. The algorithms examined were the Gaussian method, the k-means method, and Osiptsov's method, which were all compared in terms of their accuracy versus computational cost against solutions obtained by directly solving for the individual particle motions. Specific results were computed for a prototypical flowfield that mimics the highly unsteady, two-phase vortical particle flow responsible for the development of brownout conditions. It is shown that although clustering algorithms can be problem dependent and have bounds of applicability, in some conditions they offer the potential to significantly reduce the computational costs of brownout dust cloud simulations while still retaining good accuracy.
机译：已经探索出将粒子聚类方法应用于预测电力不足状况的问题，当旋翼航空器在覆盖有疏松沉积物的表面上操作并搅动大尘埃云时，会发生这种情况，这会妨碍飞行员的向外视野。这种模拟中的一个重要问题是跟踪以可接受的保真度预测尘埃云的双相流动特性和光学特性所需的大量粒子。为此，探索了计算上有效的拉格朗日方法，用于在相对粒子运动的低雷诺数下模拟稀载体粒子悬浮液。检查的算法是高斯方法，k均值方法和Osiptsov方法，它们的准确性和计算成本都与直接求解单个粒子运动获得的解决方案进行了比较。计算了一个典型流场的特定结果，该流场模拟了导致欠压条件发展的高度不稳定的两相涡旋颗粒流。结果表明，尽管聚类算法可能依赖于问题并且具有一定的适用性，但是在某些情况下，它们提供了显着降低掉电尘埃云计算的计算成本，同时仍保持良好准确性的潜力。

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  • 来源
    《AIAA Journal》 |2013年第5期|1080-1094|共15页
  • 作者

    Bharath Govindarajan; J. Gordon Leishman; Nail A. Gumerov;

  • 作者单位

    Glenn L. Martin Institute of Technology, Department of Aerospace Engineering, University of Maryland, College Park, Maryland 20742;

    Glenn L. Martin Institute of Technology, Department of Aerospace Engineering, University of Maryland, College Park, Maryland 20742;

    Institute for Advanced Computer Studies, Department of Computer Science, University of Maryland, College Park, Maryland 20742;

  • 收录信息 美国《科学引文索引》(SCI);美国《工程索引》(EI);
  • 原文格式 PDF
  • 正文语种 eng
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