Suppr超能文献

关于重粒子轨迹的拉格朗日随机模型的公式化。

On the Formulation of Lagrangian Stochastic Models for Heavy-Particle Trajectories.

作者信息

Reynolds AM

机构信息

Silsoe Research Institute, Wrest Park, Silsoe, Bedford, MK45 4HS, England, UK

出版信息

J Colloid Interface Sci. 2000 Dec 15;232(2):260-268. doi: 10.1006/jcis.2000.7208.

Abstract

The modeling approach of B. L. Sawford and F. H. Guest ("8th Symposium of Turbulence and Diffusion; San Diego, CA," pp. 96-99. Am. Meteorol. Soc., Boston, MA, 1990) is extended to encompass the formulation of Lagrangian stochastic models for fluid velocities along heavy-particle trajectories in inhomogeneous turbulent flows. The approach ensures consistency with prescribed Eulerian fluid velocity statistics. Models are formulated and then used in conjuction with the equations of motion for heavy particles to simulate the trajectories of heavy particles in vertical turbulent pipe flow. Model predictions for particle-velocity statistics, particle deposition velocities, and mean particle concentrations are shown to be in good agreement with experimental results. In contrast with "eddy-interaction" models but in accord with the results of direct numerical simulations, the models predict a buildup of mean particle concentration within the viscous sublayer at y(+) approximately 0.2. It is suggested that Lagrangian stochastic models for fluid-particle motions provide a good description of fluid velocities along the trajectories of heavy particles, when Lagrangian timescales are appropriately modified. Copyright 2000 Academic Press.

摘要

B. L. 索福德和F. H. 格斯特的建模方法(《湍流与扩散第8次研讨会;加利福尼亚州圣地亚哥》,第96 - 99页。美国气象学会,马萨诸塞州波士顿,1990年)得到扩展,以涵盖非均匀湍流中重粒子轨迹上流体速度的拉格朗日随机模型的公式化。该方法确保与规定的欧拉流体速度统计数据一致。构建模型,然后将其与重粒子的运动方程结合使用,以模拟垂直湍流管道流中重粒子的轨迹。结果表明,模型对粒子速度统计、粒子沉积速度和平均粒子浓度的预测与实验结果吻合良好。与“涡旋相互作用”模型不同,但与直接数值模拟结果一致,这些模型预测在粘性底层中y(+)约为0.2处平均粒子浓度会增加。研究表明,当拉格朗日时间尺度经过适当修正时,流体 - 粒子运动的拉格朗日随机模型能很好地描述重粒子轨迹上的流体速度。版权所有2000年学术出版社。

相似文献

1
On the Formulation of Lagrangian Stochastic Models for Heavy-Particle Trajectories.
J Colloid Interface Sci. 2000 Dec 15;232(2):260-268. doi: 10.1006/jcis.2000.7208.
2
A Lagrangian Stochastic Model for Heavy Particle Deposition.
J Colloid Interface Sci. 1999 Jul 1;215(1):85-91. doi: 10.1006/jcis.1999.6251.
3
A Lagrangian Stochastic Model for the Dispersion and Deposition of Brownian Particles.
J Colloid Interface Sci. 1999 Sep 15;217(2):348-356. doi: 10.1006/jcis.1999.6373.
4
Stokes number effects in Lagrangian stochastic models of dispersed two-phase flows.
J Colloid Interface Sci. 2004 Jul 1;275(1):328-35. doi: 10.1016/j.jcis.2004.02.039.
5
The Lagrangian stochastic model for estimating footprint and water vapor fluxes over inhomogeneous surfaces.
Int J Biometeorol. 2009 Jan;53(1):87-100. doi: 10.1007/s00484-008-0193-0. Epub 2008 Nov 26.
6
Velocity-gradient statistics along particle trajectories in turbulent flows: the refined similarity hypothesis in the Lagrangian frame.
Phys Rev E Stat Nonlin Soft Matter Phys. 2009 Dec;80(6 Pt 2):066318. doi: 10.1103/PhysRevE.80.066318. Epub 2009 Dec 29.
7
Particle dynamics and mixing in the frequency driven "Kelvin cat eyes" flow.
Chaos. 2001 Jun;11(2):351-358. doi: 10.1063/1.1366371.
8
Two-fluid approach for direct numerical simulation of particle-laden turbulent flows at small Stokes numbers.
Phys Rev E Stat Nonlin Soft Matter Phys. 2009 May;79(5 Pt 2):056703. doi: 10.1103/PhysRevE.79.056703. Epub 2009 May 14.
10
Lagrangian statistics across the turbulent-nonturbulent interface in a turbulent plane jet.
Phys Rev E Stat Nonlin Soft Matter Phys. 2013 Oct;88(4):043001. doi: 10.1103/PhysRevE.88.043001. Epub 2013 Oct 8.

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验