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Grid Convergence Index (GCI) 分析SOLPS-ITER网格不确定度

已有 5969 次阅读 2017-4-11 18:58 |系统分类:科研笔记

已完成192*72,96*36,48*18下不同网格计算,对偏滤器脱靶状态,96*36能满足计算精度需求,不确定度为20%。

步骤:

  1. Complete at least 3 simulations (Coarse, medium, fine) with a constant refinement ratio, r, between them (in our example we use r=2)

  2. Choose a parameter indicative of grid convergence. In most cases, this should be the parameter you are studying. ie if you are studying drag, you would use drag.

  3. Calculate the order of convergence, p, using:

    • p=.ln(.frac{(f_3-f_2)}{(f_2-f_1)}) / .ln(r)


  4. Perform a Richardson extrapolation to predict the value at h=0

    • f_{h=0}=f_{fine}+.frac{f_1-f_2}{r^p-1}


  5. Calculate grid convergence index (GCI) for the medium and fine refinement levels

    • GCI=.frac{F_s |e|}{r^p-1}


  6. Ensure that grids are in the asymptotic range of convergence by checking:

    • .frac{GCI_{2,3}}{r^p .times GCI_{1,2}} .approxeq 1

参考文献:

[1]https://www.grc.nasa.gov/WWW/wind/valid/tutorial/spatconv.html

[2]http://tor-eng.com/2016/07/richardson-extrapolation-grid-convergence-index/

[3]https://curiosityfluids.com/2016/09/09/establishing-grid-convergence/



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