CFD Best Practice Recommendations——流体分析最佳实践建议

2016-10-06  by:CAE仿真在线  来源:互联网

这是《CFD Best Practice Recommendations》的翻译。英文看起来无感,翻译过来看着更方便。这里面大部分都是CFD工程师都知道的常识,但还是有一些很有价值的建议。英文原文参考附录。
对于网格的建议
关键是要确保网格smooth,避免网格size和形状的突然变化,因为这会导致计算精度的显著降低。因此要注意以下方面:
1. 定义计算域,减小远场环境条件对流动的影响和交互作用。尤其:
-将inlet和outlet边界设置在关切区域尽量远的的地方,尤其当应用了均一的远场条件时,应当确保这个边界所在的区域的流动没有显著的变化。
-避免将inlet或outlet放置在几何形状剧烈变化的区域或者a region of recirculiaton.
2. 避免网格密度或size的跳跃式变化
3. 避免网格的高度失真和尖锐的网格夹角
4. 确保网格stretching是连续的
5. 避免边界层区域有非结构的四面体网格
6. 将物理量(压力,速度等)梯度较大的区域的网格细化,例如边界层,机翼的leading eadge以及任何流动特性有大的改变的区域
7. 确保边界层网格的层数足够满足你希望的计算精度,避免边界层在厚度方向少于10个point。(就是边界层尽量不要少于10层的意思呗?有必要这么多吗?)
8. 检查这些网格质量参数 aspect ratio, internal angle, concavity, skewness, negative volume

对solution assessment的建议

1. 仔细检查边界条件以保证正确性和与流动的兼容性?
2. 运行校核前所有的数值和参数设置
3. 校核初始条件是否合适
4. 监视计算收敛情况。除了残差外,还推荐监视一些对你所仿真的问题有代表性的物理量,如指定区域的 drag force,速度,温度或者压力等
5. 观察收敛曲线的变化情况。如果收敛曲线振荡或者残差减少量在某一范围内变化,就表示求解过程有一些不精确的方面
6. 采用一些内部的一致性和精确性条件 by verifying: 
-一些物理量的守恒,如稳态流动的总热焓和质量流量。
-非粘性流动的熵增率和阻力系数应该是0,他们能强烈地反映出数值发散的影响.(就是如果非0的话计算的准确性就有问题的意思吧)
7. 必要的时候比较不同网格size时的计算结果,确认计算对网格size的依赖性
8. 一些物理量对误差源比其它物理量更为敏感,如压力不如切应力敏感,切应力不如温度梯度或者heat flux敏感
9. 如果你的计算看起来很难收敛,你可以:
-从残差分布情况和与其相关的流域找线索,例如,残差很大的区域或者某些流体特性物理量处于异常水平的区域
-减小控制收敛的参数,例如 CFL number(?)或者一些松弛因子。
-考虑尝试不同的初始化条件
-确认网格质量对收敛性的影响
-先用更稳健的算法计算出一个初始流场,如先用一阶精度计算,然后再切换到更精确的算法   

对EVALUATION OF UNCERTAINTIES的建议
(这里的不确定性,我理解是指仿真与实际物理问题之间的差异)

这是个非常难的问题,因为不确定性通常并没有一个清晰的定义,并且需要对流动的物理特性有很好的判断。下面是一些建议
1. 尝试列出最重要的那些不确定因素,例如
 -几何模型的简化和制造公差
 -operational condition条件 如 入口速度或者入口的流入角度
 -模型对实际物理问题的近似处理,如将低马赫数的可压缩流体近似成不可压缩流体,这种类型的不确定性是可量化和可控的。
 -与湍流模型或其他物理模型相关的不确定性。
2. 进行一些敏感性分析,以确定某些不确定因素对仿真结果的影响程度



原文:



RECOMMENDATIONS ON GRIDS

The key recommendation is to ensure smooth grids, avoiding abrupt changes in grid size or shape, as this can lead to a significant loss of accuracy. Hence take good care to:

• Define the computational domain, in order to minimize the influence and interactions between the flow and the far-field conditions. In particular,

– Place inlet and outlet boundaries as far away as possible from the region of interest. In particular, if uniform far-field conditions are imposed, you should ensure that the boundary is not in a region where the flow may still vary significantly.

– Avoid inlet or outlet boundaries in regions of strong geometrical changes or in regions of recirculation.

• Avoid jumps in grid density or in grid size.

• Avoid highly distorted cells or small grid angles.

• Ensure that the grid stretching is continuous.

• Avoid unstructured tetrahedral meshes in boundary layer regions.

• Refine the grids in regions with high gradients, such as boundary layers, leading edges of airfoils and any region where large changes in flow properties might occur.

• Make sure that the number of points in the boundary layers is sufficient for the expected accuracy. Avoid less than 10 points over the inner part of the boundary layer thickness.

• Monitor the grid quality by adequate mesh parameters, available in most of the grid generators, such as aspect ratio, internal angle, concavity, skewness, negative volume.

RECOMMENDATIONS ON SOLUTION ASSESSMENT

Once you run your code, the following recommendations will be useful to enhance your confidence in the results obtained:

• Check very carefully the selected boundary conditions for correctness and compatibility with the physics of the flow you are modeling.

• Verify all the numerical settings and parameters, before launching the CFD run.

• Verify that your initial solution is acceptable for the problem to be solved.

• Monitor the convergence to ensure that you reach machine accuracy. It is recommended to monitor, in addition to the residuals, the convergence of representative quantities of your problem, such as a drag force or coefficient, a velocity, temperature or pressure at selected points in the flow domain.

• Look carefully at the behavior of the residual convergence curve in function of number of iterations. If the behavior is oscillatory, or if the residual does not converge to machine accuracy by showing a limit cycle at a certain level of residual reduction, it tells you that some inaccuracy affects your solution process.

• Apply internal consistency and accuracy criteria, by verifying:

– Conservation of global quantities such as total enthalpy and mass flow in steady flow calculations.

– The entropy production and drag coefficients with inviscid flows, which are strong indicators of the influence of numerical dissipation, as they should be zero.

• Check, whenever possible, the grid dependence of the solution by comparing the results obtained on different grid sizes.

• Some quantities are more sensitive than others to error sources. Pressure curves are less sensitive than shear stresses, which in turn are less sensitive than temperature gradients or heat fluxes, which require finer grids for a given accuracy level.

• If your calculation appears difficult to converge, you can

– Look at the residual distribution and associated flow field for possible hints, e.g. regions with large residuals or unrealistic levels of the relevant flow parameters.

– Reduce the values of parameters controlling convergence, such as the CFL number or some under-relaxation parameter, when available.

– Consider the effects of different initial flow conditions.

– Check the effect of the grid quality on the convergence rate.

– Use a more robust numerical scheme, such as a first order scheme, during the initial steps of the convergence and switch to more accurate numerical schemes as the convergence improves.

RECOMMENDATIONS ON EVALUATION OF UNCERTAINTIES

• This is a very difficult issue, as the application uncertainties are generally not well defined and require a sound judgment about the physics of the considered flow problem. Some recommendations can be offered:

• Attempt to list the most important uncertainties, such as

– Geometrical simplifications and manufacturing tolerances around the CAD definition.

– Operational conditions, such as inlet velocity or inlet flow angle.

– Physical approximations, such as handling an incompressible flow as a low Mach number compressible flow. This type of uncertainty is manageable, as it can more easily be quantified.

– Uncertainties related to turbulence or other physical models.

• Perform a sensitivity analysis of the relevant uncertainty to investigate its influence.




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