FLUENT的單精度和雙精度求解器

2016-09-30 by:CAE仿真在線來源:互聯(lián)網(wǎng)

單精度和雙精度的區(qū)別:

單精度:具有7位精確小數(shù)位。

雙精度:具有15位精確小數(shù)位。

另外,雙精度需要占用兩倍的內存空間,需要更長的時間完成計算。

看了一遍FLUENT幫助文件中對如何選擇單精度和雙精度求解器的說明:

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對大多數(shù)算例來說,單精度求解器已經(jīng)足夠精確。但是對以下幾種情況,雙精度求解器更適合:

1.流域具有顯著的長度尺度(例如一根細長的管道),用單精度表示點坐標可能不夠精確。

2.流域是幾個區(qū)域,彼此之間用小尺寸的管道連接起來(例如汽車發(fā)動機歧氣管),其中的一個區(qū)域的氣壓大大高于整個流域的平均水平,這種情況有必要用雙精度方案來求解壓差。

3.對于涉及到高的熱傳導率的共軛問題(共軛問題,我的理解是兩個區(qū)域的相鄰邊界傳熱或者邊界和區(qū)域內流體相互傳熱)、或網(wǎng)格單元的長寬高尺寸比率很大的網(wǎng)格(扁的或狹長的網(wǎng)格),單精度求解器不能有效地傳遞邊界信息,會導致計算不收斂和不精確。

4.對于采用了population balance模式求解particle size分布可能包含數(shù)個數(shù)量級跨度的statistical moments的的多相流問題,適合用雙精度求解器。(沒用過population balance模式,完全不知道這一條什么意思 FLUENT的單精度和雙精度求解器

)

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另外,我個人的經(jīng)驗,當計算瞬態(tài)問題的時間步長很小時,用雙精度求解器可以獲得更好的收斂性。

附:FLUENT幫助文件原文:

Single-Precision and Double-Precision Solvers

Both single-precision and double-precision versions of ANSYS FLUENT are available on all computer platforms. For most cases, the single-precision solver will be sufficiently accurate, but certain types of problems may benefit from the use of a double-precision version. Several examples are listed below:

If your geometry has features of very disparate length scales (e.g., a very long, thin pipe), single-precision calculations may not be adequate to represent the node coordinates.

If your geometry involves multiple enclosures connected via small-diameter pipes (e.g., automotive manifolds), mean pressure levels in all but one of the zones can be quite large (since you can set only one global reference pressure location). Double-precision calculations may therefore be necessary to resolve the pressure differences that drive the flow, since these will typically be much smaller than the pressure levels.

For conjugate problems involving high thermal-conductivity ratios and/or high-aspect-ratio meshes, convergence and/or accuracy may be impaired with the single-precision solver, due to inefficient transfer of boundary information.

For multiphase problems where the population balance model is used to resolve particle size distributions, which could have statistical moments whose values span many orders of magnitude.

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