1.進(jìn)行質(zhì)量縮放的僅僅針對模型的均勻性不好，極少的部分單元尺寸很小，這時(shí)候進(jìn)行質(zhì)量縮放，增大極小單元的質(zhì)量，間接增大整個(gè)模型的最小時(shí)間步長，但是需要注意的是，如果這些極小單元恰恰是你最關(guān)心的部位，這個(gè)時(shí)候質(zhì)量縮放沒有意義了！ 建議不要進(jìn)行質(zhì)量縮放！

2.如果網(wǎng)格差異比較大，有一種不常用的方法叫做子循環(huán)的方法，但是用的很少，只需要在模型中加入 *control -subcycle就行了 ，但是據(jù)說這種方法會(huì)帶來穩(wěn)定性方面的問題，引述如下：

Daniel  showed that this algorithm is in fact not stable in a classical sense, in the absence of any energy dissipation. Narrow timestep ranges are unstable, due to the nonlinearity of switching between the whole model updated once per major cycle, and the small timestep zone updated in minor cycles. As the model size increases, these unstable timestep ranges become extremely narrow, such that unstable states are very unlikely to be encountered. This situation has been labelled ‘‘statistical stability’’. The Belytschko et al. algorithm also has a second problem of possible inaccuracy due to a lack of momentum conservation at a timestep interface. This can occur due to the large timestep update only sampling the state at neighbouring small timestep nodes once per major cycle. 

我沒有進(jìn)行這方面嘗試，對此也沒有太多的發(fā)言權(quán)

3.還有一種方法，叫做子模型方法，這種方法也很少見有人使用，參考

http://www.rootfea.com/ShareView.Asp?ID=461

由于工作中自循環(huán)和子模型都沒有涉及，不能提供具體的幫助，你可以問下其它的鄰友是否有相關(guān)的經(jīng)驗(yàn)