恒星形成区磁流体动力学模拟的化学后处理：鲁棒性和缺陷
Chemical post-processing of magneto-hydrodynamical simulations of star-forming regions: robustness and pitfalls
摘要：
在数值模拟中对复杂化学进行建模的常见方法是通过对现有磁流体动力学模拟进行后处理，依赖于从原始模拟中计算粒子子集的动态历史上的化学演变。在这里，我们验证这样的技术，评估其恢复化学物种的丰度的能力，使用化学包KROME。我们还评估，第一次，主要的自由输入参数的重要性，通过一个自洽的国家的最先进的模拟，其中化学直接耦合到流体力学的直接比较。我们已经发现，后处理是高度可靠的，精度在百分比水平，即使当最宽松的输入参数。特别是，我们的研究结果表明，所使用的粒子的数量并没有显着影响的平均属性，虽然它抑制了可能重要的空间特征的外观。另一方面，积分时间步长的选择起着至关重要的作用。较长的积分时间步长可能会产生较大的误差，因为后处理溶液将被迫达到化学平衡，这一条件并不总是适用。当进行化学性质的基于插值的重建时，误差进一步增加到$\sim2$的因子。总之，我们的结果表明，这种技术是非常有用的，当探索不同的化学参数和/或网络的相对定量效果，而不需要重新运行模拟多次，但在选择粒子子样本和积分时间步长时，应注意一些。
英文摘要：
A common approach to model complex chemistry in numerical simulations is via post-processing of existing magneto-hydrodynamic simulations, relying on computing the evolution of chemistry over the dynamic history of a subset of particles from within the raw simulation. Here, we validate such a technique, assessing its ability to recover the abundances of chemical species, using the chemistry package KROME. We also assess, for the first time, the importance of the main free input parameters, by means of a direct comparison with a self-consistent state-of-the-art simulation in which chemistry was directly coupled to hydrodynamics. We have found that the post-processing is highly reliable, with an accuracy at the percent level, even when the most relaxed input parameters are employed. In particular, our results show that the number of particles used does not affect significantly the average properties, although it suppresses the appearance of possibly important spatial features. On the other hand, the choice of the integration time-step plays a crucial role. Longer integration time-steps can produce large errors, as the post-processing solution will be forced towards chemical equilibrium, a condition that does not always necessarily apply. When the interpolation-based reconstruction of chemical properties is performed, the errors further increase up to a factor of $\sim2$. Concluding, our results suggest that this technique is extremely useful when exploring the relative quantitative effect of different chemical parameters and/or networks, without the need of re-running simulations multiple times, but some care should be taken in the choice of particles sub-sample and integration time-step.
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