温度梯度变质过程中雪地比表面积演变的严谨方法

Anna Braun, Kévin Fourteau, Henning Löwe
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引用次数: 0

摘要

摘要。尽管比表面积(SSA)是雪地最基本的微观结构参数之一,但迄今为止,温度梯度变质(TGM)过程中的比表面积(SSA)动态仅在经验模型中得到了研究。为了突破这一局限,我们提出了一种严格的比表面积动态建模方法,利用表面积时间演化的精确方程,通过 X 射线计算机断层扫描图像上的水汽场与温度场耦合的孔隙尺度有限元模拟,对比比表面积进行建模。所提出的方法源自物理学第一原理,因此不依赖任何经验参数。由于计算出的 SSA 演变对实验数据的波动高度敏感,我们在随机误差模型中量化了这些波动的影响。在我们的模拟中,唯一约束较差的物理参数是凝结系数α。为了解决这个问题,我们模拟了各种 α 值的 SSA 演变,并通过最小化模拟和实验之间的差异来估计最佳值。这种方法表明,α 处于 10-3<α<10-1 的中间范围,不同实验之间略有差异。此外,我们的结果还表明,在一次 TGM 实验中,α 值发生了转变,这可以用底层表面形态的转变来解释。总体而言,我们能够再现 SSA 演化过程中非常微妙的变化,两个 TGM 时间序列的相关性分别为 R2=0.95 和 0.99。最后,我们的工作强调了包含动力学效应和使用现实微结构来理解 TGM 过程中 SSA 演变的必要性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A rigorous approach to the specific surface area evolution in snow during temperature gradient metamorphism
Abstract. Despite being one of the most fundamental microstructural parameters of snow, the specific surface area (SSA) dynamics during temperature gradient metamorphism (TGM) have so far been addressed only within empirical modeling. To surpass this limitation, we propose a rigorous modeling of SSA dynamics using an exact equation for the temporal evolution of the surface area, fed by pore-scale finite-element simulations of the water vapor field coupled with the temperature field on X-ray computed tomography images. The proposed methodology is derived from the first principles of physics and thus does not rely on any empirical parameter. Since the calculated evolution of the SSA is highly sensitive to fluctuations in the experimental data, we quantify the impact of these fluctuations within a stochastic error model. In our simulations, the only poorly constrained physical parameter is the condensation coefficient α. We address this problem by simulating the SSA evolution for a wide range of α values and estimate optimal values by minimizing the differences between simulations and experiments. This methodology suggests that α lies in the intermediate range 10-3<α<10-1 and slightly varies between experiments. Also, our results suggest a transition of the value of α in one TGM experiment, which can be explained by a transition in the underlying surface morphology. Overall, we are able to reproduce very subtle variations in the SSA evolution with correlations of R2=0.95 and 0.99, respectively, for the two TGM time series considered. Finally, our work highlights the necessity of including kinetic effects and of using realistic microstructures to comprehend the evolution of SSA during TGM.
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