Hostname: page-component-6766d58669-rxg44 Total loading time: 0 Render date: 2026-05-17T07:36:41.346Z Has data issue: false hasContentIssue false

A mathematical framework for modelling rock–ice avalanches

Published online by Cambridge University Press:  25 May 2021

Stefania Sansone*
Affiliation:
Department of Civil, Environmental and Mechanical Engineering, University of Trento, Trento, 38123, Italy
D. Zugliani
Affiliation:
Department of Civil, Environmental and Mechanical Engineering, University of Trento, Trento, 38123, Italy
G. Rosatti
Affiliation:
Department of Civil, Environmental and Mechanical Engineering, University of Trento, Trento, 38123, Italy
*
Email address for correspondence: stefania.sansone@unitn.it

Abstract

Rock–ice avalanches are liquid–granular flows that consist of a mixture of rock, ice and a liquid. The dynamics that distinguishes these types of flows from other geophysical flows is the ice melting. This process is responsible for mass and momentum transfers between the solid and liquid components of the mixture and for the effects of lubrication and fluidization that reduce the mixture strength. In this work, we analyse the problem from a mathematical point of view. Starting from the partial differential equations of a complete three-phase approach, we identify two basic assumptions that can be used to build a framework of classes of simplified models. The implications of these assumptions on the physical description of the flow are carefully analysed for each class, and particular attention is paid to the simplification of the melting process expressed in terms of mass and momentum transfers. Moreover, the derived framework allows us to classify the existing literature models and to identify a new class of models that can be considered a reasonable trade-off between simplicity and completeness. Finally, the mathematical nature of each class is investigated by performing an in-depth analysis of the eigenvalues. Results show that the most simplified models are strictly hyperbolic, while the most complete approaches are affected by a loss of hyperbolicity in given ranges of the model parameters. Further research is necessary to understand the reasons and the numerical implications of this feature.

Information

Type
JFM Papers
Creative Commons
Creative Common License - CCCreative Common License - BY
This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted re-use, distribution, and reproduction in any medium, provided the original work is properly cited.
Copyright
© The Author(s), 2021. Published by Cambridge University Press
Figure 0

Figure 1. Classes of simplified models obtained from the RIW approach by imposing an isokinetic condition moving in the vertical direction (dashed arrow) and the incompressibility assumption moving in the horizontal direction (continuous arrow). The filling colours represent the number of phases considered (blue, three; orange, two; green, one).

Figure 1

Table 1. Classes of models and corresponding unknowns. The subscript $s$ denotes the overall solid phase (rock plus ice). No subscript indicates the overall bulk mixture. Details on the unknowns are given in the related model paragraphs.

Figure 2

Table 2. Effects of the simplifications used in the framework of simplified RIW models. The symbols ✓and X denote, respectively, whether the quantities appear in the class of models or not. Under ‘concentration variability’, the term ‘ratio’ stands for the concentration ratio and the term ‘single’ indicates the single concentration of the phases in each class of models.

Figure 3

Figure 2. Classes of simplified RIW models with their corresponding acronyms and classification of the models existing in the literature within the RIW framework. The differences in colours give, as in figure 1, the differences in the number of phases considered in each class of simplified RIW models.

Figure 4

Figure 3. Reference system and variables used to describe the flow in the PI-RIW model.

Figure 5

Figure 4. Trend of the dimensionless eigenvalues obtained considering $Fr_{s}/Fr_{w}=0.7$, $C_{w}=0.5$, $C_{r}^{e}=0.37$, $K=1$ and $\rho _{i} /\rho _{r}=0.353$.

Figure 6

Figure 5. Effect of the Froude ratio on the dimensionless eigenvalue trends. Results are obtained considering $C_{w}=0.5$, $C_{r}^{e}=0.37$, $K=1$ and $\rho _{i}/\rho _{r}=0.353$.

Figure 7

Figure 6. Lower ($Fr_{w}^{low}$) and upper ($Fr_{w}^{up}$) limits of the non-hyperbolic region as functions of the Froude ratio $Fr_{s}/Fr_{w}$. Results are obtained, as in figure 4, considering $C_{w}=0.5$, $C_{r}^{e}=0.37$, $K=1$ and $\rho _{i}/\rho _{r}=0.353$.

Figure 8

Figure 7. Effect of the water concentration $C_{w}$ on the dimensionless eigenvalue trends. Results are obtained considering $Fr_{s}/Fr_{w} =0.7$, $C_{r}^{e}=0.34$, $K=1$ and $\rho _{i}/\rho _{r}=0.353$.

Figure 9

Table 3. Effects of the parameters $C_{w}$, $C_{r}^{e}$ and $K$ on the lower limit $Fr_{w}^{low}$, upper limit $Fr_{w}^{up}$ and range of the non-hyperbolic region. The values are referred to the different plots reported in the previous paragraphs and can be derived imposing both $Fr_{s} /Fr_{w}=0.7$ and $Fr_{s}/Fr_{w}=1.3$.

Figure 10

Figure 8. Effect of the rock equivalent concentration $C_{r}^{e}$ on the dimensionless eigenvalue trends. Results are obtained considering $Fr_{s}/Fr_{w}=0.7$, $C_{w}=0.4$, $K=1$ and ${\rho }_{i}/\rho _{r}=0.353$.

Figure 11

Figure 9. Effect of the earth-pressure coefficient $K$ on the dimensionless eigenvalue trends. Results are obtained considering $Fr_{s}/Fr_{w}=0.7$, $C_{w}=0.5$, $C^{e} _{r}=0.37$ and $\rho _{i}/\rho _{r}=0.353$.

Figure 12

Figure 10. Comparison between the dimensionless eigenvalues of the FI-RIW and of the PI-RIW models. Parameters used in these plots are the same used in figure 5. The FI-RIW eigenvalues are marked by continuous lines with symbols, while those of the PI-RIW model are denoted by dashed lines without any type of distinction (see figure 5 for details in the PI-RIW eigenvalues).

Figure 13

Figure 11. Comparison between the dimensionless eigenvalues of the TP-RIW and of the PI-RIW models. Parameters used in these plots are the same used in figure 5. The TP-RIW eigenvalues are marked by continuous lines with symbols, while those of the PI-RIW model are denoted by dashed lines without any type of distinction (see figure 5 for details in the PI-RIW eigenvalues).

Figure 14

Figure 12. Comparison between the dimensionless eigenvalues of the ITP-RIW and of the PI-RIW models. Parameters used in these plots are the same used in figure 5. The ITP-RIW eigenvalues are marked by continuous lines with symbols, while those of the PI-RIW model are denoted by dashed lines without any type of distinction (see figure 5 for details in the PI-RIW eigenvalues).

Figure 15

Figure 13. Comparison between the dimensionless eigenvalues of the MP-RIW and of the PI-RIW models. Parameters used in these plots are the same used in figure 5. The MP-RIW eigenvalues are marked by continuous lines with symbols, while those of the PI-RIW model are denoted by dashed lines without any type of distinction (see figure 5 for details in the PI-RIW eigenvalues).

Figure 16

Figure 14. Comparison between the dimensionless eigenvalues of the PI-RIW model and the approximated eigenvalues used by Pudasaini & Krautblatter (2014) to solve their TPPK model. Parameters used in these plots are the same used in figure 5. The TPPK eigenvalues are marked by continuous lines with symbols, while those of the PI-RIW model are denoted by dashed lines without any type of distinction (see figure 5 for details in the PI-RIW eigenvalues).