Author(s): Soumil Gurjar; Siddhartha Mishra; Perry Bartelt

Linked Author(s):

Keywords: Avalanche modelling; Powder suspension cloud; Mixed flowing-powder avalanche; RAMMS; Navier-Stokes equations

Abstract: The objective is to develop an avalanche model that can predict the behaviour of the powder suspension cloud within a mixed-flowing/powder avalanche in a natural terrain, and thereby evaluate variations for flow velocities, impact pressure and run-out distances etc., which are vital to constructing hazard scenarios. Mixed flowing/powder avalanches are characterized by a fast moving core of heavy ice/snow particles and a powder suspension cloud consisting of fine ice dust. These avalanches are especially dangerous because they reach high velocities and long run-out distances, especially in the cold, steep terrains. The area inundated by the avalanche is difficult to predict because the powder cloud can decouple from the avalanche core and move independently, reaching distances well beyond the reach of the dense core. Numerical avalanche dynamics models have become an essential part of snow engineering. An accurate prediction of avalanche run-out distances, flow velocities and impact pressures in natural 3D terrain is the driving motivation behind the development of improved snow avalanche dynamics models. Of particular importance for a powder avalanche, is the spreading velocity of the cloud when it becomes detached from the core and inundates regions beyond the reach of the dense, flowing avalanche core. Accurate modelling of the pressures associated with the spreading of the cloud is often decisive in many practical applications, for both hazard mapping and back calculations of specific events. The focus of this research is on developing numerical avalanche dynamics models in order to understand the flow of a mixed flowing/powder avalanche under complex terrain conditions. In particular, we concentrate on the simulation of the powder suspension cloud within the avalanche.

DOI: https://doi.org/10.3850/978-981-11-2731-1_339-cd

Year: 2018