Impact Forces of a Supercritical Flow of a Shear Thinning Slurry Against an Obstacle
In mountainous areas after long or intense rains, landslides may evolve into debris- or mud-flows. Their impact against obstacles may produce huge damages, sometimes with loss of lives. Prediction of the impact forces is required for a proper design of the flow barriers protecting risk prone areas. To this aim, both the effective characterization of the mud rheology and a suitable mathematical model of the flow propagation are needed. The present paper proposes a modeling framework in which the mudflow is idealized as the flow of a power-law fluid over an incline with a rigid impervious wall at the downhill end. The flow model employs the von Kármán depth-integration of the one-dimensional mass and momentum conservation equations, in the long-wave approximation. The governing equations have been solved through a space/time second-order accurate numerical method. This modeling framework is applied to a test-case, based on the soil collected from Cervinara site (Avellino, South Italy), affected by a catastrophic landslide in 1999. The investigated soil is both the raw one and a washed one, the latter introduced to mimic the effect of an intense rain in terms of removal of the dissolved soil organic carbon. The rheology of both the shear-thinning mixtures has been deeply characterized in the form of a power-law function, and the dynamics of a dam-break wave ad its impact on an obstacle, has been numerically analyzed. It is shown that the removal of the soil organic carbon affects the propagation of the mudflow and at a minor extent the maximum forces and torques acting on the downstream wall. Remarkably, in the investigated conditions, the mudflow action consists of a strong impact occurring few seconds after the landslide triggering, and a subsequent cyclic loading of about three minutes.
KeywordsFinite volume methods Impact forces Power-law fluid Soil organic carbon Slurry rheology
The research was partly supported by Regione Campania [grant LR5/02-2008 no. B36D14000770002].
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