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A new GPU-accelerated coupled discrete element and depth-averaged model for simulation of flow-like landslides

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journal contribution
posted on 2022-05-24, 14:55 authored by Xiaoli Su, Qiuhua LiangQiuhua Liang, Xilin Xia
Flow-like landslides are a common type of natural hazards that may impose a great risk to people and their properties. Different models have been reported for simulating flow-like landslides, all of which possess limitations due to the underlying assumptions and simplifications. Harnessing the advantages of two types of prevailing modelling approaches, a new coupled model is developed which adopts a discrete element method (DEM) model to simulate the complex collapsing process in the source area and a depth-averaged model (DAM) to predict the predominantly convective movement in the runout and deposition zones. The coupled model is finally implemented on the NVIDIA CUDA programming platform to achieve GPU high-performance computing. Two laboratory tests are considered to validate the model and a field-scale landslide event is simulated to verify its applicability in real-world conditions. Satisfactory results confirm that the coupled model is able to reproduce the dynamic process of real-world flow-like landslides.

Funding

Web-Based Natural Dam-Burst Flood Hazard Assessment and ForeCasting SysTem (WeACT)

Natural Environment Research Council

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History

School

  • Architecture, Building and Civil Engineering

Published in

Environmental Modelling & Software

Volume

153

Publisher

Elsevier BV

Version

  • VoR (Version of Record)

Rights holder

© The Authors

Publisher statement

This is an Open Access Article. It is published by Elsevier under the Creative Commons Attribution 4.0 International Licence (CC BY). Full details of this licence are available at: https://creativecommons.org/licenses/by/4.0/

Acceptance date

2022-04-28

Publication date

2022-05-01

Copyright date

2022

ISSN

1364-8152

Language

  • en

Depositor

Prof Qiuhua Liang. Deposit date: 24 May 2022

Article number

105412

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