To access this element change to forms mode OFF
Grant Award View - GA138702
A Machine Learning driven flow modelling of fragmented rocks in cave...
GA ID:
GA138702
Agency:
Australian Research Council
Approval Date:
24-Nov-2020
Publish Date:
25-Nov-2020
Category:
Science, Technology, Engineering and Mathematics (STEM) Research
Grant Term:
24-Nov-2020 to 30-Jun-2024
Value (AUD):
$516,000.00
(GST inclusive where applicable)
Variations:
- GA138702-V2 - Variation to Grant (19-Oct-2021 )
- GA138702-V1 - Variation to Grant (2-Aug-2021 )
One-off/Ad hoc:
No
Aggregate Grant Award:
No
PBS Program Name:
ARC 19/20 Linkage
Grant Program:
Linkage Projects
Grant Activity:
A Machine Learning driven flow modelling of fragmented rocks in cave mining
Purpose:
The project aims to develop an integrated method that uses micro scale and macro scale information to predict block scale behaviour so that a better cave mining design can be established. The role of various mineral composition on the energy storage and fracture properties of rocks will be investigated to examine rock fragmentation for block cave mining. Later Machine Learning based models will be developed to establish various predictive models for Block Scale rock mass behaviour and caveability of ore deposit. Finally, we will develop a new constitutive model based on a dual damage concept that will capture the rock fragmentation and simulate the cave propagation in a large scale mine layout using Smoothed-particle hydrodynamics.
GO ID:
GO Title:
Linkage Projects for funding applied for in 2020
Internal Reference ID:
LP20 Round 1
Selection Process:
Targeted or Restricted Competitive
Confidentiality - Contract:
No
Confidentiality - Outputs:
No
Grant Recipient Details
Recipient Name:
The University of Adelaide
Recipient ABN:
61 249 878 937
Grant Recipient Location
Suburb:
ADELAIDE
Town/City:
ADELAIDE
Postcode:
5000
State/Territory:
SA
Country:
AUSTRALIA
Grant Delivery Location
State/Territory:
SA
Postcode:
5000
Country:
AUSTRALIA