Past Projects

(Before Prof Galindo Torres joined Westlake University)

Airblast hazard in block caving sites

Funded by Newcrest Mining Ltd Australia

Prof Galindo Torres created, for the first time, a customized fluid-solid interaction simulation software to assess the risk of airblast hazards in block caving sites. In an airblast event, air pockets inside the cave can be compressed to reach very high velocities, posing dangers for both personnel and equipment. Our simulation engine is capable of measuring air gusts speed at any desired point in a mining site design, given engineers a virtual tool to estimate the potential risks and environmental impacts of this phenomenon.

The engine was validated with experiments emulating the field observations. The engine was also able to produce valuable data for prediction at the large scale. (Universal laws for air velocities in airblast events during block caving. International Journal of Rock Mechanics and Mining Sciences (2019) link; An airblast hazard simulation engine for block caving sites. International Journal of Rock Mechanics and Mining Sciences (2018) link


Estimation of hydraulic permeability of fracture networks using percolation theory

Funded by Advance Queensland Program

Using theoretical tools from statistical mechanics we were able to find fundamental relations to estimate the hydraulic conductivity from borehole statistics. This study was the first of its kind to close the gap in geological sciences on how to determine a quantity such as permeability from incomplete information coming from geostatistics. To achieve this, statistical distributions of fracture and orientation are taken from field observations and CT scans.

Then flow simulations were carried out on randomly generated fracture networks, following the distributions obtained in the previous step. With an ensemble of random realizations and the use of percolation theory from statistical mechanics, we were able to obtain first principle relations with fundamental parameters to predict the permeability of a fracture network (Scaling solutions for connectivity and conductivity of continuous random networks. Physical Review E (2015) link



Ongoing Projects

Dynamics and Deposition Morphology of Granular Column Collapses

We investigated the collapse behavior of granular columns to better understand the dynamics and rheology of granular materials.In the preliminary study, we studied the collapse of dry granular columns, which results in various flow phenomena and deposition morphologies, which we here link to the inter-granular and particle-boundary frictional coefficients and the initial aspect ratio of the granular columns.With the guidance of experiments, computational studies with the discrete element method (DEM) were performed to investigate the universality of dry granular column collapses.The resulting morphology of a collapsed granular column can be linked to an effective initial aspect ratio, and be described by different collapsing regimes, which are controlled by both the effective aspect ratio and the particle-boundary friction. The finding is important for understanding how granular materials, such as debris flows and landslides, behave and how they deposit when the granular flow halts. (arXiv:2002.02146)

Granular collapse patterns observed in both DEM simulation and experiments (left). DEM simulation (right).






Response mechanism of sediments adsorption of pollutants to hydrodynamic conditions

Sediment in rivers and lakes play an important role in pollutant transport since pollutant can be absorbed by sediment or be released to water from sediment surface. Increasing fluid velocity can enhance or hinder adsorption which roughly depends on whether sediments suspend or not. The influence of hydrodynamic conditions on adsorption process is still not well understand. One challenging issue on modelling this process is: in conventional methods, the total amount of adsorbed pollutants is at the same magnitude (even lower) as numerical errors due to the moving particles. we introduced the Random Walking Method to solve pollutants transport with exact mass conservation. This improvement allows us to study the adsorption process at grain-scale.

Solute transport in the fluid-particle system, particles are under periodic body force. Initial setup (left), Solute concentration porfile at different time step (middle), Dimensionless dispersion coefficient as a funtion of volume fraction (right).


Debris flow modelling

Debris flow is a type of mass movement that is generated under certain mechanical conditions of the soil and external factors as weather, seismic and anthropic activity. The water-sediment interaction processes dominate the behaviour of the flow while is driven by gravity. The sediments size have a broad spectrum from fine grains to big boulders (up to ~11 m in diameter). The dynamic of the interaction of these big boulders with the entire flow is not well known. And, reliable predictions of the dynamics of these phenomena is crucial for risk management.Thus, Smooth Particles Hydrodynamics (SPH) coupled to Discrete Element Method (DEM), are employed in this project to model debris flows.

SPH to represent water and soil components, whereas DEM is used to include large size boulders).





The physics of unsaturated soils

Unsaturated soils are particularly challenging since they combine the physics of air, water and solid phases. Concepts such as matrix suction, capillary action and evaporation are key components of this problem. A single unified modelling approach combining all these physical layers have eluded scientist up to this point. We aim to close this gap by creating a pore-scale modelling engine, first of its kind, capturing all these physical events and from there, together with experiments, deduce the constitutive behaviour and achieve prediction of this complex system. The capabilities created from this project will have far-reaching consequences on soil mechanics, hydrology and environmental sciences.


Artificial Intelligence for geoenvironmental problems

Artificial Intelligence (AI) and Machine Learning (ML) are emerging technologies that have steadily found applications in several areas. We aim to use AI for prediction of geoenvironmental related problems such as groundwater pollution and hydraulic fracturing. One of the main disadvantages of the use of these technologies in these situations is the lack of data from field sites. Field data from realistic situations is sometimes proprietary and most of the times incomplete. Our approach is to fill this gap with the validated and realistic computing modelling techniques that eh AI can use to train itself and predict outcomes.


Rheology of soft soils

Soft soils exist in the limit between suspensions and shear-resistant granular assemblies. Slurries and mining tailings are good examples of these soils which are very relevant in mining, civil and environmental engineering. However, the critical conditions on when the soft soil can be considered a granular material, with internal friction offering the necessary shear resistance for the soil to be stable and when it turns into a suspension, where the solid phase offers no resistance to the flow, are still unknown. Our goal is to understand and formulate realistic rheological relation encompassing these critical points where the transition takes place to create a predictive framework for this phenomenon. We plan to achieve this by carrying out pore-scale simulations, closely validated with matching experiments, of particle-fluid mixtures, and observing the transition under a broad range of conditions.