Dr Mohaddeseh Mousavi Nezhad
Dr Mousavi Nezhad is an Associate Professor in Computational Mechanics and Data Science in the School of Engineering of University of Warwick. She received her PhD from the University of Exeter in 2010. Her doctoral research project was on stochastic modelling of transport process in heterogeneous porous media. After her PhD, she joined the computational mechanics group in the University of Glasgow as an associate research fellow for a project funded by EDF Energy. She is member of Warwick Centre for Predictive Modelling (WCPM) and leads PMPM research group. Her research focuses on modelling flow and transport in heterogeneous deformable porous materials.
Postdoctoral Research Fellows
Mehrdad Vasheghani Farahani
Dr Mehrdad Vasheghani Farahani is a Postdoctoral Research Fellow at the School of Engineering, University of Warwick. Having background in computational fluid dynamics (CFD), pore-scale modelling, and experimental fluid mechanics, Mehrdad’s research is focused on development of viable techniques for visualisation of fluid flow and transport phenomena in porous media in order to provide fundamental insights regarding the pore-scale phenomena associated with fluid flow in porous geomaterials. Dr Farahani has received his BSc and MSc both in Petroleum Engineering from Sharif University of Technology (Iran) and his PhD in Applied Geoscience from Heriot-Watt University (UK).
Thanh Liem Vo
Dr Thanh Liem Vo currently works at the School of Engineering, Warwick University as a research fellow. His research focuses on hydro-mechanical behaviours of mine refuse (aggregates & tailings) and utilisations of coal mine wastes, problems involving unsaturated soils interacting with retaining wall, shallow foundations, stability/instability of slope/sinkhole/overhang/underground ore-pass. Thanh Liem Vo completed his PhD at the University of New South Wales, Australia and is trained in laboratory and physical models testing in geomechanics.
Dr Guotao (Derek) Ma is a Researcher in Computational Geomechanics and Data Science at the School of Engineering, University of Warwick. His research focuses on multivariate modelling/AI prediction/probabilistic analysis in geoscience, specifically for failure analysis and risk assessment of granular flows and geohazards. Derek develops robust statistical numerical algorithms through the integration of Computational Statistics and Data Science to quantitatively evaluate the uncertainties of granular flows in heterogeneous materials that exhibit significant randomness. He completed his PhD in Engineering from University of Warwick. Derek is also the corresponding member of the International Society of Soil Mechanics and Foundation Engineering (ISSMGE) TC-309 technical committee on “Machine Learning in Geotechnics”. He was the recipient of several research awards, including a prestigious Early Career Fellow of the Institute of Advanced Study, University of Warwick.
In fractured reservoirs, constructive interaction with the natural fracture system is critical to the stimulation treatment. To be most effective, hydraulic fractures should cross and connect natural fracture system, but it is possible that arrest, diversion, or offset could occur to hydraulic fractures in the intersections. Nima’s research aims to identify the controls on fracture propagation in deep rocks in order to carry out sealing capacity of faults in fractured reservoirs. The study contains developing analytical and numerical models for fracture propagation in anisotropic and heterogeneous media. Conducting laboratory experiments and outcrop observations to provide primary input data for the models and to validate the numerical results will be also included.
A better understanding of flow of chemical substances through porous media is essential in the development of many engineering applications. For example, in Carbon Capture and Storage, compressed Liquid CO2 is stored below ground in porous geologic features. Through better understanding, the efficiency of this technology could be enhanced. A key research problem is the requirement of accurate modelling despite uncertainty in both flow behaviour and porous media structural parameters – information on these two key components is often scarcely available. The ultimate goal of Matthew’s research is to produce a model which provides probabilistic information on local tracer concentration evolutions through a porous media sample. To do so a probability density function method will be utilized which accounts for advective transport, pore-scale dispersion and chemical fluid phase reactions. A multi-level stochastic method will be used which allows a combination of expensive and cheap approximate solvers to achieve accurate outputs more efficiently.
Geostatistics and environmental remediation expert. Experience in CFD modelling of transport phenomena in porous media. Interested in new challenges and scientific problems. My PhD project is on experimental and numerical investigation of reactive solute transport in soil to capture single and competitive sorption. Effect of soil composition, heterogeneity, and chemical type on sorption and solute dispersion will be studied.
Stefano is a materials scientist with a background in environmental science. He is pursuing his industrial doctoral studies as a Marie Skłodowska-Curie Actions researcher under the REMEDI ITN. Stefano’s research is focused on the investigation of strategies for the removal of iodinated X-ray contrast media agents (ICMs) from waters. ICMs are commonly used contrast agents for X-ray computed tomography (CT) scans. Due to their molecular design, they evade common wastewater treatment plant processes, enter urban waterways unmetabolized, and act as persistent contaminants. Stefano’s work within the REMEDI ITN will consists in the experimental investigation of different strategies for the removal and capture of ICMs from soil-water systems (including lakes and rivers), and the environmental effects of such strategies, including a focus on Fe-containing geomaterials as remediating agents.
The goal of Alisdair’s research is to produce a model for the distribution of scalars transported by turbulent flow through porous media. Lagrangian stochastic particle techniques are used to discretise the location of the scalar, while an interaction rule models mixing due to turbulence. Reaction with the medium is modelled through a boundary interaction. Distributions are then determined numerically from particle distributions. It is aimed to extend this model by using a network structure to allow more complex geometry to be modelled. One application of this work would be to chemical transport in river beds, where turbulence can occur in porous media geometries.
Elisa’s research project aims at investigating the solute transport and mixing in the hyporheic zone. It represents the interface between the aquifer and the stream where the flow exchange and mixing between the surface water and groundwater occur. This region provides a significant contribution to the attenuation of pollutants and the self-purification of the river water. Diffusion and mixing are challenging to predict within this critical area hence the present research. The solute transport and mixing in a porous medium under turbulent flow conditions are investigated. A numerical model is developed meant to capture the mentioned phenomena. Our setup is representative of mixing processes taking place within the hyporheic zone and considers the transport of dissolved chemicals close to the interface between a free fluid system and a porous medium. The study is grounded on the use of the random walking technique and an appropriate Lagrangian mixing model. The first one, consisting of the discretisation of the solute in particles, is employed to track the motion of solute particles due to diffusion processes. The second one enables the prediction of the temporal evolution of chemical concentration in the hyporheic zone, as well as the mixing of the solute mass within the porous domain. The theoretical and numerical results are benchmarked against experimental data quantifying the vertical variation of the effective dispersion coefficient with depth below the sediment-water interface.
Due to significant variability of rock types, an extensive amount of information about the micro-structure of the rocks are required for their characterisation. My research aims to investigate and characterise different rock types and develop models for predicting hydromechanical properties of the rocks. In this work, image analysis methods are used to classify the rock types. Rock features are extracted from 2D X-ray images which include the spatial distribution of mineralogy, homogeneity and pore size distribution of samples. Machine Learning algorithms are used to train and test classification data and develop permeability predictive models. The permeability models will be implemented in an existing finite element algorithm to assess their performance for simulation of hydromechanical behaviour of the rocks. The results of this research are expected to develop an improved measure for evaluating structural integrity of earth structures.