Current research areas

At the Soft Matter Research Center (SOFMAT), we are dedicated to advancing scientific understanding and developing innovative solutions in the field of soft materials. The list below is an overview of the research areas currently in development.

Experimental flow measurement techniques using image processing

Experimental flow measurement techniques using image processing

Development and application of advanced imaging methods to visualize and analyze complex flow phenomena, enabling precise investigation of soft material behavior and multiphase systems.

Rheology of drilling fluids under high pressure and high temperature

Rheology of drilling fluids under high pressure and high temperature

Investigation of the rheological properties of drilling fluids under extreme conditions to support innovation in oil and gas exploration and deep drilling technologies.

Rheology of time-dependent materials

Rheology of time-dependent materials

Experimental and theoretical research into the rheological behavior of time-dependent materials, such as thixotropic and viscoelastic fluids, relevant for a wide range of applications.

Modeling and simulation of non-Newtonian fluid flow

Modeling and simulation of non-Newtonian fluid flow

Theoretical and computational modeling of non-Newtonian fluids to understand their complex behavior and improve predictions for industrial and scientific applications.

Particulate flow experiments and simulations

Particulate flow experiments and simulations

Study of the flow behavior of granular and particulate materials under various conditions, aiming to optimize processes in sectors such as mining, energy, and food industries.

Lattice Boltzmann method for turbulent and multiphase flow

Lattice Boltzmann method for turbulent and multiphase flow

Application of the lattice Boltzmann method (LBM) to simulate turbulent and multiphase flows, providing insights into complex fluid dynamics problems with high computational efficiency.

Physics-informed neural networks

Physics-informed neural networks

Integration of machine learning with physical laws (PINNs and related methods) to enhance simulation accuracy, accelerate computational workflows, and uncover new insights in soft matter systems.