Swiss National Science Foundation Professor
| UNIL – University of Lausanne |
ISTE – Institute of Earth Sciences
Email: firstname.lastname@example.org Curriculum Vitae [pdf]
Ivan Lunati has been Swiss National Science Foundation Professor at the Institute of Earth Sciences of UNIL.
He studied physics at the University of Milan, Italy. During his diploma thesis, he visited the Ecole Nationale Supérieure d’Arts et Métiers in Bordeaux (France) and became interested in the dynamics of fluids through porous media. In April 1999 he joined the Institute of Hydrodynamics of ETH Zurich, where he earned his PhD in April 2003. In February 2004 he moved to the Institute of Fluid Dynamics as a postdoctoral researcher and in July 2005 was promoted to senior scientist. After visiting the Chevron Simulation Research Team in San Ramon, California (Fall 2005), he came back to Zurich and then moved to the EPF Lausanne where he worked as senior scientist in the Environmental Engineering Institute. In 2009 he was awarded a SNSF Professorship and joined the University of Lausanne.
His research activity has covered several topics including determination of model parameters by solution of inverse problems; stochastic description of porous media; interpretation of field measurements, especially solute- and gas-tracer tests; numerical modeling of reservoir and pore-scale processes; experimental techniques such as neutron tomography for visualization of moisture contents in geological materials. He has specialized in multiphase flow in porous media addressing issues related to multiscale modeling of reservoirs and repositories, modeling and conceptualization of pore-scale processes, as well as fundamental issue in the description of capillary forces and coupled energy and mass transport.
- Multiphase flow and transport in porous media
- Wetting and capillary phenomena
- Coupled energy and mass transfer
- Nonlinear phenomena
- Unstable fluid flow
- Multiscale modeling of physically complex systems
- Numerical methods for simulation of nonlinear flow
- Machine Learning
- Stochastic methods