Maarten is Professor of Urban Fluid Mechanics in the Department of Civil and Environmental Engineering, where he leads an active research group and serves as the department's Director of Research. He joined Imperial in 2007 after a PhD on turbulent thermal convection at TU Delft and has held the chair since 2022; he is a Fellow of the Royal Meteorological Society. He is co-founder and co-director of the Urban Fluid Mechanics Special Interest Group, which has organised twelve workshops to date drawing participants from academia, industry and government. He teaches at both under- and postgraduate level, and received Imperial's President's Award for Excellence in Teaching in 2014.

Research

His research focuses on turbulent transport in environmental fluids — how wall-bounded turbulence and buoyancy together move heat, pollutants and particles through cities, the atmosphere and the coastal ocean. The lab moves between high-fidelity simulation (centred on the open-source urban LES code uDALES we develop), reduced-order theory, and data-driven methods, so that insight from expensive DNS or LES eventually shows up in the parameterisations, surrogates and fast tools that operational weather models, urban planners and architects actually use.

The group works on the following research areas:

• Urban fluid mechanics — air quality and the urban microclimate, with a focus on sustainability. We use high-fidelity Large-Eddy Simulation and Urban Energy Balance models to quantify how sustainable infrastructure (green roofs, green walls, street trees) affects air quality and the urban heat island.
• Turbulence and mixing — turbulent entrainment in its many guises (jets and plumes, volcanic ash, gravity currents, atmospheric and oceanic boundary layers). Direct Numerical Simulation and theory give us unprecedented insight into a phenomenon that is important yet notoriously difficult to measure.
• Multiphase flows — particle dynamics in turbulent flows, with applications to microplastics in the ocean and cloud droplets in the atmosphere.
• Machine learning for fluid mechanics — surrogate models that compress expensive simulations into something deployable in operational forecasts and digital twins, Gaussian processes that turn sparse sensor networks into calibrated street-level pollutant fields, and data assimilation that fuses observations with hydrodynamic models.

Join us

Maarten leads a diverse, intentionally interdisciplinary group of PhD students and postdocs with backgrounds in Aeronautical, Civil and Mechanical Engineering, Mathematics, and Physics. There are regular opportunities for PhD and postdoctoral positions — see the Join us page or email me directly.