STOCKHOLM - This research project aims to realize faster-than-real-time (simulation time less than physical flow time) and high-resolution simulation of fluid flow in engineering applications, with indoor climate as a pilot. The expected outcome of this project is a static convolutional neural network for super-resolution to achieve fast prediction of steady-state indoor airflow, a hybrid region-based convolutional neural network for super-resolution to achieve faster-than-real-time prediction of transient indoor airflow, and standards for low-resolution input data by numerical simulation and experimental data. Furthermore, the indoor flow simulations of this project would open a broad spectrum of engineering accurate computational fluid dynamics applications, complementary to today’s standard application of Reynolds-averaged Navier-Stokes equations turbulence models.

More details at https://www.kth.se/en/om/work-at-kth/lediga-jobb/what:job/jobID:651190/type:job/where:4/apply:1.