Project Details
Projekt
Improving simulations of large-scale dense particle laden flows with machine learning: a genetic programming approach
Subject Area
Fluid Mechanics
Image and Language Processing, Computer Graphics and Visualisation, Human Computer Interaction, Ubiquitous and Wearable Computing
Mechanical Process Engineering
Image and Language Processing, Computer Graphics and Visualisation, Human Computer Interaction, Ubiquitous and Wearable Computing
Mechanical Process Engineering
Term
since 2021
Project identifier
Deutsche Forschungsgemeinschaft (DFG) - Project number 466092867
Particle-laden flows are encountered in many natural and industrial processes, such as, for instance, the flow of red and white blood cells in plasma, or the fluidization of biomass particles in furnaces. Over the last 40 years, scientists have used Euler-Lagrange (EL) simulations as a way to predict the behavior of such flows. However, EL simulations rely on models to describe the interaction between the fluid and the individually tracked particles. These models require the so-called "undisturbed" fluid velocity at the location of the particle, which is what the velocity of the fluid would have been if the particle had not been there. Current models for this are very rudimentary and precisely calculating the undisturbed fluid velocity is extremely expensive, as it would involve running many additional highly resolved simulations of the same case where one particle is left out.This is a proposal to deliver a novel model for the undisturbed fluid velocity at each particle location, given the properties of the flow around the particle and of the surrounding particles, using a supervised learning machine learning approach: genetic programming (GP). GP is highly suitable, as its result will not be a "black-box" model, but a verifiable expression for the undisturbed velocity. This expression will be validated by analytical solutions and highly resolved simulations, and will enable accurate, large-scale simulations of dense particle-laden flows, while only requiring a fraction of the cost of fully resolved simulations.
DFG Programme
Priority Programmes
Subproject of
SPP 2331:
Machine Learning in Chemical Engineering. Knowledge Meets Data: Interpretability, Extrapolation, Reliability, Trust
Co-Investigator
Professor Dr. Fabien Evrard
