Project Details
Projekt
Dynamics and data-driven control of liquid metal ducts in strong magnetic fields with wall conductivity
Subject Area
Fluid Mechanics
Term
since 2024
Project identifier
Deutsche Forschungsgemeinschaft (DFG) - Project number 552518799
Nuclear fusion is one of the most promising, but also most demanding technical concepts to address the ever-growing demand of clean electrical energy. To realize such a system, a large number of significant challenges needs to be tackled, one of them being the development of techniques for efficiently transporting heat from the fusion reactor to consecutive process steps (e.g., steam turbines). A very promising approach for this is actively controlling the flow of liquid metals within cooling blankets. In this technology, there are still large knowledge gaps, both in terms of the flow physics and efficient control strategies. The overarching goal of this project is thus the advancement of numerical simulation and surrogate-based control techniques for highly complex liquid metal flows in the presence of strong magnetic fields. Thereby, we will make significant advances in the understanding of the underlying physics as well as regarding the development of control schemes for Tokamak-type nuclear fusion reactors. To achieve the envisioned goals, we will perform basic research in the numerical modeling of MHD flows subject to strong magnetic fields and conducting walls, and in addition with control inputs, e.g., in the form of inflow velocity profiles. This will allow us to conduct detailed direct numerical simulation (DNS) studies. The central aim is to identify quasi-two-dimensional structures as well as to study their stability properties and how they react to external forcings. As a next step, we will use this simulation data and the gained physical knowledge to train highly efficient surrogate models with inputs in order to be able to tackle multi-query problems such as parameter studies or optimal control problems. The key components of these surrogate models will be based on the Koopman operator framework, which allows us to identify linear dynamics in function spaces from time series data. Finally, these surrogate models will be used to identify efficient feedback laws. In this context both model predictive control and model-based reinforcement learning approaches will be investigated. Achieving these goals for a realistic configuration requires interdisciplinary research with experts from fluid dynamics, machine learning, as well as their combination, and the team of applicants from TU Dortmund and TU Ilmenau represents exactly these areas of expertise.
DFG Programme
Research Grants
