Dr. Mathias J. Krause, Lattice Boltzmann Research Group, Karlsruher Institut für Technologie (KIT)
Title: Fluid Flow Optimization with Lattice Boltzmann Methods with Applications
Abstract:
For many medical as well as technical applications the accurate knowledge of fluid flow dynamics, e.g. flow rates or wall shear stresses, is fundamental to understand and describe underlying processes. A coupling of simulation and measurement (CFD-MRI) promises a significant progress in terms of the accuracy of the obtained flow data, even in situations of low image contrast. Compared to today’s state-of-the-art, the new approach may achieve patient-friendly diagnostics by reducing the required contrast agent. Furthermore, the increase of accuracy allows treating new scenarios and as a result, unanswered medical questions, e.g. perfusion disorders, can be addressed.
In the talk, an overall strategy for numerical simulation and optimisation of fluid flow is intro-duced. The integrative approach takes advantage of numerical simulation, high performance computing and newly developed mathematical optimization techniques, all based on a mesoscopic model description and on Lattice Boltzmann Methods (LBM) as discretisation strategies. The resulting algorithms are implemented in a highly generic way in the frame-work of the open source library OpenLB (https://www.openlb.net). The approaches and re-alisations are illustrated by means of various fluid flow simulation and optimisation exam-ples. Thereby, the main focus is placed on the CFD-MRI approach. Details are given for the 3D fluid flow optimisation problem formulation, the derivation of a first order optimality sys-tem and the solving process with gradient-based methods using Adjoint Lattice Boltzmann Methods. Validation and first application results (cf. Figure, an illustration of the CFD-MRI approach applied to a human aorta) are presented and discussed.
How to join:
The talk is held online via Zoom. You can join with the link https://uni-kl-de.zoom.us/j/94636397127?pwd=Y1g4dGVFQitzUHVRQUFpcFB4WVFKQT09.