Bridging the Gap – Mathematical Optimization Methods and Efficient Algorithms for Process Engineering Problems
A vast part of process engineering research and industrial application is dedicated to the optimal design of processes that involve a fluid flow, in order to maximize yield, minimize the required energy or in general to achieve certain objectives best. The optimization is usually carried out manually, which causes costly parameter studies with only a few design parameters and suboptimal results. An automatic numerical execution enables to vary many design parameters at once, while getting better results and saving costly and limited resources. While computational flow simulation (CFD) is well established in engineering design processes, solving fluid flow control and optimization problems numerically has not at all become a standard tool yet.
Against this background, a long-term goal is to build a bridge between the two disciplines mathematical optimization and process engineering. Therefore, opportunities and limitations shall be identified exemplarily for micro mixer (cf. Maier et al., 2018): the needs on the application side as well as the usability of the offered analytical and numerical approaches are to be identified, such that gaps can be stated explicitly and partially closed.
Contact: Julius Jeßberger
Funding: This research is funded by the KIT Center MathSee
Jeßberger, J.; Marquardt, J.E.; Heim, L.; Mangold, J.; Bukreev, F.; Krause, M.J., Optimization of a Micromixer with Automatic Differentiation. Fluidsons, 2022, 7, 144.
Jeßberger, J.; Marquardt, J.E.; Heim, L.; Krause, M.J. Numerical Optimization of a Micromixer. Poster, ProcessNet 2022