CFD-Driven Surrogate-Based Optimization

Fabian Hübenthal (), Xiao Shao (), Matthias Meinke () and Wolfgang Schröder ()
Additional contact information
Fabian Hübenthal: Chair of Fluid Mechanics and Institute of Aerodynamics
Xiao Shao: Chair of Fluid Mechanics and Institute of Aerodynamics
Matthias Meinke: Chair of Fluid Mechanics and Institute of Aerodynamics
Wolfgang Schröder: Chair of Fluid Mechanics and Institute of Aerodynamics

A chapter in High Performance Computing in Science and Engineering '23, 2026, pp 285-300 from Springer

Abstract: Abstract The potential of spanwise traveling transversal sinusoidal surface waves as an active drag reduction strategy for turbulent boundary layer flows is investigated using wall-resolved large-eddy simulations (LES). LES predictions are performed for Mach numbers $$\textrm{M} = 0.2$$ M = 0.2 and 0.7 for a fixed momentum-thickness-based Reynolds number of $$\textrm{Re}_{\theta }=1000$$ Re θ = 1000 to cover various flow conditions and to extend a previous data set beyond Mach numbers of $$\textrm{M} = 0.1$$ M = 0.1 . These initial LES data points are planned according to a near-random space-filling sampling method, i.e., Latin hypercube sampling (LHS), within the space of promising actuation parameters. The main goal of surrogate-based optimization is to choose the actuation parameters such that the objective of drag reduction for given flow conditions is maximized by exploiting and exploring the surrogate model, which is fitted to the data and enriched with prior knowledge. Two surrogate-based optimization (SO) strategies using support vector regression (SVR) and Gaussian process regression (GPR) are investigated. To compare both strategies, the simulation data for $$\textrm{M} = 0.1$$ M = 0.1 are used to model the dependence of the objective drag reduction on the actuation parameters. A previous purely exploitative approach of SVR-based ridgeline optimization (RO) for $$\textrm{M} = 0.1$$ M = 0.1 is extended to GPR-based Bayesian optimization (BO) to further automate and efficiently guide the CFD-based optimization of the actuation parameters.

Date: 2026
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-031-91312-9_20

Ordering information: This item can be ordered from
http://www.springer.com/9783031913129

DOI: 10.1007/978-3-031-91312-9_20

Access Statistics for this chapter

More chapters in Springer Books from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().