Numerical study of extreme mechanical force exerted by a turbulent flow on a bluff body by direct and rare-event sampling techniques

TL;DR

This paper uses numerical simulations and rare-event algorithms to analyze extreme forces exerted by turbulent flow on a bluff body, demonstrating the effectiveness of the GKTL algorithm in sampling rare high-drag events.

Contribution

First application of AMS and GKTL rare-event algorithms to fluid-structure interaction, showing GKTL's efficiency in sampling extreme turbulent flow events.

Findings

GKTL algorithm efficiently samples extreme drag fluctuations.

Extreme forces are linked to vortex formation in the wake.

AMS algorithm shows limited runtime savings.

Abstract

This study investigates, by means of numerical simulations, extreme mechanical force exerted by a turbulent flow impinging on a bluff body, and examines the relevance of two distinct rare-event algorithms to efficiently sample these events. The drag experienced by a square obstacle placed in a turbulent channel flow (in two dimensions) is taken as a representative case study. Direct sampling shows that extreme fluctuations are closely related to the presence of a strong vortex blocked in the near wake of the obstacle. This vortex is responsible for a significant pressure drop between the forebody and the base of the obstacle, thus yielding a very high value of the drag. Two algorithms are then considered to speed up the sampling of such flow scenarii, namely the AMS and the GKTL algorithms. The general idea behind these algorithms is to replace a long simulation by a set of much shorter ones, running in parallel, with dynamics that are replicated or pruned, according to some specific rules designed to sample large-amplitude events more frequently. These algorithms have been shown to be relevant for a wide range of problems in statistical physics, computer science, biochemistry. The present study is the first application to a fluid-structure interaction problem. Practical evidence is given that the fast sweeping time of turbulent fluid structures past the obstacle has a strong influence on the efficiency of the rare-event algorithm. While the AMS algorithm does not yield significant run-time savings as compared to direct sampling, the GKTL algorithm appears to be effective to sample very efficiently extreme fluctuations of the time-averaged drag and estimate related statistics such as return times.