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

Lestang, Thibault; Bouchet, Freddy; Lévêque, Emmanuel

doi:10.1017/jfm.2020.293

Cited by 20 publications

(34 citation statements)

References 61 publications

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“…In recent years there has been several attempts to apply these methods for geophysical applications. They have been applied to Lorenz models [275], partial differential equations [226], turbulence problems [77,113,160,164,165], geophysical fluid dynamics [28], heatwaves in general circulation models [217][218][219] and data-based stochastic weather generators [281], and tropical cyclones in regional climate models [212,272]. Here we give an overview of methods and their applications that have been used to study problems directly related to the dynamics of planetary atmospheres and making use of concepts from LDT.…”

Section: Rare Event Sampling Algorithms Based On Large Deviation Theorymentioning

confidence: 99%

“…The AMS algorithm has been used among other applications to study transitions to turbulence [225], the forces acting on a solid object in a turbulent flow [164,165], Reproduced with permission from [28] and transitions between metastable states in geophysical fluid dynamics [28]. For example, [28] studied the transitions between metastable states with two and three jets in a stochastic barotropic beta-plane quasi-geostrophic model of the atmosphere of Jupiter (left panel of Fig.…”

Section: Rare Event Sampling Algorithms Based On Large Deviation Theorymentioning

confidence: 99%

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Applications of large deviation theory in geophysical fluid dynamics and climate science

et al. 2021

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The climate is a complex, chaotic system with many degrees of freedom. Attaining a deeper level of understanding of climate dynamics is an urgent scientific challenge, given the evolving climate crisis. In statistical physics, many-particle systems are studied using Large Deviation Theory (LDT). A great potential exists for applying LDT to problems in geophysical fluid dynamics and climate science. In particular, LDT allows for understanding the properties of persistent deviations of climatic fields from long-term averages and for associating them to low-frequency, large-scale patterns. Additionally, LDT can be used in conjunction with rare event algorithms to explore rarely visited regions of the phase space. These applications are of key importance to improve our understanding of high-impact weather and climate events. Furthermore, LDT provides tools for evaluating the probability of noise-induced transitions between metastable climate states. This is, in turn, essential for understanding the global stability properties of the system. The goal of this review is manifold. First, we provide an introduction to LDT. We then present the existing literature. Finally, we propose possible lines of future investigations. We hope that this paper will prepare the ground for studies applying LDT to solve problems encountered in climate science and geophysical fluid dynamics.

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Section: Rare Event Sampling Algorithms Based On Large Deviation Theorymentioning

confidence: 99%

Section: Rare Event Sampling Algorithms Based On Large Deviation Theorymentioning

confidence: 99%

Applications of large deviation theory in geophysical fluid dynamics and climate science

et al. 2021

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“…AMS and its variants have been successfully used to compute reactive trajectories and extreme events in kinetic chemistry (Lopes & Lelièvre 2019) theoretical physics models (Rolland, Bouchet & Simonnet 2016), models of transitional flows (Rolland 2018) and idealised atmospheric flows (Bouchet et al 2019a;Simonnet et al 2021). Some variants have been applied to the study of extreme two-dimensional turbulent wakes (Lestang et al 2018;Lestang, Bouchet & Lévêque 2020) and oceanic flow reversals (Baars et al 2021). (iii) Thirdly, one can use importance sampling methods (L'Ecuyer, Mandjes & Tuffin 2009;Hartmann et al 2019).…”

Section: Introductionmentioning

confidence: 99%

“…(iii) Thirdly, one can use importance sampling methods (L'Ecuyer, Mandjes & Tuffin 2009;Hartmann et al 2019). These methods modify the dynamics so that the events of interest can then be sampled according to a new probability distribution function (see Lestang et al (2018) (Ragone & Bouchet 2019, where the rescaling of probabilities is based on large deviations for time averaged variables.…”

Section: Introductionmentioning

confidence: 99%

Collapse of transitional wall turbulence captured using a rare events algorithm

Rolland

2021

J. Fluid Mech.

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This text presents one of the first successful applications of a rare events sampling method for the study of multistability in a turbulent flow without stochastic energy injection. The trajectories of collapse of turbulence in plane Couette flow, and their probability and rate of occurrence are systematically computed using adaptive multilevel splitting (AMS). The AMS computations are performed in a system of size $L_x\times L_z=24\times 18$ at Reynolds number $R=370$ with an acceleration by a factor ${O}(10)$ with respect to direct numerical simulations (DNS) and in a system of size $L_x\times L_z=36\times 27$ at Reynolds number $R=377$ with an acceleration by a factor ${O}(10^3)$ . The AMS results are validated by a comparison with DNS in the smaller system. Visualisations indicate that turbulence collapses because the self-sustaining process of turbulence fails locally. The streamwise vortices decay first in streamwise elongated holes, leaving streamwise invariant streamwise velocity tubes that experience viscous decay. These holes then extend in the spanwise direction. The examination of more than a thousand trajectories in the $(E_{k,x}=\int u_x^2/2\,\textrm {d}^3\boldsymbol {x},E_{k,y-z}=\int (u_y^2/2+u_z^2/2)\,\textrm {d}^3\boldsymbol {x})$ plane in the smaller system confirms the faster decay of streamwise vortices and shows concentration of trajectories. This hints at an instanton phenomenology in the large size limit. The computation of turning point states, beyond which laminarisation is certain, confirms the hole formation scenario and shows that it is more pronounced in larger systems. Finally, the examination of non-reactive trajectories indicates that both the vortices and the streaks reform concomitantly when the laminar holes close.

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“…In order to face this problem, we use a rare event algorithm that concentrates ensemble simulations on trajectories of importance for the extreme events, optimizing the use of the computational resources. Rare event algorithms have recently been applied to turbulence problems (Grafke et al, 2015;Laurie & Bouchet, 2015;Ebener et al, 2019;Bouchet et al, 2019;Lestang et al, 2020) and climate applications (Ragone et al, 2018;Webber et al, 2019;Ragone & Bouchet, 2020;Plotkin et al, 2019). Based on the phenomenology of the dynamics for each family of extreme events, an appropriated type of rare event algorithm should be carefully chosen.…”

Section: Introductionmentioning

confidence: 99%