Are extreme dissipation events predictable in turbulent fluid flows?

Abstract

We derive precursors of extreme dissipation events in a turbulent channel flow. Using a recently developed method that combines dynamics and statistics for the underlying attractor, we extract a characteristic state that precedes laminarization events that subsequently lead to extreme dissipation episodes. Our approach utilizes coarse statistical information for the turbulent attractor, in the form of second order statistics, to identify high-likelihood regions in the state space. We then search within this high probability manifold for the state that leads to the most finite-time growth of the flow kinetic energy. This state has both high probability of occurrence and leads to extreme values of dissipation. We use the alignment between a given turbulent state and this critical state as a precursor for extreme events and demonstrate its favorable properties for prediction of extreme dissipation events. Finally, we analyze the physical relevance of the derived precursor and show its robust character for different Reynolds numbers. Overall, we find that our choice of precursor works well at the Reynolds number it is computed at and at higher Reynolds number flows with similar extreme events.

Precursors of backflow events and their relationship with the near-wall self-sustaining process

“…The high values of flatness at the near-wall region of wall-bounded flows shows that this region is highly intermittent (Diaz-Daniel, Laizet & Vassilicos 2017;Farazmand & Sapsis 2017). Hence, large deviations (extreme events) related to persistent nonlinear energy transfers from the large scales of motion (LSM) to the mean flow happen near the wall (Blonigan, Farazmand & Sapsis 2019). This active region is formed by alternating and organised high/low-momentum streaks with a mean spanwise wavelength λ + θ ≈ 100 (Kim, Kline & Reynolds 1971).…”

Extreme wall shear stress events in turbulent pipe flows: spatial characteristics of coherent motions

“…These large deviations are typical in chaotic systems usually characterised by an exponential decay of the probabilities of large fluctuations (Touchette 2009). According to Blonigan, Farazmand & Sapsis (2019), large deviations or extreme events in turbulent wall-bounded flows are the result of persistent nonlinear energy transfers and extreme bursts associated with a transmission of energy from large scales of motion (LSM) to the mean flow, producing growth on the energy input and its dissipation rate (Farazmand & Sapsis 2017).…”

Output-weighted optimal sampling for Bayesian regression and rare event statistics using few samples

“…In this case it is critical to choose the input samples carefully so that they provide the best possible information for the output of interest [1][2][3]. A class of problems that belong in this family is the probabilistic quantification of extreme or rare events rising from high dimensional complex systems such as turbulence [4][5][6][7][8], networks [9], waves [10][11][12], and materials or structures [13,14]. Of course the considered set-up is not limited to extreme or rare events but it is also relevant for any problem where the aim is to quantify the input-output relationship with very few but carefully selected data points.…”