2021
DOI: 10.1007/s11222-021-10035-5
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Fast generation of Gaussian random fields for direct numerical simulations of stochastic transport

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Cited by 10 publications
(28 citation statements)
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“…We have shown [32] that the representation (12) provides fast convergence of the Eulerian statistics of the generated fields, as well as of the Lagrangian statistics of trajectories. In particular, it was proven that a convergence level with a few percents error can be achieved with N c ∼ 10 d and M ∼ 10 4 , where d = 4 for time dependent potentials and d = 3 for τ d → ∞.…”
Section: Dns Numerical Methods and Codementioning
confidence: 96%
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“…We have shown [32] that the representation (12) provides fast convergence of the Eulerian statistics of the generated fields, as well as of the Lagrangian statistics of trajectories. In particular, it was proven that a convergence level with a few percents error can be achieved with N c ∼ 10 d and M ∼ 10 4 , where d = 4 for time dependent potentials and d = 3 for τ d → ∞.…”
Section: Dns Numerical Methods and Codementioning
confidence: 96%
“…The model is analysed using direct numerical simulations (DNS) [32] and the decorrelation trajectory method (DTM) [33].…”
Section: Theoretical Methodsmentioning
confidence: 99%
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“…From a Lagrangian perspective, one can show that, under the assumption of scale-separation [4], the transport coefficients are related to statistical averages V(t) = d t x(t) , 2 D(t) = d t ( x 2 (t) − x(t) 2 ) over ensembles of trajectories {x(t)}. The latter are driven by the corresponding ensemble of stochastic velocity fields via ẋ(t) = v(x(t), t), known as a V-Langevin equation [12,13].…”
Section: Introductionmentioning
confidence: 99%