Modelling the growth rate of a tracer gradient using stochastic differential equations

被引:0
|
作者
Naraigh, Lennon O. [1 ]
机构
[1] Univ Coll Dublin, Sch Math Sci, Dublin 4, Ireland
关键词
Turbulent mixing; Turbulence modelling; Fokker-Planck equation; 2D TURBULENCE; PARTICLES; ALIGNMENT; DYNAMICS; ELEMENTS; REGIME; FIELDS; FLOWS;
D O I
10.1016/j.euromechflu.2010.10.001
中图分类号
O3 [力学];
学科分类号
08 ; 0801 ;
摘要
We develop a model in two dimensions to characterise the growth rate of a tracer gradient mixed by a statistically homogeneous flow that varies on arbitrary timescales. The model is based on the orientation dynamics of the passive-tracer gradient with respect to the straining (compressive) direction of the flow, and involves reducing the dynamics to a set of stochastic differential equations. The statistical properties of the system emerge from solving the associated Fokker-Planck equation. Within the model framework, the tracer gradient aligns with the compressive direction when the mean effective rotation in the flow is zero. At finite values of rotation, the tracer gradient aligns with a different direction, but the mean growth rate of the gradient is positive in all cases. In a certain limiting case, namely temporally decorrelated (rapidly varying) flows, exact, analytical expressions exist for the mean growth rate. Using numerical simulations, we assess the extent to which our model applies to real mixing protocols, and map the stochastic parameters on to flow parameters. (C) 2010 Elsevier Masson SAS. All rights reserved.
引用
收藏
页码:89 / 98
页数:10
相关论文
共 50 条
  • [21] On the low-dimensional modelling of Stratonovich stochastic differential equations
    Xu, C
    Roberts, AJ
    PHYSICA A, 1996, 225 (01): : 62 - 80
  • [22] Nonnegative compartment dynamical system modelling with stochastic differential equations
    Kawai, Reiichiro
    APPLIED MATHEMATICAL MODELLING, 2012, 36 (12) : 6291 - 6300
  • [23] On the modelling of stochastic differential equations subject to squared white noises
    Universite du Quebec a Montreal, Montreal, Canada
    Syst Anal Modell Simul, 3-4 (169-175):
  • [24] Approximation to Stochastic Variance Reduced Gradient Langevin Dynamics by Stochastic Delay Differential Equations
    Chen, Peng
    Lu, Jianya
    Xu, Lihu
    APPLIED MATHEMATICS AND OPTIMIZATION, 2022, 85 (02):
  • [25] Modelling biochemical reaction systems by stochastic differential equations with reflection
    Niu, Yuanling
    Burrage, Kevin
    Chen, Luonan
    JOURNAL OF THEORETICAL BIOLOGY, 2016, 396 : 90 - 104
  • [26] Modelling uncertainties in electrical power systems with stochastic differential equations
    Verdejo, Humberto
    Awerkin, Almendra
    Kliemann, Wolfgang
    Becker, Cristhian
    INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS, 2019, 113 : 322 - 332
  • [27] Modelling of solid electrolyte interphase growth using neural ordinary differential equations
    Ramasubramanian, S.
    Schomburg, F.
    Roeder, F.
    ELECTROCHIMICA ACTA, 2024, 473
  • [28] MODELLING THE CANCER GROWTH PROCESS BY STOCHASTIC DELAY DIFFERENTIAL EQUATIONS UNDER VERHULTS AND GOMPERTZ'S LAW
    Mazlan, Mazma Syahidatul Ayuni
    Rosli, Norhayati
    Azmi, Nina Suhaity
    JURNAL TEKNOLOGI, 2016, 78 (3-2): : 77 - 82
  • [29] Modelling the Cancer Growth Process by Stochastic Differential Equations with the Effect of Chondroitin Sulfate (CS) as Anticancer Therapeutics
    Mazlan, Mazma Syahidatul Ayuni
    Rosli, Norhayati
    Ichwan, Solachuddin Jauhari Arief
    Azmi, Nina Suhaity
    1ST INTERNATIONAL CONFERENCE ON APPLIED & INDUSTRIAL MATHEMATICS AND STATISTICS 2017 (ICOAIMS 2017), 2017, 890
  • [30] Parametric estimation of stochastic differential equations via online gradient descent
    Nakakita, Shogo
    JAPANESE JOURNAL OF STATISTICS AND DATA SCIENCE, 2024,