Asset Details
MbrlCatalogueTitleDetail
Do you wish to reserve the book?
Synchronization to Big Data: Nudging the Navier-Stokes Equations for Data Assimilation of Turbulent Flows
by
Biferale, Luca
, Mazzino, Andrea
, Clark Di Leoni, Patricio
in
Algorithms
/ Atmospheric models
/ Computational fluid dynamics
/ Configurations
/ Data assimilation
/ Direct numerical simulation
/ Distribution functions
/ Equations of motion
/ Fluid dynamics
/ Fluid flow
/ Hurricanes
/ Isotropic turbulence
/ Magnetohydrodynamic turbulence
/ Magnetohydrodynamics
/ Mathematical models
/ Navier-Stokes equations
/ Oceans
/ Parameterization
/ Probability distribution functions
/ Rayleigh-Benard convection
/ Reconstruction
/ Synchronism
2020
Hey, we have placed the reservation for you!
By the way, why not check out events that you can attend while you pick your title.
You are currently in the queue to collect this book. You will be notified once it is your turn to collect the book.
Oops! Something went wrong.
Looks like we were not able to place the reservation. Kindly try again later.
Are you sure you want to remove the book from the shelf?
Synchronization to Big Data: Nudging the Navier-Stokes Equations for Data Assimilation of Turbulent Flows
by
Biferale, Luca
, Mazzino, Andrea
, Clark Di Leoni, Patricio
in
Algorithms
/ Atmospheric models
/ Computational fluid dynamics
/ Configurations
/ Data assimilation
/ Direct numerical simulation
/ Distribution functions
/ Equations of motion
/ Fluid dynamics
/ Fluid flow
/ Hurricanes
/ Isotropic turbulence
/ Magnetohydrodynamic turbulence
/ Magnetohydrodynamics
/ Mathematical models
/ Navier-Stokes equations
/ Oceans
/ Parameterization
/ Probability distribution functions
/ Rayleigh-Benard convection
/ Reconstruction
/ Synchronism
2020
Oops! Something went wrong.
While trying to remove the title from your shelf something went wrong :( Kindly try again later!
Do you wish to request the book?
Synchronization to Big Data: Nudging the Navier-Stokes Equations for Data Assimilation of Turbulent Flows
by
Biferale, Luca
, Mazzino, Andrea
, Clark Di Leoni, Patricio
in
Algorithms
/ Atmospheric models
/ Computational fluid dynamics
/ Configurations
/ Data assimilation
/ Direct numerical simulation
/ Distribution functions
/ Equations of motion
/ Fluid dynamics
/ Fluid flow
/ Hurricanes
/ Isotropic turbulence
/ Magnetohydrodynamic turbulence
/ Magnetohydrodynamics
/ Mathematical models
/ Navier-Stokes equations
/ Oceans
/ Parameterization
/ Probability distribution functions
/ Rayleigh-Benard convection
/ Reconstruction
/ Synchronism
2020
Please be aware that the book you have requested cannot be checked out. If you would like to checkout this book, you can reserve another copy
We have requested the book for you!
Your request is successful and it will be processed during the Library working hours. Please check the status of your request in My Requests.
Oops! Something went wrong.
Looks like we were not able to place your request. Kindly try again later.
Synchronization to Big Data: Nudging the Navier-Stokes Equations for Data Assimilation of Turbulent Flows
Journal Article
Synchronization to Big Data: Nudging the Navier-Stokes Equations for Data Assimilation of Turbulent Flows
2020
Request Book From Autostore
and Choose the Collection Method
Overview
Nudging is an important data assimilation technique where partial field measurements are used to control the evolution of a dynamical system and/or to reconstruct the entire phase-space configuration of the supplied flow. Here, we apply it to the canonical problem of fluid dynamics: three-dimensional homogeneous and isotropic turbulence. By doing numerical experiments we perform a systematic assessment of how well the technique reconstructs large- and small-scale features of the flow with respect to the quantity and the quality or type of data supplied to it. The types of data used are (i) field values on a fixed number of spatial locations (Eulerian nudging), (ii) Fourier coefficients of the fields on a fixed range of wave numbers (Fourier nudging), or (iii) field values along a set of moving probes inside the flow (Lagrangian nudging). We present state-of-the-art quantitative measurements of the scale-by-scale transition to synchronization and a detailed discussion of the probability distribution function of the reconstruction error, by comparing the nudged field and the truth point by point. Furthermore, we show that for more complex flow configurations, like the case of anisotropic rotating turbulence, the presence of cyclonic and anticyclonic structures leads to unexpectedly better performances of the algorithm. We discuss potential further applications of nudging to a series of applied flow configurations, including the problem of field reconstruction in thermal Rayleigh-Bénard convection and in magnetohydrodynamics, and to the determination of optimal parametrization for small-scale turbulent modeling. Our study fixes the standard requirements for future applications of nudging to complex turbulent flows.
Publisher
American Physical Society
This website uses cookies to ensure you get the best experience on our website.