Daniela Tordella, POLITECNICO DI TORINO. DNS and LES In the past 15-20 years, DNS and LES become viable tools to treat transitioning and turbulent flows.

Slides:



Advertisements
Similar presentations
Subgrid-Scale Models – an Overview
Advertisements

Simulazione di Biomolecole: metodi e applicazioni giorgio colombo
LARGE EDDY SIMULATION Chin-Hoh Moeng NCAR.
Introduction to Computational Fluid Dynamics
TURBULENCE MODELING A Discussion on Different Techniques used in Turbulence Modeling -Reni Raju.
Turbulent Models.  DNS – Direct Numerical Simulation ◦ Solve the equations exactly ◦ Possible with today’s supercomputers ◦ Upside – very accurate if.
Development of Simulation Methodologies for Forced Mixers Anastasios Lyrintzis School of Aeronautics & Astronautics Purdue University.
Computational Fluid Dynamics - Fall 2013 The syllabus CFD references (Text books and papers) Course Tools Course Web Site:
Lecture 9 - Kolmogorov’s Theory Applied Computational Fluid Dynamics
Department of Ferrous Metallurgy
1 LES of Turbulent Flows: Lecture 9 (ME EN ) Prof. Rob Stoll Department of Mechanical Engineering University of Utah Fall 2014.
LES Combustion Modeling for Diesel Engine Simulations Bing Hu Professor Christopher J. Rutland Sponsors: DOE, Caterpillar.
LES of Turbulent Flows: Lecture 10 (ME EN )
September, Numerical simulation of particle-laden channel flow Hans Kuerten Department of Mechanical Engineering Technische Universiteit.
Physical-Space Decimation and Constrained Large Eddy Simulation Shiyi Chen College of Engineering, Peking University Johns Hopkins University Collaborator:
Coupling Continuum Model and Smoothed Particle Hydrodynamics Methods for Reactive Transport Yilin Fang, Timothy D Scheibe and Alexandre M Tartakovsky Pacific.
Combining the strengths of UMIST and The Victoria University of Manchester Aspects of Transitional flow for External Applications A review presented by.
3-D Large Eddy Simulation for Jet Noise Prediction A.Uzun, G. Blaisdell, A. Lyrintzis School of Aeronautics and Astronautics Purdue University Funded by.
1 B. Frohnapfel, Jordanian German Winter Academy 2006 Turbulence modeling II: Anisotropy Considerations Bettina Frohnapfel LSTM - Chair of Fluid Dynamics.
Wolfgang Kinzelbach with Marc Wolf and Cornel Beffa
Boundary Layer Meteorology Lecture 4 Turbulent Fluxes Energy Cascades Turbulence closures TKE Budgets.
Atmospheric turbulence Richard Perkins Laboratoire de Mécanique des Fluides et d’Acoustique Université de Lyon CNRS – EC Lyon – INSA Lyon – UCBL 36, avenue.
DETAILED TURBULENCE CALCULATIONS FOR OPEN CHANNEL FLOW
LES of Turbulent Flows: Lecture 3 (ME EN )
1 A combined RANS-LES strategy with arbitrary interface location for near-wall flows Michael Leschziner and Lionel Temmerman Imperial College London.
Partially Resolved Numerical Simulation CRTI RD Project Review Meeting Canadian Meteorological Centre August 22-23, 2006.
Atmospheric Flow over Terrain using Hybrid RANS/LES European Wind Energy Conference & Exhibition 2007 A. Bechmann, N.N. Sørensen and J. Johansen Wind Energy.
Turbulence Modelling: Large Eddy Simulation
CFD Modeling of Turbulent Flows
Zhaorui Li and Farhad Jaberi Department of Mechanical Engineering Michigan State University East Lansing, Michigan Large-Scale Simulations of High Speed.
How to use CFD (RANS or LES) models for urban parameterizations – and the problem of averages Alberto Martilli CIEMAT Madrid, Spain Martilli, Exeter, 3-4.
A. Spentzos 1, G. Barakos 1, K. Badcock 1 P. Wernert 2, S. Schreck 3 & M. Raffel 4 1 CFD Laboratory, University of Glasgow, UK 2 Institute de Recherche.
September, 18-27, 2006, Leiden, The Nederlands Influence of Gravity and Lift on Particle Velocity Statistics and Deposition Rates in Turbulent Upward/Downward.
Fast Low-Frequency Impedance Extraction using a Volumetric 3D Integral Formulation A.MAFFUCCI, A. TAMBURRINO, S. VENTRE, F. VILLONE EURATOM/ENEA/CREATE.
1 LES of Turbulent Flows: Lecture 11 (ME EN ) Prof. Rob Stoll Department of Mechanical Engineering University of Utah Fall 2014.
Brookhaven Science Associates U.S. Department of Energy MUTAC Review April , 2004, LBNL Target Simulation Roman Samulyak, in collaboration with.
1 LES of Turbulent Flows: Lecture 12 (ME EN ) Prof. Rob Stoll Department of Mechanical Engineering University of Utah Spring 2011.
AMS 599 Special Topics in Applied Mathematics Lecture 8 James Glimm Department of Applied Mathematics and Statistics, Stony Brook University Brookhaven.
1 LES of Turbulent Flows: Lecture 15 (ME EN ) Prof. Rob Stoll Department of Mechanical Engineering University of Utah Fall 2014.
Budgets of second order moments for cloudy boundary layers 1 Systematische Untersuchung höherer statistischer Momente und ihrer Bilanzen 1 LES der atmosphärischen.
1 LES of Turbulent Flows: Lecture 16 (ME EN ) Prof. Rob Stoll Department of Mechanical Engineering University of Utah Fall 2014.
LES of Turbulent Flows: Lecture 2 (ME EN )
Some Aspects of Parameterisation in Small Scaled Models Thomas Roschke German Weather Service Department of Aviation Meteorology.
Dynamic subgrid-scale modeling in large- eddy simulation of turbulent flows with a stabilized finite element method Andrés E. Tejada-Martínez Thesis advisor:
Semilinear Response Michael Wilkinson (Open University), Bernhard Mehlig (Gothenburg University), Doron Cohen (Ben Gurion University) A newly discovered.
This work was performed under the auspices of the Lawrence Livermore National Security, LLC, (LLNS) under Contract No. DE-AC52-07NA27344 Lawrence Livermore.
George Angeli 26 November, 2001 What Do We Need to Know about Wind for GSMT?
Quality of model and Error Analysis in Variational Data Assimilation François-Xavier LE DIMET Victor SHUTYAEV Université Joseph Fourier+INRIA Projet IDOPT,
Application of LES to CFD simulation of Diesel combustion 3604A058-2 Fumio KUWABARA.
Aerospace Engineering N. C. State University Air Terminal Wake Vortex Simulation D. Scott McRae, Hassan A. Hassan N.C. State University 4 September 2003.
Conference on PDE Methods in Applied Mathematics and Image Processing, Sunny Beach, Bulgaria, 2004 NUMERICAL APPROACH IN SOLVING THE PDE FOR PARTICULAR.
Reynolds Stress Constrained Multiscale Large Eddy Simulation for Wall-Bounded Turbulence Shiyi Chen Yipeng Shi, Zuoli Xiao, Suyang Pei, Jianchun Wang,
Lecture 12 - Large Eddy Simulation Applied Computational Fluid Dynamics Instructor: André Bakker © André Bakker ( ) © Fluent Inc. (2002)
BOUT++ Towards an MHD Simulation of ELMs B. Dudson and H.R. Wilson Department of Physics, University of York M.Umansky and X.Xu Lawrence Livermore National.
Convergence Studies of Turbulent Channel Flows Using a Stabilized Finite Element Method Andrés E. Tejada-Martínez Department of Civil & Environmental Engineering.
1 LES of Turbulent Flows: Lecture 7 (ME EN ) Prof. Rob Stoll Department of Mechanical Engineering University of Utah Spring 2011.
Viscosità Equazioni di Navier Stokes. Viscous stresses are surface forces per unit area. (Similar to pressure) (Viscous stresses)
HYBRID LARGE EDDY SIMULATION/REYNOLDS AVERAGED NAVIER-STOKES FORMULATION FOR NUMERICAL WEATHER PREDICITON H. A. Hassan North Carolina State University,
Direct numerical simulation has to solve all the turbulence scales from the large eddies down to the smallest Kolmogorov scales. They are based on a three-dimensional.
Avaraging Procedure. For an arbitrary quantity  the decomposition into a mean and fluctuating part can be written as.
1 LES of Turbulent Flows: Lecture 13 (ME EN ) Prof. Rob Stoll Department of Mechanical Engineering University of Utah Spring 2011.
7. Air Quality Modeling Laboratory: individual processes Field: system observations Numerical Models: Enable description of complex, interacting, often.
turbulent open channel flow
Introduction to the Turbulence Models
Boris Galperin Univ. South Florida
Introduction to Symmetry Analysis
LES of Turbulent Flows: Lecture 8 (ME EN )
Dynamical Models - Purposes and Limits
14. Computational Fluid Dynamics
Low Order Methods for Simulation of Turbulence in Complex Geometries
Presentation transcript:

Daniela Tordella, POLITECNICO DI TORINO

DNS and LES In the past years, DNS and LES become viable tools to treat transitioning and turbulent flows --- Improvements in numerical methods --- Improvements in computers – speed, memory, cost today is not unusual to have capabilities equivalent of Cray 1 in a small group Successful application to a number of problems Tremendous potential in the future for: Understanding transition and turbulence Prediction in applications

DNS Numerical calculation that solves for the time development of the detailed, unsteady structures in a transitioning or a turbulent flow field NOT a numerical solution of Reynolds- or Favre- averaged equations It is a numerical experiment analogous to a laboratory experiment statistical scatter researcher must think like an experimentalist and ask proper questions, etc.

Strengths of Approach Compared to laboratory experiments * know all the variables at each point in space and time can follow large-scale structures can in theory comoute any statistic of interest, e.g. pressure-velocity correlation can readily compare with theory * Easy to control parameters to respect experimental conditions Compared to theory * Circumvent the closure problem

Weaknesses of Approach Limited spatial and temporal resolution Limits Reynolds number (and other key paramenters) without resorting to modeling Considers physics depending mainly on the large-scale motions, difficult to treat Kolmogorov-scale processes Opinion – complementary to laboratory experiment, theory in any particular problem (whether fundamental or applied) use methods (laboratory, theory, etc.) best suited for the problem.

Full Turbulence Simulation FTS Calculation in which all of the dynamically significant lenghth and time scales are included L_e -- energy-containing scale (e.g., integral scale) L_ k -- Kolmogorov scale (viscous dissipation scale) L_k = ( ³/  )¼ L_e / L_ k = Re ¾ N -- number of grid poits in one direction ~ Re ¾ Ntot ~ N³ ~ ( Re ¾)³ Number of time steps increases with Re as well

Large Eddy Simulation LES Motivated by the desire to remove Reynolds number limitations Prior to numerical integration are spatially filtered to eliminate the scales of motion smaller than those resolvable on the computational mesh. The effect of the subgrid-scale (SGS) motion is modeled u = ū + u’ ū – grid-scale (computed) motions u’ – subgrid-scale (modeled) motions Several approaches – model using analogies with Reynolds- averaged models

LES compared to FTS Advantages: potential to treat very high Reynolds number flows; fast reaction, etc. possibility of use in applied problems Disadvantages: ad hoc models are necessary to close euqations introduces some uncertainty into validity of results

LES compared to Reynolds-averaging approach Advantages: Large-scale motions treated directly; Can follow large-scale structures Only small scales are modeled Less energy in modeled scale; more universality Expected to provide more realistic results Disadvantages: 3D, time-dependent, high resolution

=+ 13