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Turbulence for experimental Fluid Mechanics professionals

Turbulence for experimental Fluid Mechanics professionals
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Turbulence for experimental Fluid Mechanics professionals

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Introduction
This handbook is a comprehensive guide for professionals in experimental fluid mechanics, focusing on turbulence. It introduces turbulence, its significance in modern science, and the dynamics of fluid flow, including fractal structures, deterministic chaos, and self-organization.
Definition and Examples of Turbulence
Turbulence is defined and illustrated through examples such as grid turbulence, free shear layers, boundary layers, wakes, heat transfer, and chemical turbulence. The basic equations of fluid dynamics, including the Navier-Stokes equations, are discussed with emphasis on their properties and applications.
Turbulent Flow Equations
The handbook covers the Reynolds equations, Reynolds stress, energy balance, and challenges in turbulence. It also discusses the Bernoulli equation and the transport of passive scalars.
Occurrence and Transition to Turbulence
Key experiments and theories related to stability and transition to turbulence are explored, including Reynolds' experiment and hydrodynamic instabilities like Kelvin-Helmholtz and Rayleigh-Bénard.
Developed Turbulence
This section provides a statistical description of turbulence, including spectral characteristics, turbulence scales, and the Kolmogorov theory. It also covers turbulent diffusion dynamics and the role of dynamical systems.
Modeling of Turbulence
Different modeling approaches are discussed, including Direct Numerical Simulation (DNS), Large Eddy Simulation (LES), and Reynolds-Averaged Navier-Stokes (RANS) methods. Models based on turbulent viscosity and Reynolds stress are also covered.
Phenomenology of Turbulence
The handbook delves into the kinematics of turbulence, vortex dynamics, and self-sustaining mechanisms of turbulent flow. It discusses coherent structures, hairpin vortices, and the dynamics of these structures.
Annexes
The annexes provide additional resources such as vector calculus, quantities used in turbulence theory, symmetry of turbulent flow, and statistical tools for analyzing turbulence.
Introduction to Dynamical Systems and Turbulence
The document discusses fluid systems' behavior under turbulent conditions, explaining that increased shifts lead to non-linearities and complex structures, potentially resulting in deterministic chaos and turbulence.
Phase Space and Attractors
Dynamical systems are studied in a phase space, where attractors indicate the system's tendency to evolve towards a particular state. The document describes point attractors, limit cycles, and strange attractors, the latter being associated with deterministic chaos.
Bifurcation and Fractal Structures
The bifurcation point marks the stability limit of a system's structure, leading to multiple potential development scenarios. Fractal structures are typical in systems with non-linear behavior and numerous degrees of freedom.
Deterministic Chaos
Deterministic chaos refers to seemingly random behavior governed by underlying rules, common in natural systems, including turbulent fluid flows.
Lorenz System and Chaos Theory
The document introduces the Lorenz system, a simplified mathematical model of atmospheric convection, demonstrating chaotic behavior.
Phase Space and Strange Attractors
The document discusses the concept of phase space in dynamical systems, focusing on strange attractors, particularly the Lorenz attractor.
The Butterfly Attractor
The butterfly attractor, part of the Lorenz system, symbolizes chaos theory's early exploration.
Simulation and Sensitivity to Initial Conditions
Simulations of the Lorenz system under varying initial conditions demonstrate sensitivity to small changes, highlighting the chaotic nature of the system.
Self-Organization and Coherent Structures
The document explores self-organization in dynamical systems, referencing Ilya Prigogine's work on dissipative structures.
Definition and Characteristics of Turbulence
Turbulence is characterized by randomness, diffusivity, and high vorticity, enhancing mixing significantly compared to molecular diffusion.
Continuum Environment and Knudsen Number
An environment is considered a continuum if the Knudsen number (Kn) is much less than 1, allowing for the definition of an "elementary" volume where physical quantities can be averaged over molecules.
Euler and Lagrange Descriptions
The motion of fluid can be studied using the Lagrange method, which tracks individual fluid particles, and the Euler method, which examines changes in kinematic quantities at fixed points in space.
Conservation Laws
Conservation laws, based on symmetry, are crucial in fluid dynamics. The continuity equation applies the conservation of mass to fluid mechanics.
Navier-Stokes Equations
The Navier-Stokes (N-S) equations describe fluid motion, incorporating aspects like turbulence.
Shear Stress Tensor and N-S Equations
The document discusses the formulation of shear stress using a tensor and introduces the Navier-Stokes (N-S) equations.
Properties of N-S Equations
N-S equations are second-order partial non-linear differential equations, leading to complex behaviors such as chaos and self-organization.
Symmetry of N-S Equations
The document outlines six known symmetries of N-S equations, crucial for ensuring the mathematical model's consistency with physical laws.
Equations for Pressure
The pressure in fluid dynamics is linked to the velocity field, and its distribution is governed by a Poisson equation.
Formulation for Vorticity Field
Vorticity, defined as the curl of velocity, is a fundamental property of turbulent flow.
Turbulent Flow Equation
The document introduces a statistical approach to describe turbulent flow, focusing on ensemble averages and probability laws.
Reynolds Equations
Reynolds equations provide a framework for analyzing statistically mean states of turbulent flow.
Introduction to Reynolds Equations
The document discusses the derivation of Reynolds equations from the Navier-Stokes (N-S) equations through averaging operations.
Reynolds Stress Tensor
The Reynolds stress tensor is introduced as a key component in the Reynolds equations, representing the average flow of momentum due to velocity fluctuations.
Challenges in Solving Reynolds Equations
The document outlines the challenge of solving Reynolds equations due to the presence of more unknowns than equations.
Modeling Approaches
Various approaches to modeling Reynolds stress are discussed, including the turbulent viscosity hypothesis by Boussinesq and the concept of mixing length by Prandtl.
Energy Balance in Turbulent Flows
The document examines the energy balance in turbulent flows, focusing on kinetic energy and its dissipation due to viscosity.
Conclusion
The document concludes by emphasizing the complexity of modeling turbulent flows and the ongoing challenges in closing the Reynolds equations.
Specifications and Equations
The document discusses the behavior of turbulent flows, focusing on the Reynolds number's impact on turbulence dynamics.
Main Problem of Turbulence
The document identifies the "main problem of turbulence" as the inability to close the system of Reynolds equations due to more unknowns than equations.
Bernoulli Equation
The Bernoulli equation is derived from the Navier-Stokes equations under assumptions of inviscid, incompressible fluid flow.
Transport of Passive Scalar
The document describes the transport of passive scalars, such as temperature, in fluids.
Occurrence of Turbulence
The transition from laminar to turbulent flow is discussed, emphasizing the role of perturbations and the breakdown of symmetries in the Navier-Stokes equations.
Reynolds’ Experiment
Reynolds' 1883 experiment is detailed, demonstrating the transition from laminar to turbulent flow in a pipe.
Theory of Stability
The document concludes with a discussion on stability theory, using mathematical methods to analyze the stability of fluid flows.
Introduction to Hydrodynamic Stability
The document discusses the use of linear mathematical models to examine hydrodynamic stability.
Linear Model and Flow Perturbations
The linear theory examines whether perturbations in laminar flow cease or grow over time.
Stability of Inviscid Flows
The document explores the stability of inviscid flows using Euler equations and Rayleigh's equation.
Stability of Viscous Flows
Viscous flow stability is analyzed using the Orr-Sommerfeld equation.
Types of Hydrodynamic Instability
The document describes various types of flow instabilities, including Kelvin-Helmholtz instability.
Kelvin-Helmholtz Instability
This instability occurs in free shear layers, characterized by periodic vortex structures.
Rayleigh-Bénard Instability
Occurs when a fluid in a dish is heated from below, leading to motion due to density changes.
Taylor-Couette Instability
Arises between two rotating cylinders filled with viscous fluid.
Görtler Instability
Occurs along concavely curved walls, with the Görtler number expressing the ratio of centrifugal to viscous forces.
Tollmien-Schlichting Instability
Happens in boundary layers at a critical Reynolds number, leading to vortices perpendicular to flow direction.
Wakes Behind Bodies
Instability in wakes behind bluff bodies can lead to von Kármán-Bénard vortex streets.
Other Instabilities
Includes Rayleigh-Taylor and Saffman-Taylor instabilities, among others.
Transition to Turbulence
The transition involves initial instability followed by turbulence.
Introduction to Turbulence Spots
The document discusses the development of turbulence spots characterized by intermittent signals during velocity measurements.
Transition to Turbulence
The transition to turbulence in boundary layers is explored.
Bypass Transition
Bypass transition to turbulence occurs when large perturbations bypass the traditional Tollmien-Schlichting wave mechanism.
Developed Turbulence
The document explains the development of vortex structures and the role of stretching in increasing vorticity.
Statistical Description of Turbulence
Turbulent flows are characterized as random, requiring statistical tools for analysis.
One-dimensional Spectrum
The document discusses the measurement of turbulent flows using hot-wire probes and the application of the Taylor hypothesis for spatial correlations.
Energy and Dissipation Spectrum
The energy spectrum is introduced as a simplified representation of the velocity spectrum tensor.
Taylor Hypothesis
The Taylor hypothesis is described as a method to approximate spatial correlations using time correlations.
Structure Functions
Structure functions are statistical tools used to analyze turbulent signals.
Turbulence Scales
The concept of turbulence scales is introduced, with a focus on the cascade of scales in developed turbulent flows.
Fractal Structure of Scales
The document explores the fractal nature of turbulence scales.
Kolmogorov's Theories and Models
Kolmogorov's theories on turbulence, particularly K41 and K62, are central to understanding energy dissipation in turbulent flows.
Structure Functions
Structure functions measure fluctuation intensity based on scale.
Turbulent Diffusion
Turbulent diffusion involves the separation of fluid particles, with Richardson's model describing it as more effective than classical diffusion methods.
Dynamical Systems and Shell Models
Fluid flow is described by partial differential equations, which can be approximated by ordinary differential equations.
Examples of Turbulent Flows
Grid turbulence is a typical example, occurring behind a grid of cylindrical rods.
1. Turbulence and Shear Flows
Turbulence is characterized by kinetic energy dependency on flow parameters such as mean velocity, grid geometry, and Reynolds number.
2. Statistical Quantities and Reynolds Stress
In turbulent flows, statistical quantities are distributed symmetrically, leading to zero mean velocity in certain directions.
3. Free Shear Areas
Free shear areas, such as jets and wakes, form from interactions between flows.
4. Modeling of Turbulence
Mathematical modeling of turbulence involves discretization in space and time, with methods like DNS, RANS, and LES.
5. Numerical Methods and Computational Challenges
Numerical solutions of N-S equations require parallel computing due to their elliptic nature.
Introduction to Turbulence Modeling
This document discusses various methods for modeling turbulence in fluid dynamics, focusing on RANS, DNS, and LES methods.
1. RANS Methods
Reynolds-Averaged Navier-Stokes (RANS) methods are widely used for modeling turbulent flows.
2. Direct Numerical Simulation (DNS)
DNS involves solving the Navier-Stokes equations directly using numerical methods.
3. Large Eddy Simulation (LES)
LES filters the Navier-Stokes equations to separate large and small structures.
4. Turbulent Viscosity Models
These models simplify the turbulence problem by assuming isotropic turbulent viscosity.
5. Transport Equation Models
More complex models use transport equations for turbulent variables like kinetic energy and dissipation rate.
6. Reynolds Stress Models (RSM)
RSM directly models Reynolds stress, offering detailed turbulence representation.
7. Phenomenology of Turbulence
This section explores the structure of turbulent flow, emphasizing the role of vortices.
Vorticity in Inviscid Fluid
The document discusses the concept of vorticity in inviscid fluids, emphasizing that the vorticity field is non-divergent.
Circulation and Vortex Intensity
Circulation is defined as the flow of the velocity vector along a closed curve bordering a vortex tube.
Kelvin's Theorem
Kelvin's theorem states that the time derivative of velocity circulation along a closed curve equals the circulation of acceleration along the same curve.
Mathematical Models of Vortices
The document explores mathematical models of vortices, using cylindrical coordinates to describe vortex flow.
3D Vortex Models
The Burgers vortex model is introduced for 3D analysis, accounting for convection, diffusion, and longitudinal stretching of vortices.
Mutual Interaction of Vortices
Vortex filaments can modify due to viscosity, despite the Thomson theorem suggesting otherwise.
Mechanism of Vorticity Generation
Kolmogorov's theory describes energy transfer from larger to smaller vortices.
Other Forces Acting on Vortex Structures
Vortices behave like cylindrical bodies, subject to drag and Magnus forces.
Self-Sustaining Mechanisms of Turbulent Flow
Turbulent flows sustain themselves through coherent structures in wall flows.
Research and Visualization
Researching coherent structures in turbulent flow is challenging, requiring advanced visualization methods like PIV and DNS.
Overview of Turbulent Boundary Layer Structures
This document provides an in-depth analysis of the structures within a turbulent boundary layer, focusing on the dynamics and interactions of vortices.
1. Coherent Vortex Structures
The document describes the presence of hairpin vortices within the turbulent boundary layer.
2. Longitudinal Streaks and the Bursting Phenomenon
Longitudinal streaks of low velocity play a crucial role in turbulence generation.
3. Dynamics of Coherent Structures
The document explores the origin and regeneration of coherent structures, particularly hairpin vortices.
4. Mechanisms of Vortex Creation
Various mechanisms for vortex creation are discussed, including the bridging of longitudinal vortices.
5. References and Further Reading
The document concludes with a list of recommended literature for further exploration of fluid dynamics and turbulence.
Introduction to Cauchy Distribution
The Cauchy distribution is derived from phenomena that are intermittent in nature.
Statistical Moments
Statistical moments are tools for analyzing random signals.
Correlation Function
The autocorrelation function for a stationary random process is defined.
Spectra
The velocity auto-covariance and autocorrelation functions are defined.
Fourier Transform
The Fourier transform is a classical method for signal analysis.
Wavelet Transform
The wavelet transform is a powerful tool for joint signal analysis in time and frequency domains.
Explanatory Functions and Wavelets The document discusses the conditions for reproducing single terms up to a certain order using wavelets.
Examples of Wavelets The Morlet wavelet, modulated by a Gaussian function, is discussed for its localization properties in time and frequency domains.
Wavelet Analysis Wavelets can generate a complete base of orthogonal functions, useful for processing functions without information loss and for compression.
Proper Orthogonal Decomposition (POD) Introduced by J.L. Lumley, POD is used to project coherent structures onto velocity fields.
Similarity Laws in Fluid Mechanics The document explains the importance of the Reynolds number, which characterizes the flow of viscous fluid by comparing inertial and viscous forces.
History of Turbulence Research A timeline of significant milestones in turbulence research is provided, from early philosophical ideas to modern computational methods.
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Catalog excerpts

Turbulence for experimental Fluid Mechanics professionals-1

Turbulence Turbulence Handbook for Experimental Fluid Mechanics Professionals

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Turbulence for experimental Fluid Mechanics professionals-3

Release 1.1 This Handbook may not be copied, photocopied, translated, modified, or reduced to any electronic medium or machine-readable form, in whole, or in part, without the prior written consent of Dantec Dynamics. Date: September 2012.  Copyright 2009–2012 by OPTEK, Caslavska 9, 130 00 Praha 3, Czech Republic, Prof. Vaclav Uruba Parts  Copyright 2012 by Dantec Dynamics A/S, Tonsbakken 16-18, DK-2740 Skovlunde, Denmark All rights reserved. To allow for design and specification improvements, the information in this document is subject to change at any time, without notice. This Publication...

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We are specifying here only the labels used in scripts systematically and repeatedly. Other labels will be explained during their use. Latin alphabet D (k) dissipation spectrum f longitudinal correlation function (non-dimensional); frequency g transverse correlation function (non-dimensional) i imaginary unit L Integral length scale tensor “a” arithmetic average mean file value fluctuations Hamiltonian del operator (vector!) substantial derivative with respect to time regular estimate Greek alphabet intermittency coefficient Dirac function Kronecker delta dissipation rate Levi-Civit...

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3. Introduction With the development of time-resolved methods for flow measurement, point, plane and volumetric, researchers and scientists have found that there is no such thing as “steady flow”, and that practically every flow is highly fluctuating. The “statistic” approach to evaluation of measured fluctuations has very often resulted in characterizing such fluctuations by a single number, “Turbulence intensity [%]”. But these fluctuations, when measured by sufficiently fast time-resolved methods, like CTA or TimeResolved PIV, carry a wealth of very important information about the flow and...

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Introduction to the study of turbulence Equation Section 3 The flow of water in the river, clouds in the sky, burning flames, the starry universe – these are some examples of phenomena that we can label as turbulent. Turbulence has always been a fascinating phenomenon for people, even though (or indeed because) it is difficult to grasp, due to its variability and complexity. Ever since ancient times, thinkers have attempted to come to terms with the existence of turbulence, and this effort continues to this day. The process of recognizing the laws of turbulence has not been finished. It has been...

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Everyday experiences enable us to recognize turbulence. The smoke that rises from a cigarette or fire shows the irregular behavior of the moving air that carries it. Wind is subject to sharp local changes in direction and speed, which can have dramatic results for sailors and pilots. During transport by passenger aircraft, the term “turbulence” is often associated with buckling seatbelts. The term is also used when describing free streams and streaks. When water flows in a river, its presence has an important effect for the settling of sediment on the bottom. Quick flow of fluid around an obstacle...

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The issue of turbulence somehow did not fall into the concept of science as defined by Newton, and the behavior of objects during a state of turbulence was not predicted reliably. In the past, turbulent behavior was often associated with magic, and to this day this issue is engulfed by a veil of secrecy. This problem has been dealt with systematically by a large number of scientific experts (see the addendum for a detailed overview), but practically all identified individuals involved in the study of physics have examined this problem at least to a certain extent. It is said about the Father...

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The flow of fluids can be qualitatively characterized as laminar or turbulent. Laminar flow is typically either a very slow motion or involves a level of viscosity. Fluid particles move evenly and slide across each other in layers (lamina is Latin for layer, plate), and are therefore laminar. However, turbulent flows (turbulentus is Latin for uneven) are characterized by quick motion or a low effect of viscosity, when even minor perturbations in stream grow uncontrollably and cause unpredictable local behavior of fluid and intensive eddy mixing in the whole area (a more exact definition of turbulence...

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The “bifurcation point” characterizes the condition of the system at the stability limit of a certain structure. Further development of the system can continue according to multiple scenarios. There can be two or more, or there can be an unlimited amount. The selection of the correct specific scenario depends on Brownian motion events, and from the point of view of anthropomorphic scales5 there is no remaining option other than to consider this process to be random, when the probability of the realization of individual scenarios is not zero, but is less than 1. At the end of each scenario, there...

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Figure 3.5 – Clouds The Platonic world of simple geometric sections (type of spheres, boxes, polyhedrons,…) described in classical teaching materials emerged thanks to linear mathematical models, and even relatively complicated systems can report only a very low number of active degrees of freedom. However, we live in a non-linear world of fractal geometry. For examination and analysis of fractal objects, methods developed for simple objects generally cannot be used. Characteristics such as the length of a line or the contents of a surface do not have practical significance for fractals, but...

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Turbulence for experimental Fluid Mechanics professionals-18

an apparent cause appears chaotic. During chaotic behavior of the system, its complexity grows (the number of active degrees of freedom). It has been shown that linear systems are mere idealization, and in reality no real system can be perfectly described by the linear mathematical model. The linear model can apply for the actual system with sufficient accuracy only for small shifts from the normal condition, but there is a certain limit, above which the behavior of the system becomes non-linear. However, most systems are strongly non-linear. The linearized model for such systems applies only...

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