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Detecting Intermittent Turbulence Using Advanced Signal Processing Techniques Christina Ho, Xiaoning Gilliam, and Sukanta Basu Texas Tech University AIAA – January 8 th , 2009

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Page 1: Detecting Intermittent Turbulence Using Advanced Signal Processing Techniques Christina Ho, Xiaoning Gilliam, and Sukanta Basu Texas Tech University AIAA

Detecting Intermittent Turbulence Using Advanced Signal Processing Techniques

Christina Ho, Xiaoning Gilliam, and

Sukanta BasuTexas Tech University

AIAA – January 8th, 2009

Page 2: Detecting Intermittent Turbulence Using Advanced Signal Processing Techniques Christina Ho, Xiaoning Gilliam, and Sukanta Basu Texas Tech University AIAA

Motivation

Page 3: Detecting Intermittent Turbulence Using Advanced Signal Processing Techniques Christina Ho, Xiaoning Gilliam, and Sukanta Basu Texas Tech University AIAA

Wind Turbine Inflow Generation

t = 0

t = T

TurbSim User’s Guide

Page 4: Detecting Intermittent Turbulence Using Advanced Signal Processing Techniques Christina Ho, Xiaoning Gilliam, and Sukanta Basu Texas Tech University AIAA

Wind Turbine Inflow Generation: IEC Spectral Models

Kaimal’s Spectral Model (neutral boundary layer)

Several other models: e.g., Mann’s Uniform Shear Model

Page 5: Detecting Intermittent Turbulence Using Advanced Signal Processing Techniques Christina Ho, Xiaoning Gilliam, and Sukanta Basu Texas Tech University AIAA

Nighttime (Intermittent) Turbulence

Observation (stable boundary layer)

CASES-99, Poulos et al. (2002)

Over the US Great Plains, intermittent turbulence frequently occurs in thepresence of nocturnal low-level jets.

Page 6: Detecting Intermittent Turbulence Using Advanced Signal Processing Techniques Christina Ho, Xiaoning Gilliam, and Sukanta Basu Texas Tech University AIAA

Background

Page 7: Detecting Intermittent Turbulence Using Advanced Signal Processing Techniques Christina Ho, Xiaoning Gilliam, and Sukanta Basu Texas Tech University AIAA

The Atmospheric Boundary Layer (ABL)

ABL (~ 1km)

• Turbulent fluxes of heat, momentum, and moisture are driving forces in hydrologic, weather, and climate systems

Source: NASA

Page 8: Detecting Intermittent Turbulence Using Advanced Signal Processing Techniques Christina Ho, Xiaoning Gilliam, and Sukanta Basu Texas Tech University AIAA

Atmospheric Boundary Layer (Cont…)

Original Source: Stull (1988); Courtesy: Jerome Fast

Page 9: Detecting Intermittent Turbulence Using Advanced Signal Processing Techniques Christina Ho, Xiaoning Gilliam, and Sukanta Basu Texas Tech University AIAA

Stable vs. Convective Boundary Layer (Potential Temp.)

TTU-LES: stable boundary layer

TTU-LES: convective boundary layer

Page 10: Detecting Intermittent Turbulence Using Advanced Signal Processing Techniques Christina Ho, Xiaoning Gilliam, and Sukanta Basu Texas Tech University AIAA

Flow Visualization of Boundary Layers

Turbulence-generation by mechanical shear competes with turbulence destruction by (negative) buoyancy forces

Ohya (2001)

Near-Neutral

Very Stable

Page 11: Detecting Intermittent Turbulence Using Advanced Signal Processing Techniques Christina Ho, Xiaoning Gilliam, and Sukanta Basu Texas Tech University AIAA

Nocturnal Low-Level Jets (LLJs)

Wind Speed Wind Direction

Storm et al. (2008)

Beaumont ARM Profiler

Strong wind speed and directional shear

Page 12: Detecting Intermittent Turbulence Using Advanced Signal Processing Techniques Christina Ho, Xiaoning Gilliam, and Sukanta Basu Texas Tech University AIAA

Large-Eddy Simulation of LLJs

Page 13: Detecting Intermittent Turbulence Using Advanced Signal Processing Techniques Christina Ho, Xiaoning Gilliam, and Sukanta Basu Texas Tech University AIAA

What is Intermittency?

Page 14: Detecting Intermittent Turbulence Using Advanced Signal Processing Techniques Christina Ho, Xiaoning Gilliam, and Sukanta Basu Texas Tech University AIAA

Definition of Intermittency

“The term intermittency is somewhat ambiguous in that all turbulence is considered to be intermittent to the degree that the fine scale structure occurs intermittently within larger eddies. The intermittency within a given large eddy is referred to as fine scale intermittency.

Global intermittency defines the case where eddies on all scales are missing or suppressed on a scale which is large compared to the large eddies.” (Mahrt, 1999)

- extended quiescent periods interrupted occasionally by ‘bursts’ of activity (Coulter and Doran, 2002)

Page 15: Detecting Intermittent Turbulence Using Advanced Signal Processing Techniques Christina Ho, Xiaoning Gilliam, and Sukanta Basu Texas Tech University AIAA

Causes of Turbulence Intermittency

Intermittent turbulence associated with:(i) a density current,(ii) solitary waves, and (iii) downward propagating waves from a LLJ.

Sun et al. (2002)

Page 16: Detecting Intermittent Turbulence Using Advanced Signal Processing Techniques Christina Ho, Xiaoning Gilliam, and Sukanta Basu Texas Tech University AIAA

A Multi-scale Phenomenon

Page 17: Detecting Intermittent Turbulence Using Advanced Signal Processing Techniques Christina Ho, Xiaoning Gilliam, and Sukanta Basu Texas Tech University AIAA

Outstanding Questions

What are the statistical-dynamical properties of these intermittent bursting events?

What is the statistical distribution of the on-off phases?

Is there any ‘strong’ relationship between atmospheric stability and

intermittency?

“Turbulence is normally considered to be more intermittent in very stable conditions. However, some studies have observed intermittent periods of relatively strong turbulence in less stable conditions, in contrast to background weak turbulence in very stable conditions.” (Mahrt, 1999)

Do different ‘events’ (e.g, density current vs. solitary waves) give different

intermittency signatures?

Can we numerically/synthetically generate these bursting events?

Page 18: Detecting Intermittent Turbulence Using Advanced Signal Processing Techniques Christina Ho, Xiaoning Gilliam, and Sukanta Basu Texas Tech University AIAA

Detection & Analysis of Intermittency

Page 19: Detecting Intermittent Turbulence Using Advanced Signal Processing Techniques Christina Ho, Xiaoning Gilliam, and Sukanta Basu Texas Tech University AIAA

Continuous Wavelet Transform (CWT)

Morlet Wavelet

Page 20: Detecting Intermittent Turbulence Using Advanced Signal Processing Techniques Christina Ho, Xiaoning Gilliam, and Sukanta Basu Texas Tech University AIAA

CWT of Observed and Simulated Turbulence

Observed TurbSim GP_LLJ

Page 21: Detecting Intermittent Turbulence Using Advanced Signal Processing Techniques Christina Ho, Xiaoning Gilliam, and Sukanta Basu Texas Tech University AIAA

Statistical Hypothesis Testing

In signals with a highly stochastic nature, the wavelet transform often replaces a complicated one-dimensional signal representation with an even more complex two-dimensional representation.

- we replace informal interpretation of pictures with a rigorous statistical test.

Page 22: Detecting Intermittent Turbulence Using Advanced Signal Processing Techniques Christina Ho, Xiaoning Gilliam, and Sukanta Basu Texas Tech University AIAA

Surrogate/Exemplar Analysis

Introduced by Theiler et al. (1992) for nonlinearity testing- generalizations and modifications by several others

Observed Surrogate

Page 23: Detecting Intermittent Turbulence Using Advanced Signal Processing Techniques Christina Ho, Xiaoning Gilliam, and Sukanta Basu Texas Tech University AIAA

IAAFT Algorithm (following Schreiber and Schmitz, PRL 1996)

Venema et al. (2006)

Iterative Amplitude Adjusted Fourier Transform (IAAFT) =>

identical pdf, (almost) identical spectrum (but randomized phases)

Page 24: Detecting Intermittent Turbulence Using Advanced Signal Processing Techniques Christina Ho, Xiaoning Gilliam, and Sukanta Basu Texas Tech University AIAA

Surrogate/Exemplar Analysis (Cont…)

Observed Surrogate

Page 25: Detecting Intermittent Turbulence Using Advanced Signal Processing Techniques Christina Ho, Xiaoning Gilliam, and Sukanta Basu Texas Tech University AIAA

Surrogate/Exemplar Analysis (Cont…)

Page 26: Detecting Intermittent Turbulence Using Advanced Signal Processing Techniques Christina Ho, Xiaoning Gilliam, and Sukanta Basu Texas Tech University AIAA

Intermittency Detection Framework

Original Series CWT

Surrogate Series 1

Surrogate Series 2

Surrogate Series M

CWT

CWT

CWT

max |W(b,a)| b

max |W(b,a)| b

max |W(b,a)| b

Order Statistics

T(a,)

p-value Graph

Thresholded WT

max |W(b,a)| b

Page 27: Detecting Intermittent Turbulence Using Advanced Signal Processing Techniques Christina Ho, Xiaoning Gilliam, and Sukanta Basu Texas Tech University AIAA

Intermittency Detection Framework (Cont…)

TurbSim - IECKAI TurbSim – GP_LLJ

Page 28: Detecting Intermittent Turbulence Using Advanced Signal Processing Techniques Christina Ho, Xiaoning Gilliam, and Sukanta Basu Texas Tech University AIAA

Intermittency Detection Framework (Cont…)

Observed Thresholded CWT

Generation of intermittent bursting events will require a novel nonlinear approach.

Page 29: Detecting Intermittent Turbulence Using Advanced Signal Processing Techniques Christina Ho, Xiaoning Gilliam, and Sukanta Basu Texas Tech University AIAA

Can We Fool the Intermittency Detection Framework?

AR(2) process with periodically modulated variance (Schreiber, 1998)

Page 30: Detecting Intermittent Turbulence Using Advanced Signal Processing Techniques Christina Ho, Xiaoning Gilliam, and Sukanta Basu Texas Tech University AIAA

p-Value Graph of the Modulated AR(2) Process

Page 31: Detecting Intermittent Turbulence Using Advanced Signal Processing Techniques Christina Ho, Xiaoning Gilliam, and Sukanta Basu Texas Tech University AIAA

An Existing Solution

Page 32: Detecting Intermittent Turbulence Using Advanced Signal Processing Techniques Christina Ho, Xiaoning Gilliam, and Sukanta Basu Texas Tech University AIAA

TurbSim

Kelley and Jonkman (2008)

Page 33: Detecting Intermittent Turbulence Using Advanced Signal Processing Techniques Christina Ho, Xiaoning Gilliam, and Sukanta Basu Texas Tech University AIAA

Implications for Wind Energy Research

Page 34: Detecting Intermittent Turbulence Using Advanced Signal Processing Techniques Christina Ho, Xiaoning Gilliam, and Sukanta Basu Texas Tech University AIAA

LLJ Climatology & Wind Resource

Bi-Annual Low-Level Jet Frequency and Wind Resource (Smith 2003)

Page 35: Detecting Intermittent Turbulence Using Advanced Signal Processing Techniques Christina Ho, Xiaoning Gilliam, and Sukanta Basu Texas Tech University AIAA

Modern Wind Turbines

Page 36: Detecting Intermittent Turbulence Using Advanced Signal Processing Techniques Christina Ho, Xiaoning Gilliam, and Sukanta Basu Texas Tech University AIAA

Low Level Jets during CASES-99 Field Campaign

CASES-99 Experiment (Banta et al. 2002)

Page 37: Detecting Intermittent Turbulence Using Advanced Signal Processing Techniques Christina Ho, Xiaoning Gilliam, and Sukanta Basu Texas Tech University AIAA

Coincidence?

Storm and Basu (2009); Based on Hand (2003)

Page 38: Detecting Intermittent Turbulence Using Advanced Signal Processing Techniques Christina Ho, Xiaoning Gilliam, and Sukanta Basu Texas Tech University AIAA

Recap: Neutral Flows vs. Low-level Jets

Wind profile: logarithmic (approximated by a power-law)

Nominal wind speed shear (α ~0.14)

Nominal wind directional shear

Bottom-up boundary layer (turbulence is generated near the surface)

Global-scale intermittency is absent

Wind profile: jet-type

Extreme wind speed shear (α >>0.14)

Strong wind directional shear

Bottom-up boundary layer (turbulence is generated near the surface); Upside-down boundary layer structure is also possible (turbulence is generated near the LLJ-core)

Global-scale intermittency is observed quite frequently

Neutral LLJ

Page 39: Detecting Intermittent Turbulence Using Advanced Signal Processing Techniques Christina Ho, Xiaoning Gilliam, and Sukanta Basu Texas Tech University AIAA

To be continued…

Page 40: Detecting Intermittent Turbulence Using Advanced Signal Processing Techniques Christina Ho, Xiaoning Gilliam, and Sukanta Basu Texas Tech University AIAA

On-Off Intermittency (aka Modulational Intermittency)

Toniolo et al., 2002