Detecting Intermittent Turbulence Using Advanced Signal Processing Techniques Christina Ho, Xiaoning...

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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