Detecting Intermittent Turbulence Using Advanced Signal Processing Techniques

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

2. Motivation

3. Wind Turbine Inflow Generation t = T t = 0 TurbSim User’s Guide

4. Wind Turbine Inflow Generation: IEC Spectral Models Kaimal’s Spectral Model (neutral boundary layer) Several other models: e.g., Mann’s Uniform Shear Model

5. Nighttime (Intermittent) Turbulence Observation (stable boundary layer) Over the US Great Plains, intermittent turbulence frequently occurs in the presence of nocturnal low-level jets. CASES-99, Poulos et al. (2002)

6. Background

7. The Atmospheric Boundary Layer (ABL) ABL (~ 1km) Source: NASA • Turbulent fluxes of heat, momentum, and moisture are driving forces in hydrologic, weather, and climate systems

8. Atmospheric Boundary Layer (Cont…) Original Source: Stull (1988); Courtesy: Jerome Fast

9. Stable vs. Convective Boundary Layer (Potential Temp.) TTU-LES: stable boundary layer TTU-LES: convective boundary layer

10. Flow Visualization of Boundary Layers Turbulence-generation by mechanical shear competes with turbulence destruction by (negative) buoyancy forces Near-Neutral Very Stable Ohya (2001)

11. Nocturnal Low-Level Jets (LLJs) Beaumont ARM Profiler Wind Speed Wind Direction Strong wind speed and directional shear Storm et al. (2008)

12. Large-Eddy Simulation of LLJs

13. What is Intermittency?

14. 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)

15. 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)

16. A Multi-scale Phenomenon

17. 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?

18. Detection & Analysis of Intermittency

19. Continuous Wavelet Transform (CWT) Morlet Wavelet

20. CWT of Observed and Simulated Turbulence Observed TurbSim GP_LLJ

21. 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.

22. Surrogate/Exemplar Analysis • Introduced by Theiler et al. (1992) for nonlinearity testing - generalizations and modifications by several others Observed Surrogate

23. IAAFT Algorithm (following Schreiber and Schmitz, PRL 1996) Iterative Amplitude Adjusted Fourier Transform (IAAFT) => identical pdf, (almost) identical spectrum (but randomized phases) Venema et al. (2006)

24. Surrogate/Exemplar Analysis (Cont…) Observed Surrogate

25. Surrogate/Exemplar Analysis (Cont…)

26. Thresholded WT Original Series CWT max |W(b,a)| b p-value Graph max |W(b,a)| b Surrogate Series 1 CWT T(a,) max |W(b,a)| b Surrogate Series 2 CWT Order Statistics max |W(b,a)| b Surrogate Series M CWT Intermittency Detection Framework

27. Intermittency Detection Framework (Cont…) TurbSim - IECKAI TurbSim – GP_LLJ

28. Intermittency Detection Framework (Cont…) Thresholded CWT Observed Generation of intermittent bursting events will require a novel nonlinear approach.

29. Can We Fool the Intermittency Detection Framework? AR(2) process with periodically modulated variance (Schreiber, 1998)

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

31. An Existing Solution

32. TurbSim Kelley and Jonkman (2008)

33. Implications for Wind Energy Research

34. LLJ Climatology & Wind Resource Bi-Annual Low-Level Jet Frequency and Wind Resource (Smith 2003)

35. Modern Wind Turbines

36. Low Level Jets during CASES-99 Field Campaign CASES-99 Experiment (Banta et al. 2002)

37. Coincidence? Storm and Basu (2009); Based on Hand (2003)

38. Recap: Neutral Flows vs. Low-level Jets Neutral LLJ • 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

39. To be continued…

40. On-Off Intermittency (aka Modulational Intermittency) Toniolo et al., 2002