turbulence spectra and cospectra measured during fire front passage daisuke seto, craig b. clements,...

Turbulence Spectra and Cospectra Measured during Fire Front Passage

Daisuke Seto, Craig B. Clements, and Fred SnivelyDaisuke Seto, Craig B. Clements, and Fred SnivelyDepartment of Meteorology and Climate ScienceDepartment of Meteorology and Climate Science

San José State UniversitySan José State UniversitySan José, CASan José, CA

Warren E. Heilman Warren E. Heilman Northern Research StationNorthern Research Station

USDA Forest Service USDA Forest Service East Lansing, MIEast Lansing, MI

San José State UniversityFire Weather Research Laboratory

Overview of Presentation

• Background

• Experimental datasets

• Data processing

• Results– Velocity and temperature spectra– Momentum and heat flux cospectra

• Preliminary Conclusion

• Future WorkSan José State University

Fire Weather Research Laboratory

Background and Motivation

• Fire-atmosphere coupling occurs over spatial scales from tens of meters to kilometers.

→spectral analysis of in-situ turbulence data allows for the general description of turbulence structure over frequency domain.

• Fire spread rate predictions would be improved by accounting for the effect of turbulence (Albini 1983; Sun et al. 2009).

• Spectral analysis is used for – parameterizing eddy diffusivities – estimating dispersion coefficients

• Validity of surface layer similarity theory must be questioned when used for wildland fire applications.

San José State UniversityFire Weather Research Laboratory

Experimental Datasets

San José State UniversityFire Weather Research Laboratory

Grass fire in valley (CA)-head fire

Grass fire on slope (CA)-head fire

Sub-canopy burn (NC)-backing fire

slash burn (Finland)-backing fire

10:30 11:00 11:30 12:00 12:30 13:00 13:30 14:00 14:3020

30

40

50

60

70

80

90

100

110

Ts

Data Processing• Wind velocity and temperature (10Hz): ATI Sx-probe

– U: mean wind direction– V: lateral wind direction– W: tilt corrected vertical velocity– Ts: sonic temperature

• Define Pre-, During-, Post-Fire Front Passage (FFP)• Spectra and cospectra were calculated every 30 min,

using Fast Fourier Transform (FFT) algorithm before smoothing and averaging.

San José State UniversityFire Weather Research Laboratory 0.001 0.01 0.1 1 10

0.001

0.01

0.1

1

10

f = frequency

fS(f

)

rawsmoothed

Post-FFP

During-FFP

Pre-FFP

Tem

pera

ture

(°C

)

Result: Grass Fire in Valley

San José State UniversityFire Weather Research Laboratory

z = 6.7 m

Upre = 2.27 m/s

UFFP = 3.33 m/s

Upost = 3.67 m/s

Grass fire in valley (CA)

Spectra: Grass Fire in ValleyU

V

W

Ts

f = frequency

f S

(f)

-2/3

Grass Fire on Slope

San José State UniversityFire Weather Research Laboratory

z = 11 m

Upre = 6.23 m/s

UFFP = 7.03 m/s

Upost = No data

Grass fire on slope (CA)

Spectra: Grass Fire on Slopef

S(f

)U W

V Ts

f = frequency

-2/3

Sub-canopy

San José State UniversityFire Weather Research Laboratory

z = 3 m

Upre = 1.04 m/s

UFFP = 1.77 m/s

Upost = 1.08 m/s

Sub-canopy (NC)

Spectra: Sub-canopyf

S(f

)U W

V Ts

f = frequency

-2/3

Spectra: Sub-Canopy

Slash Burn

San José State UniversityFire Weather Research Laboratory

z = 11 m

Upre = 1.43 m/s

UFFP = 2.76 m/s

Upost = No data

slash burn (Finland)

Spectra: Slash Burnf

S(f

)U W

V Ts

f = frequency

-2/3

Normalized Spectral Density: Pre- and Post-FFP

n = fz/U

f S

(f)/

T*2

U W

V Ts

Stability class

f S

(f)/

u *2

Normalized Spectral Density: During-FFPf

S(f

)/u *2

n = fz/U

U W

V Ts

f S

(f)/

T*2

Stability class

Normalized momentum and heat flux cospectra-f

Cuw

(f)/

u *2

-f C

wT(f

)/u *

T*

n = fz/U

Momentum flux

Heat flux

Pre- and Post-FFP

Pre- and Post-FFP

During-FFP

During-FFP

Summary

• Unique velocity and temperature spectra were observed in each burn during FFP.

• Increases in velocity spectra may be related to the degree of coupling between fire and atmosphere.

• Increased temperature spectra was observed over entire frequency range in all cases.

• Surface layer similarity theory is valid for Pre- and Post-FFP velocity and temperature spectra.

• Normalized velocity spectra during FFP did not collapse into one curve. However, overall slope was conserved at higher frequencies.

• One universal behavior observed during FFP in all cases was a slower roll-off at both low and high frequencies in the normalized spectral curves.

• This is due to increased spectral density at all frequencies.

• Ambient turbulence is strongly affected by the fire front

San José State UniversityFire Weather Research Laboratory

Future Work

• Find empirical formula for velocity, temperature, and turbulence dissipation rate during FFP.

• Compare Ts spectral characteristics with measured total and radiative heat flux spectra.

San José State UniversityFire Weather Research Laboratory

Acknowledgements

• Dr. Tara Strand

• Grant # JFSP

• This research is also supported by Joint Venture Research Agreement from the USDA Northern Research Station #07-JV-11242300-073.

San José State UniversityFire Weather Research Laboratory