Turbulent flow field was modeled based on experimental flow visualization and radial-basis neural networks. Turbulent fluctuations were modeled based on the recorded concentration at various locations in the Karman vortex street, which were used as inputs and outputs of the neural network. From the measured and the modeled concentration the power spectra and spatial correlation functions were calculated. The measured and the modeled concentration power spectra correspond well to the −5/3 turbulence decay law, and exhibit the basic spectral peak of fluctuation power at the same frequency. The predicted and measured correlation functions of concentration exhibit similar behavior.

Issue Section:
Technical Papers
Keywords:
turbulence, computational fluid dynamics, radial basis function networks, flow visualisation, fluctuations, vortices, flow simulation
Topics:
Artificial neural networks, Modeling, Spectra (Spectroscopy), Turbulence, Vortex street, Visualization, Fluctuations (Physics), Flow visualization
1.
Faller
,
W. E.
, and
Schreck
,
S. J.
,
1995
, “
Real Time Prediction of Unsteady Aerodynamics, Application for Aircraft Control and Maneuverability Enhancement
,”
IEEE Trans. Neural Netw.
,
6
(
6
), pp.
1461
–
1468
.
2.
Faller
,
W. E.
, and
Schreck
,
S. J.
,
1996
, “
Neural Networks: Applications and Opportunities in Aeronautics
,”
Prog. Aerosp. Sci.
,
32
(
5
), pp.
433
–
456
.
3.
Jacobson
,
S. A.
, and
Reynolds
,
W. C.
,
1998
, “
Active Control of Streamwise Vortices and Streaks in Boundary Layers
,”
J. Fluid Mech.
,
360
, pp.
179
–
211
.
4.
Jambunathan
,
K.
,
Fontama
,
V. N.
,
Hartle
,
S. L.
, and
Ashford-Frost
,
S.
,
1997
, “
Using ART2 Networks to Deduce Flow Velocities
,”
Artif. Intell. Eng.
,
11
, pp.
135
–
141
.
5.
Grant
,
I.
, and
Pan
,
X.
,
1997
, “
The Use of Neural Techniques in PIV and PTV
,”
Meas. Sci. Technol.
,
8
, pp.
1399
–
1405
.
6.
Kimura, I., Hattori, A., Kuroe, Y., and Kaga, A., 1998, “Estimation of Flow Velocity Vector Fields Using Neural Networks,” 8th International Symposium on Flow Visualization, Sorrento, pp. 151.1–151.7.
7.
Dibike
,
Y. B.
,
Minns
,
A. W.
, and
Abbot
,
M. B.
,
1999
, “
Applications of Artificial Neural Networks to the Generation of Wave Equations from Hydraulic Data
,”
J. Hydraul. Res.
,
37
(
1
), pp.
81
–
97
.
8.
Blackwelder R., 1995, “A Preliminary Attempt to Use Neural Networks for Turbulent Eddy Classification,” Anual Research Briefs, Center for turbulence research, Stanford University.
9.
Ferre-Gine
,
J.
,
Rallo
,
R.
,
Arenas
,
A.
, and
Giralt
,
F.
,
1997
, “
Extraction of Structures from Turbulent Signals
,”
Artif. Intell. Eng.
,
11
(
4
), pp.
413
–
419
.
10.
Delgado, A., Benning, R., and Becker, T., 1998, “On the Use of ANN in the Prediction of the Flow Fields,” EUFIT 98, pp. 7–10.
11.
Zhang
,
L.
,
Akiyama
,
M.
,
Sugiyama
,
H.
, and
Ninomiya
,
N.
,
1997
, “
Application of Structured Artificial Neural Networks to Computational Fluid Dynamical Problems
,”
Japanese Journal of Fuzzy Theory and Systems
,
9
, pp.
511
–
520
.
12.
Giralt
,
F.
,
Arenas
,
A.
,
Ferre-Gine
,
J.
,
Rallo
,
R.
, and
Kopp
,
G. A.
,
2000
, “
The Simulation and Interpretation of Free Turbulence with a Cognitive Neural System
,”
Phys. Fluids
,
12
(
7
), pp.
1862
–
1869
.
13.
Rediniotis
,
O. K.
, and
Chrisanthakopoulos
,
G.
,
1998
, “
A Wide-Range, High-Accuracy Neural/Fuzzy Calibration Technique for Flow-Diagnostics Instrumentation
,”
ASME J. Fluids Eng.
,
120, 1
, pp.
95
–
101
.
14.
Fan
,
H. Y.
,
Lu
,
W. Z.
,
Xi
,
G.
, and
Wang
,
S. J.
,
2003
, “
A New Neural-Network-Based Calibration Method for Aerodynamic Pressure Probes
,”
ASME J. Fluids Eng.
,
125, 1
, pp.
113
–
120
.
15.
Gad-el-Hak
,
M.
,
1996
, “
Modern Developments in Flow Control
,”
Appl. Mech. Rev.
,
49
, pp.
365
–
379
.
16.
Lee
,
C.
,
Kim
,
J.
,
Babcock
,
D.
, and
Goodman
,
R.
,
1997
, “
Application of Neural Networks to Turbulence Control for Drag Reduction
,”
Phys. Fluids
,
9
(
6
), pp.
1740
–
1747
.
17.
Gillies
,
E. A.
,
1998
, “
Low Dimensional Control of the Circular Cylinder Wake
,”
J. Fluid Mech.
,
371
, pp.
157
–
180
.
18.
McComb, W. D., 1996, The Physics of Fluid Turbulence, Clarendon Press, Oxford, UK.
19.
Batchelor, G. K., 1971, The Theory of Homogeneous Turbulence, Cambridge University Press, Cambridge, UK.
20.
Hinze, J. O., 1975, Turbulence: An introduction to its mechanism and theory, second edition, McGraw-Hill book company, New York, USA.
21.
Grabec, I., and Sachse, W., 1997, Synergetics of Measurement, Prediction and Control, Springer, Berlin, Germany.
22.
Simon
,
D.
,
2002
, “
Training Radial Basis Neural Networks with the Extended Kalman Filter
,”
Neurocomputing
,
48
, pp.
455
–
475
.
23.
Kim
,
H. S.
, and
Lee
,
J. Y.
,
2002
, “
Image Coding by Fitting RBF-Surfaces to Subimages
,”
Pattern Recogn. Lett.
,
23
, pp.
1239
–
1251
.
24.
Orr, M., 1999, “Matlab Functions for Radial Basis Function Networks,” http://www.anc.ed.ac.uk/∼mjo/software/rbf2.zip
25.
Gerdes
,
F.
, and
Olivari
,
D.
,
1999
, “
Analysis of Pollutant Dispersion in an Urban Street Canyon
,”
Eng. Educ.
,
81
(
1
), pp.
105
–
124
.
26.
Simoens
,
S.
, and
Ayrault
,
M.
,
1994
, “
Concentration Flux Measurements of a Scalar Quantity in Turbulent Flows
,”
Exp. Fluids
,
16
, pp.
273
–
28
.
27.
Aider, J. L., Westfried, and J. E., 1995, “Visualization and PDF of the Fluctuations of a Passive Scalar in a Turbulent Go¨rtler Flow,” FED, Experimental and numerical flow visualization, ASME 218, pp. 123–130.
28.
Grisch
,
F.
, and
Bresson
,
A.
,
2001
, “
Radical Imaging and Temperature Mapping in Turbulent Gaseous Flowfields
,”
C. R. Acad. Sci., Ser IV: Phys., Astrophys.
,
2
(
7
), pp.
1037
–
1047
.
29.
Chatzipanagiotidis
,
A.
, and
Olivari
,
D.
,
1996
, “
Pollutant Dispersal Downstream of a Hill in Different Wind Conditions
,”
J. Wind. Eng. Ind. Aerodyn.
,
64
(
2-3
), pp.
233
–
248
.
30.
Law
,
A. W. K.
, and
Wang
,
H.
,
2000
, “
Measurement of Mixing Processes with Combined Digital Particle Image Velocimetry and Planar Laser Induced Fluorescence
,”
Exp. Therm. Fluid Sci.
,
22
, pp.
213
–
229
.
31.
Balu
,
M.
,
Balachandar
,
R.
, and
Wood
,
H.
,
2001
, “
Concentration Estimation in Two-Dimensional Bluff Body Wakes Using Image Processing and Neural Networks
,”
J. Fluids Struct.
,
8
(
2-3
), pp.
121
–
140
.
32.
Urner, P., 1980, Beitrag zum Einfluß der Stro¨mung auf das Meßsignal bei der Wirbelfrequenz-Durchflußmessung, Ph.D. Thesis, TU Dresden, Fakulta¨t fu¨r Maschinenwesen.
33.
Anagnostopoulos
,
P.
,
1997
, “
Computer Aided Flow Visualization and Vorticity Balance in the Laminar Wake of a Circular Cylinder
,”
J. Fluids Struct.
,
11
, pp.
33
–
72
.
34.
Landau, L. D., and Lifshitz, E. M., 1987, Fluid Mechanics, Pergamon, Oxford, UK.
35.
Keles
,
R. S.
,
2000
, “
Active Control of Transition to Turbulence in the Wake of a Cylinder
,”
J. Aircr.
,
3
, pp.
1
–
15
.
36.
Spalart
,
P. R.
,
2000
, “
Strategies for Turbulence Modelling and Simulations
,”
Int. J. Heat Fluid Flow
,
21
, pp.
252
–
263
.
37.
Grabec
,
I.
,
2001
, “
Experimental Modeling of Physical Laws
,”
Eur. Phys. J. B
,
22
, pp.
129
–
135
.
Copyright © 2004
 by ASME
You do not currently have access to this content.