Data of Ch5 --Dual rotor
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* Results
*Conclusion*
- little effect of rear rotor on Cp_1
- Cp1 is independent of TI
** TI effect on single-rotor, front,
| cp | ct | TI | TSR |
| | | 1 | |
| | | 15 | |
** Dual rotor X=4D, TI 15% -- CFD-RANS
# TI 15%, RANS results
# TSR1 TSR2/TSR1 TSR2 Cp_1 Ct_1 Cp_2 Ct_2
5.0 0.730 3.65 0.396 0.824 -0.024 0.289
5.0 0.600 3.00 0.397 0.829 0.010 0.284
5.0 0.500 2.50 0.395 0.826 0.005 0.265
TSR1 TSR2/TSR1 TSR2 Cp_1 Ct_1 Cp_2 Ct_2
5 0.733 3.665 0.394 0.82 -0.044 0.229
5 0.644 3.22 0.393 0.82 -0.02 0.218
5 0.55 2.75 0.394 0.819 -0.005 0.21
5 0.5 2.5 0.395 0.82 0.002 0.184
5 0.45 2.25 0.396 0.821 0.000 0.168
5 0.4 2 0.396 0.821 -0.004 0.153
5 0.35 1.75 0.395 0.821 -0.006 0.143
5 0.2 1 0.396 0.822 -0.004 0.09
** Dual X=4D same TSR -- BEM + Park model
# ak, distance (norm by D)= 3.99999991E-02 4.00000000
# TSR1, C_T_tot, C_P_tot, omega2/omega1
1.000000E+00 2.862664E-01 2.394140E-02 9.567473E-01
1.250000E+00 3.237689E-01 4.581533E-02 9.503006E-01
1.500000E+00 3.736952E-01 7.827624E-02 9.411572E-01
1.750000E+00 4.390565E-01 1.231780E-01 9.281210E-01
2.000000E+00 5.195406E-01 1.788598E-01 9.113323E-01
2.250000E+00 6.136195E-01 2.441946E-01 8.893722E-01
2.500000E+00 7.156332E-01 3.136399E-01 8.634881E-01
2.750000E+00 8.213180E-01 3.814463E-01 8.335000E-01
3.000000E+00 9.222423E-01 4.392351E-01 8.015752E-01
3.250000E+00 1.001631E+00 4.755293E-01 7.784010E-01
3.500000E+00 1.065189E+00 4.984458E-01 7.609080E-01
3.750000E+00 1.116553E+00 5.122202E-01 7.491560E-01
4.000000E+00 1.160889E+00 5.199742E-01 7.404510E-01
4.250000E+00 1.196960E+00 5.233117E-01 7.338645E-01
4.500000E+00 1.229946E+00 5.234426E-01 7.281728E-01
4.750000E+00 1.261175E+00 5.211463E-01 7.232024E-01
5.000000E+00 1.291180E+00 5.169932E-01 7.189118E-01
5.250000E+00 1.313614E+00 5.118050E-01 7.153068E-01
5.500000E+00 1.339595E+00 5.045193E-01 7.123726E-01
5.750000E+00 1.359179E+00 4.970232E-01 7.101494E-01
6.000000E+00 1.381723E+00 4.874390E-01 7.084185E-01
6.250000E+00 1.399795E+00 4.772219E-01 7.073638E-01
** DONE Cp one Rear Rotor at Re 1e6 - R=0.6
*Flow Features:*
keywords:
largely stalled)
High Angle of Attack, naca0012, stall,
Goal: performance when naca0012 is stalled
C:\Users\kaiming\Documents\ZJU\naca0012_Dual_Rotor\OneRotor_Rear_1M\tsr4
| TSR | Cp | Ct | Re | U(m/s) | omega(rad/s) | turbulence models |
| 4 | -0.011 | | 1e6 | 4.4 | 77.22 | standard k-e |
| 4 | 0.05 | | 1e6 | 4.4 | 77.22 | sst ko |
| 4.5 | - 0.013 | | 1e6 | 4.4 | 86.87 | |
OneRotor_Rear_1M/rear_st_tsr4_ke_7k.dat.gz
** Wake
*** TKE
refernces:
N Stergiannis CFD modelling approaches against single wind turbine wake measurements using RANS
*** velocity contour in the wake
fig.9 mycek
** wake width measurement in CFD?
iso-surface plot, set variable as: U_x
** Mean axial velocity from CFD at a given X/D?
- wake is normal distribution, gaussian
? how to get the mean of normal distribution?
- arear averaged axial mean veolocity of wake (Mycek 2014)
+ (rotor radius,R)
reference:
#+CAPTION:area-averaged velocity (disc diameter=1D) (fig.8b mycek 2014 dual rotor)
file:figures/post/disc_averaged_axial_velocity_mycek_2014.png
Area used in my case:
circular, r=1.2R (radius of turbine)
How to define the edge of of wake in CFD post processing at different X/D?
how to define the edge of wake?
U_x = 0.99U_\infty
how to define the "mean" U_x in the wake?
? is r=1R used by mycek right?
*** One Rotor Front, Eldad Blade TSR 5 TI = 1%
# One Rotor, front, eldad blade
# TSR 5, TI =1%, \theta_T = 2 deg
#X/D X half width, Ux U Ux/U
1 0.46 0.288 0.332 0.6 0.553333333
2 0.92 0.299 0.326 0.6 0.543333333
3 1.38 0.305 0.337 0.6 0.561666667
4 1.84 0.311 0.354 0.6 0.59
5 2.3 0.318 0.374 0.6 0.623333333
6 2.76 0.326 0.394 0.6 0.656666667
7 3.22 0.332 0.409 0.6 0.681666667
8 3.68 0.341 0.437 0.6 0.728333333
9 4.14 0.35 0.457 0.6 0.761666667
10 4.59 0.352 0.464 0.6 0.773333333
*** How to the area average velocity of wake at a given X/D?
1. cacluation wake width (b) at a given X/D
create a iso-surface plot with U_o,
2. get area average in CFDpost
+ create an expression in CFD post
~areaAve(Velocity in Stn Frame w)@areaAverage~
3. change X=2D...
*** *Turbulence kinetic energy*
3e-5, 1e-2
Number of contours, 51
Velocity
0.02-0.6
Number of contours, 31
** 3D streamline
what does 3D streamline means
** k correction
calibration
| TI (%) | k | RMS Error |
| 15 | 0.0190 | 0.0190 |
| 1 | 0.0075 | 0.0371 |
*** Bayesian Calibration
- based on exprimental data: overall power
(Rathmann 2017)
variables: hub-height wind speed, wind direction
math function: probability density function
reommmended k value: 0.06 offshore and 0.09 onshore
- Rathmann, Ole Steen, et al. "Validation of the Revised WAsP Park Model." WindEurope 2017. 2017.
- Rathmann O., Estimation of the Wake Expansion Coefficient from Eddy Diffusivity Theory. Research note, DTU Wind Energy. (2017).
- M.C. Kennedy, A. O’Hagan. Bayesian calibration of computer models. Journal of the Royal
Statistical Society: Series B (Statistical Methodology), 63(3), 425-464. (2001).
- Murcia, J.P. et al., Uncertainty quantification in wind farm flow models. PhD thesis, DTU Wind
Energy (2017).
- Murcia, J.P. et al., Wake Model Calibration Based On SCADA Data Considering Uncertainty In The
Inflow Conditions. Private communication (2017).
*** k vs TI
k= 0.4 TI [fn:goccmen2016wind]
k=0.04 when TI=10%
k=0.4 TI_h
- TI_h : hub height TI
k=0.4TI = 0.038 at the Sexbierum wind fams [fn:pena2016application]
[fn:pena2016application] Peña, Alfredo, Pierre‐Elouan Réthoré, and M. Paul van der Laan. "On the application of the Jensen wake model using a turbulence‐dependent wake decay coefficient: the Sexbierum case." Wind Energy 19.4 (2016): 763-776.
[fn:goccmen2016wind] Göçmen, Tuhfe, et al. "Wind turbine wake models developed at the technical university of Denmark: A review." Renewable and Sustainable Energy Reviews 60 (2016): 752-769.
*** Pyakurel‘s method
- based on CFD data: centre line axial mean velocity
- Eq (10) in Pyakurel 2017
- *observed* axial velocity, U_s = *centre line* velocity from CFD RANS (this value is used as experimental data)
- Predicted axial velocity, U_c = Jensen model in which Ct is also from CFD RANS
Root mean square error = (U_s - U_c )_rms
# limit
centre line veolocity is lower than the area averaged velocity, thus low centre line velocity as baseline, k is not accurate
** Jump value of moment time history of dual rotor
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