the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
An analytical formulation for turbulent kinetic energy added by wind turbines based on large-eddy simulation
Abstract. Wind turbine wakes are plume-like regions characterized by reduced wind speed and enhanced turbulent kinetic energy (TKE) that form downstream of wind turbines. Numerical mesoscale models, like the Weather Research and Forecast (WRF) model, are generally effective at reproducing the wind speed deficit, but lack skills at simulating the TKE added by wind turbines. Here we propose an analytical formulation for added TKE by a wind turbine that reproduces, via least-square error parameter fitting, the main features of the three-dimensional structure of added TKE as simulated in previous large-eddy simulation (LES) studies, including: a streamwise peak at x = 4–6D (where D is the turbine diameter), a vertical peak near the upper rotor region, and an annular Gaussian-like distribution along the rotor edge. Validation of the proposed formulation against independent LES results and wind tunnel observations from the literature indicates a promising performance in the case of a single wind turbine wake. The ultimate goal is to insert the proposed formulation, after further improvements, in the WRF model for use within existing or new wind farm parameterizations.
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Status: open (until 13 Dec 2024)
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RC1: 'Comment on wes-2024-128', Anonymous Referee #1, 15 Nov 2024
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The paper presents a new analytical formulation for turbulent kinetic energy in the wake of a single wind turbine. The model proposes a detailed three-dimensional description of the tke field, intended to remain valid in both the near and far wake regions. In total, 15 parameters are introduced and calibrated against the results of large-eddy simulations through a two-step least-squares method. The paper is clear, easy to read, and has the potential to contribute to the improvement of the latest wake models.
Major comments
- Line 189, 203: For both Weibull-like laws, the shape parameters are set before starting the fitting process. The authors should motivate the choice of the values kA=2 and kW=4, and clarify why those parameters are not fitted using the two-step least-squares method.
- Line 274: The authors claim that kr is independent of CT. However, Figure 2b shows differences up to 20% between the value of kr at CT=0.4 and at CT=0.9. It would perhaps be interesting to consider an expression other than CT^b for the fitting.
- Figure 2: Because the form of Eq. 15 cannot capture the stability conditions, the differences between the “direct fit” values and that of the functional relationships are often large in the stable and unstable cases. For consistency, shouldn’t only neutral conditions be used for calibration?
- Line 305: The authors should clarify what they mean by “entire wake regions” and specify the limits of the regions along y and z as well, as this will influence the value of the RMSE.
Minor comments
- Line 6: The notation x = 4 – 6D appears a bit confusing. It might be worth considering an alternative notation.
- P2: The introduction is rich and well-documented. However, the relevance of the section between lines 28 and 46 is questionable in the scope of this work as it addresses velocity deficit models.
- Line 76: “We note that also Eq. 1 and 2 can be reduced to the same form”. This is not true for CT in the case of Eq. 2. The authors should maybe re-phrase this sentence for consistency.
- Line 133: Typo “u, v, andw”
- Line 160: Different definitions of $\Delta TI$ exist in the literature. It is worth clarifying which one is used and its connection to $\Delta TKE$.
- Line 189: The expression selected for A(x) is very similar to the one proposed by T Delvaux et al2024 Phys.: Conf. Ser. 2767 092089. The authors could consider providing additional reference for it.
- Line 194: Missing space after the bracket.
- Line 277, 279: Missing space before the bracket.
- Line 351: Typo “citeWuetal2023”
- 3.2: The proposed model appears to outperform the model of Ishara and Qian (2018) in most of the validation cases. The comparison could be further enriched with the 3D model of Tian et al. (2022).
Citation: https://doi.org/10.5194/wes-2024-128-RC1
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