Twin Cities Campus Saint Anthony Falls Laboratory Engineering, Environmental, Biological and Geophysical Fluid Dynamics College of Science and Engineering Mississippi River at 3 rd Avenue S.E. Minneapolis, MN 55414 Dept. Main Office: 612-624- 4363 Fax: 612-624-4398 Project Title: Development of a High-Resolution Virtual Wind Simulator for Optimal Design of Wind Energy Projects Contract Number: RD3-42 Milestone Number: 5 Report Date: 10/12/11 Principal Investigator: Fotis Sotiropoulos Contract Contact: Bridget Foss fotis@umn.edu 612-624-5571 Congressional District: (Corporate office) Minnesota 5 th Congressional District: (Project location) Minnesota 5 th MILESTONE REPORT Executive Summary: This project aims at developing a Virtual Wind Simulator (VWS) for the prediction of atmospheric boundary layer flow and its interactions with wind turbines and wind farms. The use of the simulator will assist in the improved design of potential wind energy projects by providing more accurate predictions of local and wind turbulence at site and turbine levels. Additionally, the VWS will help increase the level of wind energy utilization and reduce the cost of energy production. Computational Fluid Dynamics (CFD) methods are used in this project to develop a computational framework for conducting high-resolution simulations of wind turbulence at the meso and micro scales. In particular, the Large-Eddy Simulation (LES) technique will allow for accurate simulations of the turbulent flow at spatial resolutions as small as one to ten meters, and temporal resolutions of just a few seconds. Parameterizations for wind turbine forces will also be developed in the LES framework. In addition, three-dimensional, time-evolving flow fields obtained from LES at any location within a potential wind farm site could then be used as the inflow condition for even more detailed simulations of the turbulent flow around the blades of specific wind turbines using a hybrid Reynolds-Averaged Navier-Stokes (RANS)/LES technique. The SAFL computational models will be coupled to macro-scale regional models to develop a powerful multi-scale computational tool, the VWS. The VWS will integrate the latest advancements in computational fluid dynamics research and provide reliable, high-resolution descriptions of wind turbulence across the entire range of scales that are relevant to wind power production. This information will provide objective, scientifically based criteria that can be used by wind energy project developers for the site-specific, optimal selection and placement (micrositing) of wind turbines. 1
As planned, during this reporting period (quarter) activities have been carried out that address the following objectives: (a) Development of a multi-scale computational fluid dynamics (CFD) framework for accurate simulation of high-resolution wind and turbulence fields and their effects on wind turbine operation and energy output. (b) Validation of the proposed Virtual Wind Simulator using high-resolution wind and turbulence measurements collected in an atmospheric boundary layer wind tunnel. (c) Systematic measurements in the wind tunnel considering fully developed model wind farms with different layouts (turbine spacing of 6, 8, 10 and 12 rotor diameters) were performed to test our Virtual Wind Simulator. Fundamental physics is being extracted from both CFD and experiments. Project funding provided by customers of Xcel Energy through a grant from the Renewable Development Fund. Technical Progress: We have made substantial progress in Task 1, Task 2 and Task 4. The progress made in the three tasks is discussed below. Task 1. Development of the Virtual Wind Simulator for high-resolution simulations of wind, turbulence and their effect on energy production Progress has continued on the development and testing of the Virtual Wind Simulator. Our current efforts are focused on the aligned wind farm performance under different spacing by using the large-eddy simulation with actuator disk model. In actuator disk model, the rotor is represented by uniformly distributed body forces on a disk. Hence, the Navier-Stokes equation with a body force for rotor model reads as, u i t + u iu j = p + (ν + ν x j x i x t ) u i + f j x i. j The total force of acting on the flow from a wind turbine in the streamwise direction is calculated using the following expression F t = 1 2 ρc T U d 2 π 4 D2 where C T = C T, U (1 a) 2 d = (1 a)u is the velocity on the disc, and D is the diameter of the disk. The a is the axial induction factor and C T = 4a(1 a) which is from the onedimensional momentum theory. In general the disk grids don t coincide with background grids. Discrete delta functions are used to transfer quantities between the disk grids (g t ) and background grids (g b ) as shown below U d (X) = uδ h (x X) V(x), x g b f i (x) = F i δ h (x X) A(X) X g t 2
where δ h (x X) is the discrete delta function, V(x) is the volume of background grid and A(X) is the area of the disk surface grid. The discrete delta function used in the present work can guarantee the total force and total torque applied on the background grids are the same as that on the disk. In this part, we present the results from different streamwise spacing S x and spanwise spacings S y. Some cases have the same spanwise spacing S y = 4D, and the other cases are with the same streamwise spacing S x = 7D. For all the cases, the Reynolds number based on the bulk velocity and the height of the boundary layer is 1 10 5. The axial induction factor a equals 0.25. Fig. 1 shows the mean velocity profiles with different streamwise spacing and spanwise spacing. We can see that the double log laws exist beneath the wind turbine region and beyond the wind turbine region for all the cases. Fig. 2 shows the normalized extracted power density for different spacing. The extracted power density is defined as P d = 1 N t P l N t l=1 where P S x S l is the extracted power of the l th y turbine. From this figure, it can be seen that the maximum extracted power density is larger when we increase the streamwise spacing or the spanwise spacing. And it can be seen that it is more efficient when we increase the streamwise spacing. Fig. 3 shows the disk-averaged root-mean-square (RMS) velocity fluctuations on the disk which are nondimensionallized by the averaged streamwise velocity on the disk U d. This figure shows that the RMS of velocity fluctuations decreases quickly when we increase the streamwise spacings. The RMS of velocity fluctuations decreases when we increase S y from 2D to 4D but it doesn't change very much if we further increase the spanwise spacing. In all, we find that the extracted power can be increased by increasing both of the streamwise spacing and spanwise spacing, but it is more efficient to increase the streamwise spacing. The extracted power densities are larger for the layouts which have the larger streamwise spacing at the same occupied area. For the turbulence intensities on the disc, the increases in the streamwise spacing can reduce them effectively and the effects are minor when we increase the spanwise spacing. 3
Fig. 1. Mean velocity profiles for different spacing. Left: different streamwise spacing with fixed spanwise spacing S y = 4D; Right: different spanwise spacing with fixed streamwise spacing S x = 7D Fig. 2. Extracted power density for different streamwise and spanwise spacing. 4
Fig. 3 Averaged root-mean-square velocity fluctuations on the disc normalized by the disc-averaging streamwise velocity U d. Task 2. Validation of the Virtual Wind Simulator using wind tunnel measurements Wind tunnel experiments Progress has continued on the fundamental understanding of the turbulent flow properties around wind turbines. In this quarter we have focused on the flow characteristics in a fully developed wind farm. Wind tunnel experiment has been performed to quantify the turbulent processes occurring in large wind farms with different layouts (turbine spacing of 6, 8, 10 and 12 rotor diameters). Experiments were performed in the Reynolds numbers independence regime (Chamorro, Arndt and Sotiropoulos, 2011). These experiments will also help us to test and validate our high resolution numerical model. Figures 4 and 5 show the set-up and some of the results for the wind farm with turbine separation of 6 rotor diameters. 5
Fig. 5. Wind tunnel set-up for a large wind farm. Fig. 6. Flow measurements at different locations behind the n-th row of wind turbines in a large wind farm. Left: mean velocity. Right: similar plot in log scale to stress the development of a log region above the wind turbines as predicted by the CFD simulations (Fig. 1). (Uo is the freestream velocity). We are also investigating the dynamics of the tip vortices shed by the turbine blades. Wind tunnel experiments are being carried out using a 60 cm model wind turbine (see Figures 7, 8 and 9). Understanding the stability and strength of these structures is essential to determine the turbulent loads present in the wake of wind turbines. These loads define the structural design of wind turbines placed in the wake of others. 6
Fig. 7. 60-cm model wind turbine (left); and model wind turbine in the free stream flow of the wind tunnel (right). Fig. 8. Qualitative PIV measurements right behind the 60-cm model wind turbine around the top tip height (5cm above and 5 cm below tip): Turbulence intensity around the top tip vortex. Windows size is (z=10cm)*(x=10cm). 7
Fig. 9. Qualitative PIV measurements right behind the 60-cm model wind turbine around the top tip height (5cm above and 5 cm below tip): Mean velocity (top and center); turbulence intensity (bottom). Windows size is (z=10cm)*(x=135cm). Task 4. Virtual wind simulator Application (Wind Assessment at Undeveloped Site with Complex Terrain) Field experiments Measurements continued in collaboration with our partners from WindLogics, Inc. and Barr Engineering. WindLogics finished collecting measurements from the sodar atop the bluff near Prairie Island. These activities fall under Task 4, subtask 4.1 in the original proposal. Barr continued to monitor and QA/QC data from equipment deployed at the complex terrain site at Prairie Island, near Red Wing, Minnesota. The equipment is deployed on property owned by the Prairie Island Indian Community, at sites selected in cooperation with the PI Indian Community. The following data were collected at the Prairie Island complex terrain site: Conventional anemometry data from a 50-meter meteorological tower at the Prairie Island Casino Site; Three-dimensional data from two CSAT3 sonic anemometers that are installed on the same 50-meter tower as the conventional anemometry; High resolution moments data and standard 10-minute increment data from the ASC sodar unit at the Prairie Island Casino Site; 10-minute increment data from a Triton Sodar unit at the Prairie Island Bluff Site. 8
Figure 10 is a photograph of the CSAT3 and conventional anemometry installed on the meteorological tower at the Prairie Island Casino Site. Due to equipment problems with the CSAT3 sensors, in June, 2011, Barr had to lower the 50-meter tower at the site, fix the CSAT3 sonic anemometers, and re-instal the tower. Since then, the CSAT3 sonic equipment at Prairie Island has collected over three gigawatts of data for use on the project. Having collected over six months of Sodar data at the Prairie Island site, in mid-july, 2011, Barr and WindLogics both removed their Sodar units from site. The conventional anemometry and SCAT3 sonic units, however, continue to collect data at the Casino Site. A summary of the data collected by the conventional anemometry at the Casino Site to date is attached. Fig. 10. Conventional and sonic anemometry installed on 50-meter tower at Prairie Island Casino Site 9
Additional Milestones: Work is in progress towards Milestone 6. Project Status: The project is ahead of schedule due to the fact that work on the project started before the contract was finalized. LEGAL NOTICE THIS REPORT WAS PREPARED AS A RESULT OF WORK SPONSORED BY NSP. IT DOES NOT NECESSARILY REPRESENT THE VIEWS OF NSP, ITS EMPLOYEES, OR THE RENEWABLE DEVELOPMENT FUND BOARD. NSP, ITS EMPLOYEES, CONTRACTORS, AND SUBCONTRACTORS MAKE NO WARRANTY, EXPRESS OR IMPLIED, AND ASSUME NO LEGAL LIABILITY FOR THE INFORMATION IN THIS REPORT; NOR DOES ANY PARTY REPRESENT THAT THE USE OF THIS INFORMATION WILL NOT INFRINGE UPON PRIVATELY OWNED RIGHTS. THIS REPORT HAS NOT BEEN APPROVED OR DISAPPROVED BY NSP NOR HAS NSP PASSED UPON THE ACCURACY OF ADEQUACY OF THE INFORMATION IN THIS REPORT. Appendix: List publications Papers in Refereed Journals 9. Chamorro, L.P., Arndt, REA and Sotiropoulos F. Drag reduction in large wind turbines through riblets: Evaluation of riblet geometry and application strategies. Renewable Energy. Under review. 8. Chamorro, L.P., Guala, M., Arndt, REA and Sotiropoulos F. On the evolution of turbulent scales in the wake of a wind turbine model. J. of Turbulence. Under review. 7. Chamorro, L.P., Arndt, REA and Sotiropoulos F. Turbulent flow properties around a staggered wind farm. Bound. Layer Meteorol. DOI:10.1007/s10546-011-9649-6. 6. Chamorro, L.P., Arndt, REA and Sotiropoulos F. Reynolds number dependence of turbulence statistics in the wake of wind turbines. Wind Energy. DOI: 10.1002/we.501. 5. Chamorro, L.P. and Arndt, REA. Non-uniform velocity distribution e ect on the Betz limit Wind Energy. Under review. 4. Chamorro, L.P., and F. Porté-Agel, 2010. Thermal stability and boundary-layer effects on wind turbine wakes. A wind tunnel study. Boundary-Layer Meteorology, 136: 489-513. 3. Lu, H., and F. Porté-Agel, 2010. A modulated subgrid-scale model for large-eddy simulation: Application to a neutral atmospheric boundary layer. Physics of Fluids, 22(1):015109. 2. Wu, Y.-T., and F. Porté-Agel, 2011. Large-eddy simulation of wind-turbine wakes: Evaluation of turbine parameterizations. Boundary-Layer Meteorology, 138:345 366. 1. Chamorro, L.P., and F. Porté-Agel, 2009. A wind tunnel investigation of wind turbines wakes: Boundary-layer turbulence and surface roughness effects. Boundary-Layer Meteorology, 132: 129-149. 10
Conference Presentations 10. Chamorro, L.P., R.E.A. Arndt and F. Sotiropoulos, 2011. Turbulence patterns around a large wind farm. 49th AIAA Aerospace Sciences Meeting, Orlando, Florida. 9. Chamorro, L.P., R.E.A. Arndt and F. Sotiropoulos, 2010. Turbulence characteristics around a staggered wind farm configuration. A wind tunnel study. American Physical Society Meeting. Minneapolis, Long Beach, CA. 8. Porté-Agel, F., Y.-T. Wu, and H. Lu, 2009. Large-eddy simulation of wind-turbine wakes. American Physical Society Meeting. Minneapolis, MN. 7. Chamorro, L.P., and F. Porté-Agel, 2009. A wind tunnel investigation of wind turbine wakes. American Physical Society Meeting. Minneapolis, MN. 6. Porté-Agel, F., F. Sotiropoulos, R. Conzemius, L. Chamorro, Y.-T. Wu, S. Behara, H. Lu, 2009. Development of a high-resolution Virtual Wind Simulator for optimal design of wind energy projects. E3 -Energy, Economic and Environmental- Conference. Minneapolis, MN. 5. Porté-Agel, F., Y.-T. Wu, Y.-T.., H. Lu, 2009. Parameterization of turbulent fluxes and wind-turbine forces in large-eddy simulation. European Geophysical Union. Vienna. 4. Chamorro, L.P., and F. Porté-Agel, 2009. A wind tunnel investigation of wind turbine wakes: Boundary-layer turbulence and surface roughness effects. European Geophysical Union. Vienna. 3. Wu, Y.-T.., H. Lu, and F. Porté-Agel, 2008. Large-eddy simulation of wind-turbine wakes. American Geophysical Union. San Francisco, CA. 2. Chamorro, L.P., and F. Porté-Agel, 2008. A wind tunnel investigation of wind turbine wakes: Boundary-layer turbulence and surface roughness effects. American Geophysical Union. San Francisco, CA. 1. Porté-Agel, F., F. Sotiropoulos, R. Conzemius, L. Chamorro, Y.-T. Wu, S. Behara, H. Lu, 2008. Development of a high-resolution Virtual Wind Simulator for optimal design of wind energy projects. E3 -Energy, Economic and Environmental- Conference. Minneapolis, MN. 11