Skip to main content
SpringerLink
Log in

Windfarm layout optimization with a newly-modified multi-wake model based on aerodynamic characteristics of floating wind-turbines

  • Original Article
  • Published:
Journal of Mechanical Science and Technology Aims and scope Submit manuscript

Abstract

The aerodynamics of a floating wind turbine was analyzed, and the results were applied to a wake model by introducing a modified NOJ model. In addition, the effect of the 6-DOFs characteristics on the wind farm optimization is examined by comparing the platform type (fixed and floating) and wake models (NOJ model and modified NOJ model). The unsteady vortex-lattice method (UVLM) is used to analyze the aerodynamic characteristics of a floating wind turbine. The relationship between the 6-DOFs motion and power performance is validated through theoretical analysis. This study proposes a modified NOJ model to consider the multiwake effect of a floating wind farm. The results demonstrate that the conventional layout optimization method has an error in the layout position and power performance due to the characteristics of a floating wind turbine. Therefore, 6-DOFs motion and its influence should be considered for accurate floating wind farm optimization.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

Abbreviations

c l :

Lift coefficient

c l,Tab :

Lift coefficient of lookup table

c l,UVLM :

Lift coefficient of UVLM

c m :

Moment coefficient

c m,Tab :

Moment coefficient of lookup table

c m,UVLM :

Moment coefficient of UVLM

C p :

Power coefficient

C T :

Thrust coefficient

δ :

Eddy viscosity coefficient

dS :

Differential surface area

ε :

Residual

n :

Normal vector

r :

Vector between point of interest and vortex segment

γ :

Vortex strength

Γ:

Circulation

Γcorrected :

Corrected circulation

Γinitial :

Initial circulation

ρ :

Air density

U :

Wind speed

Ū :

Average wind speed

Φ :

Velocity potential

Φ ∞ :

Freestream potential

References

  1. F. Ahmed, M. Naeem and M. Iqbal, ICT and renewable energy: a way forward to the next-generation telecom base stations, Telecommunication Systems, 64(1) (2017) 43–56.

    Article  Google Scholar 

  2. A. Arapogianni and A. Genach, Deep Water—The Next Step for Offshore Wind Energy, European Wind Energy Association (2013).

  3. N. N. Sørensen, J. A. Michelsen and S. Schreck, Navier-Stokes predictions of the NREL phase VI rotor in the NASA Ames 80 ft × 120 ft wind tunnel, Wind Energy: An International Journal for Progress and Applications in Wind Power Conversion Technology, 5(2–3) (2002) 151–169.

    Article  Google Scholar 

  4. A. Suzuki, Application of dynamic inflow theory to wind turbine rotors, Doctoral Thesis, The University of Utah (2000).

  5. D. A. Peters, D. D. Boyd and C. J. He, Finite-state-induced flow model for rotors in hover and forward flight, Journal of the American Helicopter Society, 34(4) (1989) 5–17.

    Article  Google Scholar 

  6. J. M. Jonkman and M. L. Buhl Jr, Fast User’s Guide-updated August 2005 (No. NREL/TP-500-38230), National Renewable Energy Laboratory (NREL), Golden, CO, USA (2005).

    Book  Google Scholar 

  7. M. Jeon, S. Lee, T. Kim and S. Lee, Wake influence on dynamic load characteristics of offshore floating wind turbines, AIAA Journal (2016) 3535–3545.

  8. H. Kim, S. Lee and S. Lee, Numerical analysis on the aerodynamics of HAWTs using nonlinear vortex strength correction, Current Applied Physics, 10(2) (2010) S311–S315.

    Article  Google Scholar 

  9. G. Mosetti, C. Poloni and B. Diviacco, Optimization of wind turbine positioning in large windfarms by means of a genetic algorithm, Journal of Wind Engineering and Industrial Aerodynamics, 51(1) (1994) 105–116.

    Article  Google Scholar 

  10. P. Costa, A. J. de Pina Martins and A. da Silva Carvalho, Optimization of Energy Generation in Wind Farm Through Fuzzy Control, European Wind Energy Association (2004).

  11. S. F. Rodrigues, R. T. Pinto, M. Soleimanzadeh, P. A. Bosman and P. Bauer, Wake losses optimization of offshore wind farms with moveable floating wind turbines, Energy Conversion and Management, 89 (2015) 933–941.

    Article  Google Scholar 

  12. H. Díaz, D. Silva, C. Bernardo and C. G. Soares, Micro sitting of floating wind turbines in a wind farm using a multi-criteria framework, Renewable Energy, 204 (2023) 449–474.

    Article  Google Scholar 

  13. L. Cao, M. Ge, X. Gao, B. Du, B. Li, Z. Huang and Y. Liu, Wind farm layout optimization to minimize the wake induced turbulence effect on wind turbines, Applied Energy, 323 (2022) 119599.

    Article  Google Scholar 

  14. B. J. Jonkman and J. M. Jonkman, Addendum to the User’s Guides for FAST, A2AD, and AeroDyn Released March 2010–February 2013, National Renewable Energy Laboratory (NREL), Golden, CO, USA (2013).

    Google Scholar 

  15. D. Laino and A. C. Hansen, Current efforts toward improved aerodynamic modeling using the AeroDyn subroutines, 42nd AIAA Aerospace Sciences Meeting and Exhibit, 826 (2004).

  16. J. Katz and A. Plotkin, Low-speed Aerodynamics, 2nd ed, Cambridge University Press (2001).

  17. B. Hwang, H. Kim and S. Lee, Wind farm layout optimization using multidisciplinary model, Journal of Mechanical Science and Technology, 32(6) (2018) 2919–2924.

    Article  Google Scholar 

  18. H. M. Johlas, L. A. Martínez-Tossas, M. A. Lackner, D. P. Schmidt and M. J. Churchfield, Large eddy simulations of offshore wind turbine wakes for two floating platform types, Journal of Physics: Conference Series, 1452(1) (2020) 012034.

    Google Scholar 

  19. I. Katic, J. Højstrup and N. O. Jensen, A simple model for cluster efficiency, European Wind Energy Association Conference and Exhibition, 1 (1986).

  20. R. Shakoor, M. Y. Hassan, A. Raheem and Y. K. Wu, Wake effect modeling: A review of wind farm layout optimization using Jensen’s model, Renewable and Sustainable Energy Reviews, 58 (2016) 1048–1059.

    Article  Google Scholar 

  21. N. Srinivas and K. Deb, Muiltiobjective optimization using nondominated sorting in genetic algorithms, Evolutionary Computation, 2(3) (1994) 221–248.

    Article  Google Scholar 

  22. J. Jonkman, S. Butterfield, W. Musial and G. Scott, Definition of a 5-MW Reference Wind Turbine for Offshore System Development, National Renewable Energy Lab, Golden, CO, USA (2009).

    Book  Google Scholar 

  23. IEC 61400-3, Wind Turbines - Part 3: Design Requirements for Offshore Wind Turbines, International Electrotechnical Commision (IEC) (2005).

  24. T. Zilong and D. X. Wei, Layout optimization of offshore wind farm considering spatially inhomogeneous wave loads, Applied Energy, 306 (2022) 117947.

    Article  Google Scholar 

  25. D. Micallef and T. Sant, A review of wind turbine yaw aerodynamics, Wind Turbines-Design, Control and Applications (2016).

  26. B. Wen, X. Tian, X. Dong, Z. Peng and W. Zhang, Influences of surge motion on the power and thrust characteristics of an offshore floating wind turbine, Energy, 141 (2017) 2054–2068.

    Article  Google Scholar 

  27. B. Wen, X. Dong, X. Tian, Z. Peng, W. Zhang and K. Wei, The power performance of an offshore floating wind turbine in platform pitching motion, Energy, 154 (2018) 508–521.

    Article  Google Scholar 

Download references

Acknowledgments

This research has been supported by the Institute of Advanced Aerospace Technology, SNU and Institute of Engineering Research at Seoul National University.

Author information

Authors and Affiliations

  1. Department of Mechanical and Aerospace Engineering, Seoul National University, Seoul, 08826, Korea

    Hyunkee Kim, Wonsuk Han & Dongwook Kim

  2. Dept. of Aerospace Engineering and Institute of Engineering Research, Seoul National University, Seoul, 08826, Korea

    Soogab Lee

Authors
  1. Hyunkee Kim
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Wonsuk Han
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Dongwook Kim
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Soogab Lee
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Soogab Lee.

Additional information

Soogab Lee is a Professor in the Department of Mechanical and Aerospace Engineering at Seoul National University. He received his Ph.D. in Aeronautics and Astronautics from Stanford University in 1992. He worked as a Research Scientist at NASA Ames Research Center from 1992 to 1995. His research interests are in the area of aerodynamics and acoustics of rotating machines including wind turbine systems.

Hyunkee Kim is a Ph.D. student in the Department of Mechanical and Aerospace Engineering at Seoul National University. He received his M.S. in Mechanical and Aerospace Engineering at Seoul National University in 2019. His research interests are in wind farm optimization and multiwake model.

Wonsuk Han is a Ph.D. student in the Department of Mechanical and Aerospace Engineering at Seoul National University. He received his M.S. in Mechanical and Aerospace Engineering at Seoul National University in 2020. His research interests are in wind turbine aerodynamics and noise analysis.

Dongwook Kim is a Ph.D. student in the Department of Mechanical and Aerospace Engineering at Seoul National University. He received his M.S. in Mechanical and Aerospace Engineering at Seoul National University in 2018. His research interests are in machinery noise and noise reduction.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Kim, H., Han, W., Kim, D. et al. Windfarm layout optimization with a newly-modified multi-wake model based on aerodynamic characteristics of floating wind-turbines. J Mech Sci Technol 37, 4661–4670 (2023). https://doi.org/10.1007/s12206-023-0821-y

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12206-023-0821-y

Keywords

Search

Navigation