Skip to main content

Advertisement

SpringerLink
Log in

Topology optimization of an offshore jacket structure considering aerodynamic, hydrodynamic and structural forces

  • Original Article
  • Published:
Engineering with Computers Aims and scope Submit manuscript

Abstract

The present work focused on the optimization of offshore wind turbine structure which can sustain different environmental conditions and is of the least cost. Size and topology optimization is carried out for the jacket structure from the National Renewable Energy Laboratory (NREL) [used in the Offshore Code Comparison Collaboration Continuation (OC4) project] by using teaching learning-based optimization (TLBO) algorithm and genetic algorithm (GA). The optimization process is carried out in Matlab along with the time-dependent dynamic wind turbine simulation with the aerodynamic, hydrodynamic and structural forces in the fatigue, aerodynamics, structures, and turbulence code (FAST) from NREL. This is an innovative process which can be used to substitute the time-consuming construction of a wind turbine for its analysis. In this work, both static and dynamic analyses are carried out for simultaneous size and topology optimization. The forces applied to the structure are realistic in nature and fatigue analysis is carried out to ensure that the structure does not fail during its design life. This ensures that the simulation is more accurate and realistic as compared with other analysis. The results showed that the TLBO algorithm is effective compared to GA in terms of size and topology optimization. Further, the other state-of-the art algorithms from the Congress on Evolutionary Computation (CEC) such as differential evolution, LSHADE, multi-operator EA-II, effective butterfly optimizer, and unified differential evolution are also implemented and the comparative results of all the algorithms are presented.

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

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14

Similar content being viewed by others

References

  1. Nelson V (2013) Wind energy: renewable energy and the environment. CRC Press, Boca Raton

    Book  Google Scholar 

  2. IPCC (2018) IPCC Special report—global warming of 1.5 ºC. Intergovernmental panel on climate change Geneva. http://www.ipcc.ch/report/sr15/

  3. Martens JH (2014) Topology optimization of a jacket for an offshore wind turbine: by utilization of genetic algorithm (Master's thesis, Institutt for bygg, anlegg og transport)

  4. Kolios A, Collu M, Chahardehi A, Brennan FP, Patel MH (2010) A multi- criteria decision making method to compare support structures for offshore wind turbines. In: Offshore, Process & Engineering Department, School of Engineering, Cranfield University, Bedforshire, European Wind Energy Conference and Exhibition (EWEC)

  5. Sullivan RG, Kirchler LB, Cothren J, Winters SL (2013) Offshore wind turbine visibility and visual impact threshold distances. Environ Pract 15(1):33–49

    Article  Google Scholar 

  6. Chew KH, Tai K, Ng EYK, Muskulus M (2016) Analytical gradient-based optimization of offshore wind turbine substructures under fatigue and extreme loads. Mar Struct 47:23–41

    Article  Google Scholar 

  7. Mohammadi SF, Galgoul NS, Starossek U, Videiro PM (2016) An efficient time domain fatigue analysis and its comparison to spectral fatigue assessment for an offshore jacket structure. Mar Struct 49:97–115

    Article  Google Scholar 

  8. Oest J, Overgaard LCT, Lund E (2015) Gradient based structural optimization with fatigue constraints of jacket structures for offshore wind turbines. In: 11th World Congress on Structural and Multidisciplinary Optimization

  9. Jonkman J, Butterfield S, Musial W, Scott G (2009) Definition of a 5-MW reference wind turbine for offshore system development (No. NREL/TP-500-38060). National Renewable Energy Laboratory (NREL), Golden, CO

  10. AlHamaydeh M, Barakat S, Nasif O (2017) Optimization of support structures for offshore wind turbines using genetic algorithm with domain-trimming. Math Problems Eng 2017(2):1–14

    Article  Google Scholar 

  11. Häafele J, Rolfes R (2016) Approaching the ideal design of jacket substructures for offshore wind turbines with a Particle Swarm Optimization algorithm. In: The 26th International Ocean and Polar Engineering Conference. International Society of Offshore and Polar Engineers

  12. Gentils T, Wang L, Kolios A (2017) Integrated structural optimisation of offshore wind turbine support structures based on finite element analysis and genetic algorithm. Appl Energy 199:187–204

    Article  Google Scholar 

  13. Tejani GG, Savsani VJ, Bureerat S, Patel VK, Savsani PV (2018) Topology optimization of truss subjected to static and dynamic constraints by integrating simulated annealing into passing vehicle search algorithms. Eng Comput. https://doi.org/10.1007/s00366-018-0612-8

    Article  Google Scholar 

  14. Oest J, Sørensen R, Overgaard CT, Lund E (2017) Structural optimization with fatigue and ultimate limit constraints of jacket structures for large offshore wind turbines. Struct Multidiscip Optim 55(3):779–793

    Article  MathSciNet  Google Scholar 

  15. Techasen T, Wansasueb K, Panagant N, Pholdee N, Bureerat S (2018) Simultaneous topology, shape, and size optimization of trusses, taking account of uncertainties using multi-objective evolutionary algorithms. Eng Comput. https://doi.org/10.1007/s00366-018-0629-z

    Article  Google Scholar 

  16. Tejani GG, Savsani VJ, Patel VK, Mirjalili S (2018) Truss optimization with natural frequency bounds using improved symbiotic organisms search. Knowl-Based Syst 143:162–178

    Article  Google Scholar 

  17. Rao RV, Savsani VJ, Vakharia DP (2011) Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Comput Aided Des 43(3):303–315

    Article  Google Scholar 

  18. Vorpahl F, Popko W, Kaufer D (2011) Description of a basic model of the “UpWind reference jacket” for code comparison in the OC4 project under IEA Wind Annex. Fraunhofer Institute for Wind Energy and Energy System Technology (IWES), Bremerhaven.

  19. Manning T, Sleator RD, Walsh P (2013) Naturally selecting solutions: the use of genetic algorithms in bioinformatics. Bioengineered 4(5):266–278

    Article  Google Scholar 

  20. Karaboga D, Basturk B (2007) A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm. J Global Optim 39(3):459–471

    Article  MathSciNet  Google Scholar 

  21. Dhiman G, Kumar V (2018) Multi-objective spotted hyena optimizer: a multi-objective optimization algorithm for engineering problems. Knowl-Based Syst 150:175–197

    Article  Google Scholar 

  22. Geem ZW, Kim JH, Loganathan GV (2001) A new heuristic optimization algorithm: harmony search. Simulation 76(2):60–68

    Article  Google Scholar 

  23. Patel VK, Savsani VJ (2015) Heat transfer search (HTS): a novel optimization algorithm. Inf Sci 324:217–246

    Article  Google Scholar 

  24. Yazdani M, Jolai F (2016) Lion optimization algorithm (LOA): a nature-inspired metaheuristic algorithm. J Comput Design Eng 3(1):24–36

    Article  Google Scholar 

  25. Jonkman JM, Hayman GJ, Jonkman BJ, Damiani RR (2015) AeroDyn v15 user’s guide and theory manual. NREL Draft Report

  26. Jonkman JM, Robertson A, Hayman GJ (2014) HydroDyn user’s guide and theory manual. National Renewable Energy Laboratory

  27. Veritas DN (2010) Fatigue design of offshore steel structures. No DNV-RP-C 203:30

    Google Scholar 

  28. Amzallag C, Gerey JP, Robert JL, Bahuaud J (1994) Standardization of the rainflow counting method for fatigue analysis. Int J Fatigue 16(4):287–293

    Article  Google Scholar 

  29. Awad N, Ali MZ, Reynolds RG (2015) A differential evolution algorithm with success-based parameter adaptation for CEC2015 learning-based optimization. In: 2015 IEEE congress on evolutionary computation, pp 1098–1105

  30. Tanabe R, Fukunaga AS (2014) Improving the search performance of SHADE using linear population size reduction. In: 2014 IEEE congress on evolutionary computation (CEC) 2014, pp 1658–1665

  31. Elsayed SM, Sarker RA, Essam DL, Hamza NM (2014) Testing united multi-operator evolutionary algorithms on the CEC2014 real-parameter numerical optimization. In: 2014 IEEE congress on evolutionary computation (CEC), pp 1650–1657

  32. Kumar A, Misra RK, Singh D (2017) Improving the local search capability of effective butterfly optimizer using covariance matrix adapted retreat phase. In: 2017 IEEE congress on evolutionary computation (CEC), pp 1835–1842

  33. Trivedi A, Srinivasan D, Biswas N (2018) An improved unified differential evolution algorithm for constrained optimization problems. In: Proceedings of 2018 IEEE congress on evolutionary computation (CEC), pp 1–10

Download references

Author information

Authors and Affiliations

  1. Department of Engineering, Canadore College, North Bay, ON, Canada

    Vimal Savsani

  2. Pandit Deendayal Petroleum University, Gandhinagar, Gujarat, India

    Parth Dave & Vivek Patel

  3. Indus University, Ahmedabad, Gujarat, India

    Bansi D. Raja

Authors
  1. Vimal Savsani
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Parth Dave
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Bansi D. Raja
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Vivek Patel
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Vivek Patel.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Savsani, V., Dave, P., Raja, B.D. et al. Topology optimization of an offshore jacket structure considering aerodynamic, hydrodynamic and structural forces. Engineering with Computers 37, 2911–2930 (2021). https://doi.org/10.1007/s00366-020-00983-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00366-020-00983-3

Keywords

Search

Navigation