Advertisement

Design optimization of a small-scale hydropower harvesting device

  • Rishav Aryal
  • Zoi Dokou
  • Ramesh B. Malla
  • Amvrossios C. BagtzoglouEmail author
Industrial Application Paper
  • 97 Downloads

Abstract

This paper presents a semi-analytical model that facilitates the optimum design of small-scale hydropower systems, so that maximum possible energy can be harvested under low head and streamflow conditions in run-of-the-stream settings. The hydropower harvesting model developed and tested at the University of Connecticut (Malla et al., Renewable Energy 36(5):1568–1577, 2011) comprises a rotating cylinder attached to a piston producing reciprocating motion when placed in moving water. Taking this model as the base, the semi-analytical model employs genetic algorithm–based optimization and develops optimal dimensions for the system, with the objective to minimize the time period while at the same time maximizing the stroke of the piston. The semi-analytical model is tested in steps: first with single-objective optimization for the time period and the stroke of the piston separately and then using multi-objective optimization to produce a set of Pareto optimal solutions. As many energy harvesting approaches are based on the reciprocating motion of the mechanical/structural system, which is greatly affected by the geometric dimensions of the system, optimization of the system geometry becomes crucial for energy harvesting. Compared to the hydropower harvesting model tested before (Malla et al., Renewable Energy 36(5):1568–1577, 2011), the semi-analytical model is able to reduce the arms’ dimensions while obtaining a much higher stroke of piston for much lower time period. The model has some limitations but is able to produce optimization results comparable to laboratory experimental data and applicable to actual flow data from Shetucket River in Willimantic, CT, USA.

Keywords

Hydropower Reciprocating cylinder system Power harvesting Optimization Genetic algorithm Robins–Magnus effect 

Notes

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

References

  1. Ahmed R, Mir F, Banerjee S (2017) A review on energy harvesting approaches for renewable energies from ambient vibrations and acoustic waves using piezoelectricity. Smart Mater Struct 26(8):085031CrossRefGoogle Scholar
  2. Beeby PS, Tudor MJ, White MN (2006) Energy harvesting vibration sources for micro systems applications. Meas Sci Technol 17:175–195CrossRefGoogle Scholar
  3. Bourguet R, Jacono RD (2014) Flow-induced vibrations of a rotating cylinder. J Fluid Mech, Cambridge University Press (CUP) 740:342–380CrossRefGoogle Scholar
  4. Davis DL (1991) Handbook of genetic algorithms. Van Nostrand Reinhold, New York, NYGoogle Scholar
  5. Díez P (2003) A note on the convergence of the secant method for simple and multiple roots. Appl Math Lett 16(8):1211–1215MathSciNetCrossRefGoogle Scholar
  6. Elvin GN, Lajnef N, Elvin AA (2006) Feasibility of structural monitoring with vibration powered sensors. Smart Mater Struct 15(4):977–986CrossRefGoogle Scholar
  7. Erturk A, Inman JD (2009) An experimentally validated bimorph cantilever model for piezoelectric energy harvesting from base excitations. Smart Mater Struct 18(2):025009CrossRefGoogle Scholar
  8. François B, Hingray B, Raynaud D, Borga M, Creutin JD (2015) Complementarity between solar and hydro power: sensitivity study to climate characteristics in northern-Italy. Renew Energy 86:543–553CrossRefGoogle Scholar
  9. François B, Hingray B, Raynaud D, Borga M, Creutin JD (2016) Increasing climate-related-energy penetration by integrating run-of the river hydropower to wind/solar mix. Renew Energy 87:686–696CrossRefGoogle Scholar
  10. Gleick PH (1992) Environmental consequences of hydroelectric development: the role of facility size and type. Energy 17(8):735–747CrossRefGoogle Scholar
  11. Goldberg ED (1989) Genetic algorithms in search, optimization, and machine learning. Addison-Wesley Longman, Boston, MAzbMATHGoogle Scholar
  12. Goodarzi M, Dehkordi EK (2017a) Complete vortex shedding suppression allocating twin rotating controllers at a suitable position. Ocean Eng 137:215–223CrossRefGoogle Scholar
  13. Goodarzi M, Dehkordi EK (2017b) Geometrical parameter analysis on stabilizing the flow regime over a circular cylinder using two small rotating controllers. Comput Fluids 145:129–140MathSciNetCrossRefGoogle Scholar
  14. Kecik K, Borowiec M (2013) An autoparametric energy harvester. SpringerLink, Springer Berlin Heidelberg 222(7):1597–1605Google Scholar
  15. Malla RB, Shrestha B, Bagtzoglou A, Drasdis J, Johnson P (2011) Hydropower harvesting from a small scale reciprocating system. Renew Energy 36(5):1568–1577CrossRefGoogle Scholar
  16. Marler RT, Arora JS (2004) Survey of multi-objective optimization methods for engineering. Struct Multidiscip Optim 26(6):369–395MathSciNetCrossRefGoogle Scholar
  17. McCarthy JM, Soh GS (2010) Geometric design of linkages, 2nd edn. Springer-Verlag, New YorkGoogle Scholar
  18. Myszka D (2012) Machines and mechanisms: applied kinematic analysis. Pearson Education, New JerseyGoogle Scholar
  19. NASA (2008) Lift of rotating cylinder. National Aeronautics and Space Administration. Glenn Research Center, Cleveland, OH http://www.grc.nasa.gov/WWW/K-12/airplane/cyl.html Google Scholar
  20. Sengupta TK, Talla SB (2004) Robins-Magnus effect: a continuing saga. Curr Sci 86(7):1033–1036MathSciNetGoogle Scholar
  21. Sengupta TK, Kasliwal A, De S, Nair M (2004) Temporal flow instability for Magnus-Robins effect at high rotation rates. J Fluids Struct 17(7):941–953CrossRefGoogle Scholar
  22. Shen D, Choe SY, Kim DJ (2006) Comparison of piezoelectric material for vibration energy conversion devices. MRS Proc:966Google Scholar
  23. Shen W, Zhu S, Zhu H (2016) Experimental study on using electromagnetic devices on bridge stay cables for simultaneous energy harvesting and vibration damping. Smart Mater Struct 25:065011CrossRefGoogle Scholar
  24. Shenck SN, Paradiso AJ (2001) Energy scavenging with shoe-mounted piezoelectrics. IEEE Micro 21(3):30–42CrossRefGoogle Scholar
  25. The MathWorks (1993) MATLAB user’s guide. The MathWorks Inc., Natick, MAGoogle Scholar
  26. Tokumaru PT, Dimotakis PE (1993) The lift of a cylinder executing rotary motions in a uniform flow. J. Fluid Mech 255:1–10CrossRefGoogle Scholar
  27. Torres EO, Gabriel AR (2005) Harvesting ambient energy will make embedded devices autonomous. EE times, San Francisco, CA https://www.eetimes.com/document.asp?doc_id=1276782 Google Scholar
  28. USGS (2018) Site Map for USGS 01122500 Shetucket River near Willimantic, CT, http://waterdata.usgs.gov/ct/nwis/nwismap/?site_no=01122500&agency_cd=USGS. Accessed 10 May 2018
  29. Vila L, Malla RB (2012) Investigation on bridge vibration energy harvesting using an electromagnetic system. Proc., 21st Connecticut Symposium on Microelectronics & Optoelectronics: 79-80Google Scholar
  30. Wang D, Chao C, Chen J (2012) A miniature hydro-energy generator based on pressure fluctuation in Karman vortex. Street. J Intell Mater Syst Struct 24(5):612–626CrossRefGoogle Scholar
  31. White FM (2011) Fluid mechanics, 7th edn. McGraw-Hill, New York, NY, p 550Google Scholar
  32. Whitley D (1994) A genetic algorithm tutorial. Stat Comput 4(2):65–85CrossRefGoogle Scholar
  33. Zheng B, Chang CJ, Gea HC (2009) Topology optimization of energy harvesting devices using piezoelectric materials. Struct Multidiscip Optim 38(1):17–23.  https://doi.org/10.1007/s00158-008-0265-0 CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  • Rishav Aryal
    • 1
  • Zoi Dokou
    • 1
  • Ramesh B. Malla
    • 1
  • Amvrossios C. Bagtzoglou
    • 1
    Email author
  1. 1.Department of Civil and Environmental EngineeringUniversity of ConnecticutStorrsUSA

Personalised recommendations