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Controllable pitch propeller optimization through meta-heuristic algorithm

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Abstract

This paper describes a methodology to design and optimize a controllable pitch propeller suitable for small leisure ship boats. A proper range for design parameters has to be set by the user. An optimization based on the Particle Swarm Optimization algorithm is carried out to minimize a fitness function representing the engine’s fuel consumption. The OpenProp code has been integrated in the procedure to compute thrust and torque. Blade’s geometry and tables about pitch, thrust and consumption are the main output of the optimization process. A case study has been included to show how the procedure can be implemented in the design process. A case study shows that the procedure allows a designer to sketch a controllable pitch propeller with optimal efficiency; computational times are compatible with the design conceptual phase where several scenarios must be investigated to set the most suitable for the following detailed design. A drawback of this approach is given by the need for a quite skilled user in charge of defining the allowable ranges for design parameters, and the need for data about the engine and boat to be designed.

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Notes

  1. Form 2 by Formlabs (https://formlabs.com/3d-printers/form-2/).

Abbreviations

\(A_\mathrm{E}/A_\mathrm{O}\) :

Blade area ratio

\(\beta _i\) :

Initial stagger angle (\(\circ\))

\(\beta _2\) :

Stagger angle for intermediate velocity (\(\circ\))

\(\beta _1\) :

Stagger angle for lowest advance velocity (\(\circ\))

c/D :

Chord distribution along blade

D :

Diameter (m)

\(D_\mathrm{hub}\) :

Hub diameter (m)

\(D_\mathrm{opt}\) :

Optimum propeller diameter (m)

\(\eta\) :

Propeller efficiency

\(\mathrm{FC}\) :

Fuel consumption (kg / h)

f/c :

Camber distribution along blade

\(\Gamma\) :

Vortex circulation

J :

Advance ratio

\(K_\mathrm{T}, K_\mathrm{Q}\) :

Thrust and torque propeller coefficients

\(k_{\mathrm{cav}}\) :

Cavitation penalization coefficient

\(k_{\mathrm{stress}}\) :

Maximum stress penalization coefficient

\(k_{\mathrm{thrust}}\) :

Generated thrust penalization coefficient

n :

Rotational speed (1 / s)

P/D :

Pitch distribution along blade

\(P_\mathrm{T}\) :

Thrust power (W)

\(P_\mathrm{D}\) :

Delivered power (W)

Q :

Torque (N m)

RPM:

Rotational speed ( 1 / min)

\(\rho\) :

Water density (kg / m\(^3\))

\(\sigma _{N}\) :

Cavitation number

t :

Deduction factor

T :

Generated thrust (N)

\(T_{\mathrm{des}}\) :

Desired thrust (N)

\(\%_{\mathrm{th}}\) :

Throttle percentage

\(V_\mathrm{a}\) :

Volumetric mean inflow velocity (m / s )

\(V_\mathrm{s}\) :

Advance ship speed ( m / s)

Z :

Blade number

References

  1. Techet A (2017) Hydrodynamics for ocean engineers

  2. Lewis EV (1988) Principles of naval architecture—vol II: Resistance, propulsion and vibration. The Society of Naval Architects and Marine Engineers, New Jersey

    Google Scholar 

  3. Griffin PE, Kinnas SA (1998) A design method for high-speed propulsor blades. J Fluids Eng 120(3):556. https://doi.org/10.1115/1.2820698

    Article  Google Scholar 

  4. Mishima S, Kinnas S (1997) Application of a numerical optimization technique to the design of cavitating propellers in nonuniform flow. J Ship Res 41(2):93–107

    Article  Google Scholar 

  5. Gaafary M, El-Kilani H, Moustafa M (2011) Optimum design of b-series marine propellers. Alex Eng J 50(1):13–18. https://doi.org/10.1016/j.aej.2011.01.001. http://www.sciencedirect.com/science/article/pii/S1110016811000093

  6. Chen J-H, Shih Y-S (2007) Basic design of a series propeller with vibration consideration by genetic algorithm. J Mar Sci Technol 12(3):119–129. https://doi.org/10.1007/s00773-007-0249-6

    Article  Google Scholar 

  7. Vesting F (2015) Marine propeller optimisation—strategy and algorithm development.Ph.D. thesis, Chalmers University of Technology, Department of Shipping and Marine Technology

  8. Lee C-S, Choi Y-D, Ahn B-K, Shin M-S, Jang H-G (2010) Performance optimization of marine propellers. Int J Naval Architect Ocean Eng 2(4):211–216. https://doi.org/10.2478/IJNAOE-2013-0038. http://www.sciencedirect.com/science/article/pii/S2092678216302503

  9. Gaggero S, Tani G, Villa D, Viviani M, Ausonio P, Travi P, Bizzarri G, Serra F (2017) Efficient and multi-objective cavitating propeller optimization: an application to a high-speed craft. Appl Ocean Res 64:31–57. https://doi.org/10.1016/j.apor.2017.01.018

    Article  Google Scholar 

  10. Balsamo F, De Luca F, Pensa C (2011) Continuous optimization of controllable pitch propellers for fast ferries, IX. HSMV, Naples

    Google Scholar 

  11. Bertetta D, Brizzolara S, Gaggero S, Viviani M, Savio L (2012) CPP propeller cavitation and noise optimization at different pitches with panel code and validation by cavitation tunnel measurements. Ocean Eng 53:177–195. https://doi.org/10.1016/j.oceaneng.2012.06.026

    Article  Google Scholar 

  12. Stoye T (2011) Propeller design and propulsion concepts for ship operation in off-design conditions. Second international symposium on marine propulsors. SMP’11, Hamburg

    Google Scholar 

  13. Amoroso CL, Liverani A, Caligiana G (2018) Numerical investigation on optimum trim envelope curve for high performance sailing Yacht hulls. Ocean Eng 163:76–84

    Article  Google Scholar 

  14. Epps BP, Kimballz RW (2013) Unified rotor lifting line theory. J Ship Res 57(4). DOIurlhttps://doi.org/10.5957/JOSR.57.4.110040

  15. Kerwin J (2007) Hydrofoils and propellers 2007

  16. Priyanta D (2011) Basic turbine—propeller matching. Department of Marine Engineering-ITS, Surabaya

    Google Scholar 

  17. Wrench J (1957) The calculation of propeller induction factors. In: Technical report 1116, David Taylor model basin

  18. Black S (1997) Integrated lifting surface/Navier–Stokes design and analysis methods for marine propulsors. Ph.D. thesis, MIT

  19. Keshavarzzadeh V, Meidani H, Tortorelli D (2016) Gradient based design optimization under uncertainty via stochastic expansion methods. Comput Methods Appl Mech Eng 306:47–76

    Article  MathSciNet  Google Scholar 

  20. Fishman G (1995) Monte Carlo: concepts, algorithms, and applications. Springer, New York

    MATH  Google Scholar 

  21. Goldberg D (1989) Genetic algorithms in search, optimization and machine learning. Addison-Wesley, Reading

    MATH  Google Scholar 

  22. Tan Y, Zhu Y (2010) Fireworks algorithm for optimization. In: Lecture notes in computer science (including subseries Lecture notes in artificial intelligence and Lecture notes in bioinformatics), vol 6145. LNCS, issue part 1, 2010, pp 355–364. 1st international conference on advances in swarm intelligence, ICSI 2010, Beijing, 12–15 June 2010

  23. Atashpaz-Gargari E, Lucas C (2007) Imperialist competitive algorithm for optimization inspired by imperialistic competition. In: IEEE congress in evolutionary computation (ed) IEEE congress in evolutionary computation, Singapore

  24. Kirkpatrick S, Gelatt C, Vecchi M (1983) Optimization by simulated annealing. Science 220:671–680

    Article  MathSciNet  Google Scholar 

  25. Gambardella LM, Dorigo M (4996) Solving symmetric and asymmetric TSPS by ant colonies. In: Proceedings of the IEEE conference on evolutionary computation 1996, ICEC’96, Nagoya, pp 622–627, 20–22 May 1996. https://doi.org/10.5957/JOSR.57.4.110040

  26. Srinivas N, Deb K (1994) Muiltiobjective optimization using nondominated sorting in genetic algorithms. Evolut Comput 2(3):221–248. https://doi.org/10.1162/evco.1994.2.3.221

    Article  Google Scholar 

  27. Huismann J, Foeth EJ (2017) Automated multi-objective optimization of ship propellers. In: Proceedings of the fifth international symposium on marine propulsors, SMP’17, Espoo

  28. Kennedy J, Eberhart R (1995) Particle swarm optimization. In: IEEE international conference on neural networks—conference proceedings, vol 4, pp 1942–1948, Part 1 (of 6); Perth, 27 Nov–1 Dec 1995

  29. Ceruti A, Marzocca P (2017) Heuristic optimization of Bezier curves based trajectories for unconventional airships docking. Aircr Eng Aerosp Technol 89(1):76–86

    Article  Google Scholar 

  30. Ceruti A (2018) Meta-heuristic multidisciplinary design optimization of wind turbine blades obtained from circular pipes. Eng Comput 30:1–17

    Google Scholar 

  31. Vesting F, Gustafsson R, Bensow RE (2016) Development and application of optimisation algorithms for propeller design. Ship Technol Res 63(1):50–69. https://doi.org/10.1080/09377255.2016.1145916

    Article  Google Scholar 

  32. Bacciaglia A, Ceruti A, Liverani A (2019) A systematic review of voxelization method in additive manufacturing. Mech Ind 20(6):630. https://doi.org/10.1051/meca/2019058

    Article  Google Scholar 

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  1. DIN Department, School of Engineering and Architecture, University of Bologna, Viale Risorgimento 2, Bologna, Italy

    Antonio Bacciaglia, Alessandro Ceruti & Alfredo Liverani

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  1. Antonio Bacciaglia
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  2. Alessandro Ceruti
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  3. Alfredo Liverani
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Correspondence to Antonio Bacciaglia.

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Bacciaglia, A., Ceruti, A. & Liverani, A. Controllable pitch propeller optimization through meta-heuristic algorithm. Engineering with Computers 37, 2257–2271 (2021). https://doi.org/10.1007/s00366-020-00938-8

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