Position parameters optimization of surface piercing propeller by artificial neural network.
Journal
Scientific reports
ISSN: 2045-2322
Titre abrégé: Sci Rep
Pays: England
ID NLM: 101563288
Informations de publication
Date de publication:
27 Jan 2024
27 Jan 2024
Historique:
received:
06
11
2023
accepted:
17
01
2024
medline:
28
1
2024
pubmed:
28
1
2024
entrez:
27
1
2024
Statut:
epublish
Résumé
Improving the performance of surface-piercing propellers is achieved by investigating the influential factors. In this study, Artificial Neural Network is used to identify nonlinear models for estimating various phenomena. Non-Dominated Sorting Genetic Algorithm II is considered as an optimization tool. In this study, in order to optimize the position parameters, including the immersion ratio, angle of attack, and yaw angle, data from experimental tests at the HYDROTECH center of IUST were collected as the initial data field for the generation of training data by the artificial neural network, then experimental tests were implemented in the position of the Non-Dominated Sorting Genetic Algorithm II proposed as the output, and the results were compared. The Artificial Neural Network results showed that the mean error of the trained verified and test data is 7.5e-5, 1e-4, and 1e-4, respectively. Comparing the experimental and optimization results, the thrust coefficient showed a relative error of 9.7%, while the torque coefficient showed a relative error of 7.5%, this algorithm can be used as a cost-effective, time-saving method for a similar problem.
Identifiants
pubmed: 38280956
doi: 10.1038/s41598-024-52325-8
pii: 10.1038/s41598-024-52325-8
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
2295Informations de copyright
© 2024. The Author(s).
Références
Okada, Y., Yoshioka, M., Fujita, T. & Watanabe, K. Experimental study on surface piercing propeller. J. Soc. Naval Arch. Japan 2000, 111–116 (2000).
doi: 10.2534/jjasnaoe1968.2000.188_111
Peterson, D. T. Surface piercing propeller performance, Citeseer, (2005).
Ferrando, M., Crotti, S. & Viviani, M. in Proceedings of 2nd International Conference on Marine Research and Transportation (ICMRT), Ischia. 28–30.
Rose, J., Kruppa, C. & Koushan, K. Surface piercing propellers-propeller/hull interaction. (1993).
Dyson, P. K. Modelling, testing and design, of a surface piercing propeller drive. (2000).
Shiba, H. Air-drawing of marine propellers. Rep. Transp. Tech. Res. Inst. 9, 1–320 (1953).
Hadler, J. Performance of partially submerged propellers. 7th ONR Symposhium on Naval Hydrodynamics-Rome (August 1968) (1968).
Shields, C. E. Performance characteristics of several partially submerged supercavitating propellers. (Naval Ship Research and Development Center Washington DC Hydromechanics Lab, 1968).
SHAO-ZONG, L. in International High-Performance Vehicle Conference-Shanghai,(November 1988).
Hecker, R. & Crown, D. Performance Characteristics of Partially-Submerged Propeller 4281 with Varying Number of Blades at Low Advance Coefficients. NSRDC T and E Report (1970).
Olofsson, N. Force and flow characteristics of a partially submerged propeller. (Chalmers University of Technology, 1996).
Nozawa, K. & Takayama, N. Experimental study on propulsive performance of surface piercing propeller. Journal-Kansai Society Of Naval Architects Japan, 63–70 (2002).
Ferrando, M., Viviani, M., Crotti, S., Cassella, P. & Caldarella, S. in Proceedings of 7th international conference on hydrodynamics (ICHD), Ischia.
Ding, E. in Proc., 9th International Conference on Fats Sea Transportation, Shanghai, China.
Lorio, J. M. Open water testing of a surface piercing propeller with varying submergence, yaw angle and inclination angle. (Florida Atlantic University, 2010).
FlorianVesting, R. B. in Second International Symposium on Marine Propulsors.
Misra, S., Gokarn, R., Sha, O., Suryanarayana, C. & Suresh, R. Development of a four-bladed surface piercing propeller series. Nav. Eng. J. 124, 105–138 (2012).
Vesting, F., Johansson, R. & Bensow, R. In Proceedings of the Third International Symposium on Marine Propulsors.(Launceston, Tasmania, Australia). Ed. by J. Binns, R. Brown, and N. Bose. University of Tasmania. 397–404.
Nouri, N. M., Mohammadi, S. & Zarezadeh, M. Optimization of a marine contra-rotating propellers set. Ocean Eng. 167, 397–404 (2018).
doi: 10.1016/j.oceaneng.2018.05.067
Seyyedi, S. M., Shafaghat, R. & Donyavizadeh, N. A review on the hydrodynamic characteristics of the spp concerning to the available experimental data and evaluating regression polynomial functions. Int. J. Mar. Technol. 10, 25–35 (2018).
doi: 10.29252/ijmt.10.25
Yousefi, A. & Shafaghat, R. Numerical study of the parameters affecting the formation and growth of ventilation in a surface-piercing propeller. Appl. Ocean Res. 104, 102360 (2020).
doi: 10.1016/j.apor.2020.102360
Tadros, M., Ventura, M. & Guedes Soares, C. Optimization procedures for a twin controllable pitch propeller of a ROPAX ship at minimum fuel consumption. J. Mar. Eng. Technol. 1, 1–9 (2022).
Rajhi, W. et al. Prediction of milled surface characteristics of carbon fiber-reinforced polyetheretherketone using an optimized machine learning model by gazelle optimizer. Measurement 222, 113627 (2023).
doi: 10.1016/j.measurement.2023.113627
Alhawsawi, A. M., Moustafa, E. B., Fujii, M., Banoqitah, E. M. & Elsheikh, A. Kerf characteristics during CO2 laser cutting of polymeric materials: experimental investigation and machine learning-based prediction. Eng. Sci. Technol. Int. J. 46, 101519 (2023).
Elsheikh, A. H. Applications of machine learning in friction stir welding: Prediction of joint properties, real-time control and tool failure diagnosis. Eng. Appl. Artif. Intell. 121, 105961 (2023).
doi: 10.1016/j.engappai.2023.105961
Bahaj, A., Molland, A., Chaplin, J. & Batten, W. Power and thrust measurements of marine current turbines under various hydrodynamic flow conditions in a cavitation tunnel and a towing tank. Renew. Energy 32, 407–426 (2007).
doi: 10.1016/j.renene.2006.01.012
Pustoshny, A. V., Boiutsov, V., Lebedev, E. P. & Stroganov, A. A. In 9th International Conference on Fast Sea Transportation, FAST 2007. 348–354.
Zakerdoost, H. & Ghassemi, H. Hydrodynamic optimization of ship’s hull-propeller system under multiple operating conditions using MOEA/D. J. Mar. Sci. Technol. 26, 419–431 (2021).
doi: 10.1007/s00773-020-00747-0
McCulloch, W. S. & Pitts, W. A logical calculus of the ideas immanent in nervous activity. Bull. Math. Biophys. 5, 115–133 (1943).
doi: 10.1007/BF02478259
Agatonovic-Kustrin, S. & Beresford, R. Basic concepts of artificial neural network (ANN) modeling and its application in pharmaceutical research. J. Pharm. Biomed. Anal. 22, 717–727 (2000).
doi: 10.1016/S0731-7085(99)00272-1
pubmed: 10815714
JERI, T. G. hdl. handle. net. (2023).
Samuel, O. W., Asogbon, G. M., Sangaiah, A. K., Fang, P. & Li, G. An integrated decision support system based on ANN and Fuzzy_AHP for heart failure risk prediction. Expert Syst. Appl. 68, 163–172 (2017).
doi: 10.1016/j.eswa.2016.10.020
Verma, S., Pant, M. & Snasel, V. A comprehensive review on NSGA-II for multi-objective combinatorial optimization problems. IEEE Access 9, 57757–57791 (2021).
doi: 10.1109/ACCESS.2021.3070634