Prediction of the hydrodynamic performance and cavitation volume of the marine propeller using gene expression programming

References

• ANSYS Fluent. 2016. 15.0 user Guide. New Hampshire: ANSYS Inc.
   Google Scholar
• Bensow RE, Bark G. 2010. Implicit LES predictions of the cavitatng flow on a propeller. J Fluids Eng. 132(4):0413021–04130210.
   Web of Science ®Google Scholar
• Chiang ST, Chen JH. 2016. A comparison study of different cavitation models on the development of 2-D cavitating flows. The 12th International Conference on Hydrodynamics; September; Egmond aan Zee, Netherlands. p. 18–23.
   Google Scholar
• Dubbioso G, Muscari R, Di Mascio A. 2013. Analysis of the performances of a marine propeller operating in oblique flow. Comput Fluids. 75:86–102.
   Web of Science ®Google Scholar
• Ferreira C. 2001a. Gene expression programming in problem solving. 6th Online World Conference on Soft Computing in Industrial Applications (Invited Tutorial), World Federation on Soft Computing (WFSC).
   Google Scholar
• Ferreira C. 2001b. Gene expression programming: a new adaptive algorithm for solving problems. Complex Syst. 13(2):87–129.
   Google Scholar
• Ferreira C. 2004. Gene expression programming and the evolution of computer programs. In: Castro LND, Zuben FJV, editor(s). Recent developments in biologically inspired computing. Pennsylvania: Idea Group Publishing; p. 82–103.
   Google Scholar
• Ferreira C. 2006. Gene expression programming: mathematical modeling by an artificial intelligence. 2nd ed. New York: Springer.
   Google Scholar
• Ghassemi H. 2009. The effect of wake flow and skew angle on the ship propeller performance. Trans B Mech Eng. 16:149–158.
   Google Scholar
• Ha S, Um TS, Roh MI, Shin HK. 2017. A structural weight estimation model of FPSO topsides using an improved genetic programming method. Ships Offshore Struct. 12(1):43–55.
   Web of Science ®Google Scholar
• Herath MT, Natarajan S, Gangadhara Prusty B, John NS. 2015. Isogeometric analysis and genetic algorithm for shape-adaptive composite marine propellers. Comput Methods Appl Mech Eng. 284(1):835–860.
   Google Scholar
• Ji B, Luo X, Wu Y. 2014. Unsteady cavitation characteristics and alleviation of pressure fluctuations around marine propellers with different skew angles. J Mech Sci Tech. 28(4):1339–1348.
   Web of Science ®Google Scholar
• Kim SY, Moon BY. 2006. Wake distribution prediction on the propeller plane in ship design using artificial intelligence. Ships Offshore Struct. 1(2):89–98.
   Google Scholar
• Kowalski J, Krawczyk B, Woźniak M. 2017. Fault diagnosis of marine 4-stroke diesel engines using a one-vs-one extreme learning ensemble. Eng Appl Artif Intell. 57:134–141.
   Web of Science ®Google Scholar
• Koza JR. 1992. Genetic programming: on the programming of computers by means of natural selection. Cambridge: MIT Press.
   Google Scholar
• Kruszewski J. 2014. Ship’s propulsion neural controller main engine – pitch propeller – shaft generator. IFAC Proc Volumes. 47(1):905–912.
   Google Scholar
• Liu Y, Zhao P, Wang Q, Chen Z. 2012. URANS computation of cavitating flows around skewed propellers. J Hydrodyn Ser B. 24(3):339–346.
   Web of Science ®Google Scholar
• Mahmoodi K, Ghassemi H. 2018. Outlier detection in ocean wave measurements by using unsupervised data mining methods. Pol Marit Res. 25(1):44–50. DOI:10.2478/pomr-2018-0005.
   Web of Science ®Google Scholar
• Mahmoodi K, Ghassemi H, Nowruzi H. 2017. Data mining models to predict ocean wave energy flux in the absence of wave records. Sci J Marit Univ Szczecin. 49(121):119–129.
   Google Scholar
• Mao Y, Young YL. 2016. Influence of skew on the added mass and damping characteristics of marine propellers. Ocean Eng. 121(15):437–452.
   Google Scholar
• Mitchell M. 1996. An introduction to genetic algorithms. Cambridge: MIT Press.
   Google Scholar
• Niu X, Yang C, Wang H, Wang Y. 2017. Investigation of ANN and SVM based on limited samples for performance and emissions prediction of a CRDI-assisted marine diesel engine. Appl Therm Eng. 111(25):1353–1364.
   Google Scholar
• Nowruzi H, Ghassemi H. 2016. Using artificial neural network to predict velocity of sound in liquid water as a function of ambient temperature, electrical and magnetic fields. J Ocean Eng Sci. 1(3):203–211.
   Web of Science ®Google Scholar
• Nowruzi H, Ghassemi H, Amini E, Sohrabi-asl I. 2017a. Prediction of impinging spray penetration and cone angle under different injection and ambient conditions by means of CFD and ANNs. J Braz Soc Mech Sci Eng. 39(10):3863–3880.
   Web of Science ®Google Scholar
• Nowruzi H, Ghassemi H, Ghiasi M. 2017b. Performance predicting of 2D and 3D submerged hydrofoils using CFD and ANNs. J Marine Sci Tech. 22(4):710–733.
   Web of Science ®Google Scholar
• Pasini A. 2015. Artificial neural networks for small dataset analysis. J Thorac Dis. 7:953–960.
   PubMed Web of Science ®Google Scholar
• Rudzki K, Tarelko W. 2016. A decision-making system supporting selection of commanded outputs for a ship's propulsion system with a controllable pitch propeller. Ocean Eng. 126(1):254–264.
   Google Scholar
• Ryan D, Hamill GA, Johnston HT. 2013. Determining propeller induced erosion alongside quay walls in harbours using artificial neural networks. J Ocean Eng. 59(1):142–151.
   Google Scholar
• Schneer GH, Sauer J. 2001. Physical and numerical modeling of unsteady cavitation dynamics. Paper presented at 4th International Conference on Multiphase Flow; Orleans.
   Google Scholar
• Shamsi R, Ghassemi H. 2013. Numerical investigation of yaw angle effects on propulsive characteristics of podded propulsors. Int J Naval Architect Ocean Eng. 5:287–301.
   Web of Science ®Google Scholar
• Shora MM, Ghassemi H, Nowruzi H. 2017. Using computational fluid dynamic and artificial neural networks to predict the performance and cavitation volume of a propeller under different geometrical and physical characteristics. J Marine Eng Tech. DOI:10.1080/20464177.2017.1300983.
   Web of Science ®Google Scholar
• Singhal AK, Athavale MM, Li H, Jiang Y. 2002. Mathematical basic and validation of full cavitation model. J Fluids Eng. 124(3):617–624.
   Web of Science ®Google Scholar
• Ueno M, Tsukada Y. 2016. Estimation of full-scale propeller torque and thrust using free-running model ship in waves. Ocean Eng. 120:30–39.
   Web of Science ®Google Scholar
• Vaghefi M, Mahmoodi K, Akbari M. 2017. A comparison among data mining algorithms for outlier detection using flow pattern experiments. Sci Iranica. 25(2):90–605.
   Web of Science ®Google Scholar
• Vaghefi M, Mahmoodi K, Akbari M. 2018. Detection of outlier in 3D flow velocity collection in an open-channel bend using various data mining techniques. Iran J Sci Technol Trans Civ Eng. DOI:10.1007/s40996-018-0131-2.
   Web of Science ®Google Scholar
• Van Lammeren WPA, Van Manen JD, Oosterveld MWC. 1969. The Wageningen B-screw series.
   Google Scholar
• Young YL. 2008. Fluid-structure interaction analysis of flexible composite marine propellers. J Fluids Struct. 24(6):799–818.
   Web of Science ®Google Scholar
• Zhu Z. 2015. Numerical study on characteristic correlation between cavitating flow and skew of ship propellers. Ocean Eng. 99(1):63–71.
   Google Scholar