Atricle

Info

Title:: Hydrodynamic Performance Prediction of Stepped Planing Craft Using CFD and ANNs

Author(s):: Hamid Kazemi¹; M. Mehdi Doustdar¹; Amin Najafi¹; Hashem Nowruzi²; M. Javad Ameri¹

Affilations:

Keywords:: Stepped planing craft; Hydrodynamic performance; Artificial neural network(ANN); Computational fluid dynamics (CFD); Resistance

DOI:: 10.1007/s11804-020-00182-y

Abstract:: In the present paper, the hydrodynamic performance of stepped planing craft is investigated by computational fluid dynamics (CFD) analysis. For this purpose, the hydrodynamic resistances of without step, one-step, and two-step hulls of Cougar planing craft are evaluated under different distances of the second step and LCG from aft, weight loadings, and Froude numbers (Fr). Our CFD results are appropriately validated against our conducted experimental test in National Iranians Marine Laboratory (NIMALA), Tehran, Iran. Then, the hydrodynamic resistance of intended planing crafts under various geometrical and physical conditions is predicted using artificial neural networks (ANNs). CFD analysis shows two different trends in the growth rate of resistance to weight ratio. So that, using steps for planing craft increases the resistance to weight ratio at lower Fr and decreases it at higher Fr. Additionally, by the increase of the distance between two steps, the resistance to weight ratio is decreased and the porpoising phenomenon is delayed. Furthermore, we obtained the maximum mean square error of ANNs output in the prediction of resistance to weight ratio equal to 0.0027. Finally, the predictive equation is suggested for the resistance to weight ratio of stepped planing craft according to weights and bias of designed ANNs.

References:

Ahmadi F, Ranji AR, Nowruzi H (2020) Ultimate strength prediction of corroded plates with center-longitudinal crack using FEM and ANN. Ocean Eng 206:107281
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
Armstrong JS, Collopy F (1992) Error measures for generalizing about forecasting methods: empirical comparisons. Int J Forecasting 8:69–80
Bakhtiari M, Veysi S, Ghassemi H (2016) Numerical modeling of the stepped planing hull in calm water. Int J Eng-Trans B: Appl 29(2):236–245
Blount DL, Clement EP (1963) Resistance test of a systematic series of planing hull forms. Trans SNAME 71:491–579
Bowles BJ, Denny BS (2005) Water surface disturbance near the bow of high speed, hard chine hull forms. In: Paper presented at: 8th international conference on fast sea transportation, Petersburg, Russia
Brizzolara S, Serra F (2007) Accuracy of CFD codes in the prediction of planing surfaces hydrodynamic characteristics. In: Paper presented at: 2nd international conference on marine research and transportation, Naples, Italy
Caponnetto M (2001) Practical CFD simulations for planing hulls. In: Paper presented at: Process of Second International Euro Conference on High Performance Marine Vehicles, Hamburg, Germany
CD-Adapco (2015) User guide STAR-CCM+ Version 10.06
Celik IB, Ghia U, Roache PJ, Freitas CJ (2008) Procedure for estimation and reporting of uncertainty due to discretization in CFD applications. J Fluid Eng-T ASME 130:078001–078004
Choi B, Lee JH, Kim DH (2008) Solving local minima problem with large number of hidden nodes on two-layered feed-forward artificial neural networks. Neurocomputing 71:3640–3643
Committee P (2002) Final report and recommendations to the 23rd ITTC. Proceeding of 23rd ITTC
Cucinotta F, Guglielmino E, Sfravara F (2017) An experimental comparison between different artificial air cavity designs for a planing hull. Ocean Eng 140:233–243
De Luca F, Mancini S, Miranda S, Pensa C (2016) An extended verification and validation study of CFD simulations for planing hulls. J Ship Res 60(2):101–118
De Marco A, Mancini S, Miranda S, Scognamiglio R, Vitiello L (2017) Experimental and numerical hydrodynamic analysis of a stepped planing hull. Appl Ocean Res 64:135–154
Di Caterino F, NiazmandBilandi R, Mancini S, Dashtimanesh A, De Carlini M (2018) A numerical way for a stepped planing hull design and optimization. In: Proceedings of NAV 2018, 19th International Conference on Ship & Maritime Research, Trieste, Italy
Djavareshkian MH, Esmaeili A (2013) Neuro-fuzzy based approach for estimation of hydrofoil performance. Ocean Eng 59:1–8
Doctors LJ (1985) Hydrodynamics of high-speed small craft (No. 292)
Ferziger JH, Peri? M (1999) Computational methods for fluid dynamics, 3rd edn. Springer, Verlag
Garland WR, Maki KJ (2012) A numerical study of a two-dimensional stepped planing surface. J Ship Prod Des 28:60–72
Garson GD (1991) Interpreting neural network connection weights. Artif Int Expert 6:47–51
Ghadimi P, Loni A, Nowruzi H, Dashtimanesh A, Tavakoli S (2014) Parametric study of the effects of trim tabs on running trim and resistance of planing hulls. Adv Shipp Ocean Eng:3
Hay A, Leroyer A, Visonneau M (2006) H-adaptive Navier–Stokes simulations of free-surface flows around moving bodies. J Mar Sci Tech-Japan 11:1–18
ITTC (2014) Recommended procedures and guidelines - practical guidelines for ship CFD applications, section 7.5-03-02-03. In:International Towing Tank Conference
Jiang Y, Sun H, Zou J, Hu A, Yang J (2016) Analysis of tunnel hydrodynamic characteristics for planing trimaran by model tests and numerical simulations. Ocean Eng 113:101–110
Katayama T, Hayashita S, Suzuki K, Ikeda Y (2002) Development of resistance test for high speed planing craft using very small model scale effects on drag force. In: Paper presented at: Asia Pacific Workshop on Hydrodynamics, Kobe, Japan
Loni A, Ghadimi P, Nowruzi H, Dashtimanesh A (2013) Developing a computer program for mathematical investigation of stepped planing hull characteristics. Int J Phys Res 1
Mahmoodi K, Ghassemi H, Nowruzi H (2017) Data mining models to predict ocean wave energy flux in the absence of wave records. ZeszytyNaukoweAkademiiMorskiej w Szczecinie
Makasyeyev MV (2009) Numerical modeling of cavity flow on bottom of a stepped planing hull. In: paper presented at: 7th International Symposium on Cavitation, Ann Arbor, Michigan, USA
Masumi Y, Nikseresht AH (2017) Comparison of numerical solution and semi-empirical formulas to predict the effects of important design parameters on porpoising region of a planing vessel. Appl Ocean Res 68:228–236
Morabito MG (2015) Prediction of planing hull side forces in yaw using slender body oblique impact theory. Ocean Eng 101:47–57
Najafi A, Nowruzi H, Ghassemi H (2018) Performance prediction of hydrofoil-supported catamarans using experiment and ANNs. Appl Ocean Res 75:66–84
Niazmand Bilandi R, Mancini S, Vitiello L, Miranda S, DeCarlini M (2018) A validation of symmetric 2D+ T model based on singlestepped planing hull towing tank tests. J Mar Sci Eng 6(4):136
Niazmand Bilandi R, Mancini S, Dashtimanesh A, Tavakoli S, De Carlini M (2019) A numerical and analytical way for double-stepped planing hull in regular wave. In: Proceedings of VIII International Conference on Computational Methods in Marine Engineering, MARINE 2019, Gothenburg, Sweden 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 1:203–211
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 39:3863–3880
Nowruzi H, Ghassemi H, Ghiasi M (2017b) Performance predicting of 2D and 3D submerged hydrofoils using CFD and ANNs. J Mar Sci Tech-Japan 22:710–733
Nowruzi H, Ghassemi H, Yousefifard M (2020) Prediction of hydrodynamic instability in the curved ducts by means of semi-analytical and ANN approaches. Partial Differ Equ Appl Math 1:100004. https://doi.org/10.1016/j.padiff.2020.100004
P Committee (2002) Final report and recommendations to the 23rd ITTC. Proceeding of 23rd ITTC Prechelt L (1999) Early stopping — but when?, in Neural networks: tricks of the trade. Springer, Berlin Heidelberg
Radoj?i? D, Kalajd?i? M (2018) Resistance and trim modeling of Naples hard chine systematic series. RINA Trans Int J Small Craft Technol.https://doi.org/10.3940/rina.ijsct,p.b1
Rumelhart DE, Hinton GE, Williams RJ (1986) Learning internal representations by error propagation, in: parallel distributed processing. MIT Press, Cambridge
Savitsky D (1964a) Hydrodynamic analysis of planing hulls. Mar Technol 1:71–95
Savitsky D (1964b) Hydrodynamic design of planing hulls. Mar Technol 1
Savitsky D, Morabito M (2010) Surface wave contours associated with the fore body wake of stepped planing hulls. Mar Technol 47:1–16
Savitsky D, DeLorme MF, Datla R (2007) Inclusion of whisker spray drag in performance prediction method for high-speed planing hulls. Mar Technol 44:35–56
Seo J, Choi HK, Jeong UC, Lee DK, Rhee SH, Jung CM, Yoo J (2016) Model tests on resistance and sea keeping performance of wavepiercing high-speed vessel with spray rails. Int J Nav Arch Ocean 8:442–455
Shora MM, Ghassemi H, Nowruzi H (2018) 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 Mar Eng Technol 17:59–84
Shuford CL Jr (1958) A theoretical and experimental study of planing surfaces including effects of cross section and plan form. NACA Tech Rep 1355
Su Y, Chen Q, Shen H, Lu W (2012) Numerical simulation of a planing vessel at high speed. J Mar Sci Appl 11:178–183
Sukas OF, Kinaci OK, Cakici F, Gokce MK (2017) Hydrodynamic assessment of planing hulls using overset grids. Appl Ocean Res 65:35–46
Tafuni A, Sahin I, Hyman M (2016) Numerical investigation of wave elevation and bottom pressure generated by a planing hull in finitedepth water. Appl Ocean Res 58:281–291
Taghva HR, Ghassemi H, Nowruzi H (2018) Seakeeping performance estimation of the container ship under irregular wave condition using artificial neural network. Am J Civil Eng Archit 6:147–153
Trenn S (2008) Multilayer perceptrons: approximation order and necessary number of hidden units. IEEE T Neural Networ 19:836–844
Wheelwright S, Makridakis S, Hyndman RJ (1998) Forecasting: methods and applications. Wiley, New York
Yousefi R, Shafaghat R, Shakeri M (2013) Hydrodynamic analysis techniques for high-speed planing hulls. Appl Ocean Res 42:105–113

Journal of Marine Science and Application[ISSN:1002-2848/CN:61-1400/f]

Info

References:

Memo

Commonly used function

Navigate

Tools

Statistics