Geometrical and environmental conditions influence the hydrodynamic performance of a catamaran. The separation distance between the demi-hulls of a catamaran (i.e., the interference factor or span) is one of the main effective geometrical parameters, and water depth to draft ratio (i.e., h/T) is an important environmental condition. Therefore, in the present study, the hydrodynamic performance of the Delft catamaran (DC372) is numerically investigated under various spans and the water depth to draft ratios. To accomplish this, the Reynolds-Averaged Navier-Stokes (RANS) solver within the OpenFOAM CFD toolbox was employed. The numerical results are appropriately validated against the existing numerical and experimental data. Based on the major results, sinkage significantly decreases when the water depth to draft ratio increases from h/T = 2 to deep water, particularly for span 0.5. In the case of span 0.9, sinkage at h/T = 3 is obtained higher compared to h/T = 2. Increasing the spans from 0.5 to 0.9 significantly decreases the total resistance, particularly for Froude numbers higher than 0.2. Finally, the generalized design optimization framework for selecting the span is presented.
CastiglioneTHeWSternF, et al. URANS simulations of catamaran interference in shallow water. J Mar Sci Technol2014; 19: 33–51.
2.
JuliantoRIMuttaqieTAdiputraR, et al. Hydrodynamic and structural investigations of catamaran design. Procedia Struct Integr2020; 27: 93–
3.
ChenCDelefortrieGLataireE.Effects of water depth and speed on ship motion control from medium deep to very shallow water. Ocean Eng2021; 231: 109102.
4.
ShiGPriftisAXing-KaedingY, et al. Numerical investigation of the resistance of a zero-emission full-scale fast catamaran in shallow water. J Mar Sci Eng2021; 9(6): 563.
5.
UlgenKDhanakMR.Hydrodynamic performance of a catamaran in shallow waters. J Mar Sci Eng2022; 10(9): 1169.
6.
UlgenK.Hydrodynamic performance and sea-keeping analysis of a catamaran in transforming near-shore head and following seas. PhD Thesis, Florida Atlantic University, USA, 2022.
7.
SulistyawatiWSudjastaBWaskitoDY.Comparison of shallow water effect on a monohull and chine catamaran. IOP Conf Ser Earth Environ Sci2022; 972: 012046.
8.
DumanSBoulougourisEAungMZ, et al. Numerical evaluation of the wave-making resistance of a zero-emission fast passenger ferry operating in shallow water by using the double-body approach. J Mar Sci Eng2023; 11(1): 187.
9.
MartićIDegiuliNBorčićK, et al. Numerical assessment of the resistance of a solar catamaran in shallow water. J Mar Sci Eng2023; 11(9): 1706.
10.
LauCYAli-LavroffJHollowayDS, et al. A novel CFD approach for the prediction of ride control system response on wave-piercing catamaran in calm water. Ocean Eng2023; 286: 115494.
11.
MahmoodiHHajivandA.Numerical simulation of resistance test for a naval vessel in shallow water. Int J Marit Technol2023; 19: 57–64.
12.
YeQOuYXiangG, et al. Numerical study on the influence of water depth on air layer drag reduction. Appl Sci2024; 14(1): 431.
13.
FarkasADegiuliNTomljenovićI, et al. Numerical investigation of interference effects for the Delft 372 catamaran. Proc Inst Mech Eng Part M: J Eng Marit Environ2024; 238(2): 385–394.
14.
HadiESTuswanTAzizahG, et al. Influence of the canal width and depth on the resistance of 750 DWT Perintis ship using CFD simulation. Brodogradnja2023; 74(1): 117–144.
15.
MartićIAnušićBDegiuliN, et al. Numerically investigating the effect of trim on the resistance of a container ship in confined and shallow water. Appl Sci2024; 14(15): 6570.
16.
MaZJiNZengQ, et al. Influence of scale effect on flow field offset for ships in confined waters. Brodogradnja2024; 75(1): 1–22.
17.
DoğrulAKahramanoğluEÇakıcıF.Numerical prediction of interference factor in motions and added resistance for Delft catamaran 372. Ocean Eng2021; 223: 108687.
18.
CastiglioneTSternFBovaS, et al. Numerical investigation of the seakeeping behavior of a catamaran advancing in regular head waves. Ocean Eng2011; 38(16): 1806–1822.
19.
PanahiRJahanbakhshESeifMS.Towards simulation of 3D nonlinear high-speed vessels motion. Ocean Eng2009; 36: 256–265.
20.
JasakH.Error analysis and estimation for the finite volume method with applications to fluid flows. PhD Thesis, Imperial College, UK, 1996.
21.
RuscheH.Computational fluid dynamics of dispersed two-phase flows at high phase fractions. PhD Thesis, Imperial College, UK, 2002.
22.
BerberovićENilsPHinsbergV, et al. Drop impact onto a liquid layer of finite thickness: dynamics of the cavity evolution. Phys Rev E2009; 79(3): 036306.
23.
MenterFR.Review of the shear-stress transport turbulence model experience from an industrial perspective. Int J Comput Fluid Dyn2009; 23(4): 305–316.
24.
HinzeJO.Turbulence. New York, NY: McGraw-Hill, 1975.
25.
SpezialeCG.Analytical methods for the development of Reynolds stress closures in turbulence. Annu Rev Fluid Mech1991; 23: 107–157.
26.
International Towing Tank Conference. Uncertainty analysis in CFD verification and validation, methodology and procedures. In: Proceedings of the 28th international towing tank conference, Wuxi, China, 17–22 September 2017.
27.
FarkasADegiuliNMartićI.Numerical investigation into the interaction of resistance components for a series 60 catamaran. Ocean Eng2017; 146: 151–169.
28.
SeraniAScholczTPVanziV.A scoping review on simulation-based design optimization in marine engineering: trends, best practices, and gaps. Arch Computat Methods Eng2024; 31(8): 4709–4737.
29.
BatesDMWattsDG.Nonlinear regression analysis and its applications, vol. 2. New York, NY: Wiley, 1988.
30.
NajafiANowruziHGhassemiH.Performance prediction of hydrofoil-supported catamarans using experiment and ANNs. Appl Ocean Res2018; 75: 66–84.
31.
KazemiHDoustdarMMNajafiA, et al. Hydrodynamic performance prediction of stepped planing craft using CFD and ANNs. J Mar Sci Appl2021; 20(1): 67–84.
32.
NowruziH.Performance prediction of stepped planing hulls using experiment and ANNs. Ocean Eng2022; 246: 110660.
33.
AhmadiFNowruziHRahbar-RanjiA.Estimation of ultimate shear strength of one-side corroded-plates cracks by FEM and ANNs. J Braz Soc Mech Sci Eng2023; 45(7): 385.
34.
NocedalJWrightSJ.Numerical optimization. New York, NY: Springer, 2006.
35.
ZhuCByrdRHLuP, et al. Algorithm 778: L-BFGS-B: Fortran subroutines for large-scale bound-constrained optimization. ACM Trans Math Softw1997; 23(4): 550–560.
36.
HosmerDWJrLemeshowSSturdivantRX.Applied logistic regression. Hoboken, NJ: John Wiley & Sons, 2013.
