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An approach was presented to improve the performance prediction of marine propeller through computational fluid dynamics (CFD). After a series of computations were conducted, it was found that the passage in the former study was too narrow, resulting in the unnecessary radial outer boundary effects. Hence, in this study, a fatter passage model was employed to avoid unnecessary effects, in which the diameter was the same as the length from the propeller to the downstream outlet and the diameter was larger than the previous study. The diameter and length of the passage were 5_{t} and_{q} were less than 5% and 3% on different advance coefficients

The performance of propellers is of significant importance as it affects the overall performance of the machinery. Nowadays, computational fluid dynamics (CFD) is extensively used for the design purposes, allowing experimental tests to be performed only in the final stages of the project. With reference to propeller applications, CFD simulations can be used to predict the flow around propellers and their performances. Therefore, with the continuous improvement of propeller’s performance prediction, numerical research rather than experiment can ultimately be achieved in a cheaper and faster way.

In recent years, researchers had used different computational domain and turbulence models to conduct studies of the propellers, which validated the feasibility of numerical methods to some extent in comparison with the experimental data. Paterson et al. [

Some more advanced but computationally demanding models were also discussed. Yu et al. [

It was noteworthy that the whole blades passage could acquire the flow more directly than a single blade passage, because the flow got from a single passage had to duplicate roughly to form the whole flow filed. The computational domain was subdivided into

The propeller DTMB P5168 was used to evaluate the fat passage model. Meanwhile, the standard

The standard

In the derivation of the

The turbulence kinetic energy,

and

In these equations, G_{k} represents the generation of turbulence kinetic energy due to the mean velocity gradients, G_{b} is the generation of turbulence kinetic energy due to buoyancy, _{k} and _{k} and

The turbulent (or eddy) viscosity,

where

As shown in Figure

(a) DTMB P5168 model; (b) computational domain.

Taking the propeller blade as the origin and the flow direction of the liquid as the positive direction of the

Figure

Related settings in simulation.

Numerical method | Settings/Value |
---|---|

Inlet and the radial outer boundary setting | A free-stream velocity components turbulence intensity: 1% |

Outlet setting | Pressure-outlet static pressure: 0 Pa |

all solid surfaces setting | No Slip boundary condition |

Calculation model | The standard |

Calculation scheme | SIMPLEC |

Momentum | Second-order backward discretization |

pressure | a body force weighted |

(a) DTMB P5168 blade surface meshes; (b) global grids.

The propeller motion can be divided into rotating motion around its own axis and moving along the axis direction, which can be replaced by

where

By applying the fluid velocity to the propeller instead of the forward propulsion velocity, six groups of different advance coefficients (

The notation used throughout the paper was as follows. Thrust_{t}, torque coefficient_{q}, and propeller efficiency

where^{3}) was the density of the fluid.

The relative percentage errors

Before the CFD calculation, a grid independence study at^{–3}, and the number of iterations reached 5000. Finally, the total grid number was 3.6 million, which included 1.4 million of the rotating part and 2.2 million of the fixed part. In addition, there were 0.1 million and 0.68 million gird cells in the blade part and hub part, respectively. Figure

(a) Grid independence study; (b) the y+ distribution on the blade surface.

(a) A relative error diagram of simulated results of dimensionless thrust coefficient_{t}; (b) hydrodynamic performance prediction and test curve of_{t}

Figures _{t}_{q}_{t} and_{q} were less than 5% and 3%, respectively. However, no matter what value_{t} and_{q} were in the range of _{t} and_{q} in Yang’s work were slightly smaller than ours [

(a) A relative error diagram of simulated results of torque coefficient_{q}; (b) hydrodynamic performance prediction and test curve of_{q}

(a) A relative error diagram of simulated results of propeller efficiency

Distributions of three velocity components on a particular plane of_{x}); (b) tangential velocity components (_{t}); (c) radial velocity components (_{r}).

The experimental results of axial, tangential, and radial velocity components at different radial distances

The accurate prediction of the pressure distribution on the propeller blade was important for analyzing the flow characteristics of a propeller design. When the turbulence was fully developed on blade surfaces, the pressure gradient was expected to be nearly similar with different advance coefficients (

Pressure profile on the pressure side.

Focusing on the pressure on the suction side shown in Figure

Pressure profile on the suction side.

In Figure

Velocity and streamlines profile on the plane of x=0.

Figure

Velocity field on the plane of z=0.

It was interesting to note that the middle area closing to the outlet boundary provided a lower velocity, where the streamlines twisted up clearly but the backflow did not appear, which indicated that the computational domain with the fat cylinder was applicable to obtain the highly accurate predicted results. In conclusion, the velocity flow not only explained the reason why the blade and streamlines changed with the advance coefficient but also suggested that the computational domain was rational.

In this paper, the propeller DTMB P5168 was chosen to carry out the investigation. The purpose of this study was to improve the performance prediction ability of marine propellers under steady flow. Owing to employ the Multiple Reference Frame (MRF) approach, the computational domain was subdivided into

Except for the case of_{t} and_{q} were less than 5% and 3%, respectively. However, no matter what value

With the increase of

Despite lacks of computation capacity, the steady-state simulation with hexastructured girds still provides a detailed understanding of the potential physical phenomena. CFD is a useful tool to validate and improve the performances of marine propeller models, which can play an important role in shortening the design cycle.

Propeller diameter, 0.4027 m

Propeller radius, m

Propeller rotational speed, rev/s

Incoming flow velocity, m/s

Advance coefficient,

Torque, N

Thrust, N·m

Torque coefficient,^{2}·^{5})

Thrust coefficient,^{2}·^{4})

Propeller efficiency,_{T}/(2_{Q})

Axial velocity, normalized by

Radial velocity, normalized by

Tangential velocity in the rotating frame, normalized by V, m/s

Axial coordinate, from propeller mid plane

Density, 998 kg/m^{3}

Kinematic viscosity, m^{2}/s.

The data used to support the findings of this study are available from the corresponding author upon request.

The authors declared that there were no conflicts of interest regarding the publication of this paper.

This work was supported by the Postdoctoral Fund of Liaoning Province (Grant no. 20170520351), the Key Scientific and Technological Projects of Jilin Province (Grant no. 20170204066GX) and Science and Technology Projects of the Edcation Department of Jilin Province (Grant No. JJKH20180137KJ).