Numerical investigations of flow characteristics of a pumpjet propulsor in oblique inflow

Lu, Lin; Wang, Chen; Qin, Denghui

doi:10.1016/j.apor.2020.102343

Cited by 13 publications

(7 citation statements)

References 24 publications

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“…The SST k-ω turbulence model is a two-equation eddy-viscosity model proposed by Menter [24], which has become widely used in flow simulation of underwater vehicles and PJP [25][26][27][28][29][30][31]. The shear stress transport (SST) formulation combines the best of k-ω model and k-ε model, is able to investigate a model of low Reynolds number without using the wall function method.…”

Section: Numerical Simulation Methodsmentioning

confidence: 99%

Influence of Various Stator Parameters on the Open-Water Performance of Pump-Jet Propulsion

Huang

Pan

et al. 2021

JMSE

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In order to improve the hydrodynamic performance of pump-jet propulsion (PJP) when matching stator with the rotor, the RANS method with SST k-ω turbulence model is employed to study the influence of six kinds of stator parameters, which are classified into three groups, i.e., stator solidity, stator angles and rotor–stator spacing (S). Results show that the stator solidity involves the blade number (Ns) and chord length (L), has an obvious acceleration effect at and after stator, and produces a higher thrust and torque with a slight efficiency change. Further comparing Ns and L results, we find greater distinctions between the two cases when stator solidity is greatly adjusted. Three stator angles, i.e., stagger angle (α), lean angle (γ), and sweep angle (β), are studied. The α has the biggest effect on the thrust, torque, and efficiency; meanwhile, it shifts the advance number that corresponds to maximum efficiency. The effect of γ is similar to α, but its influence is far less than α. However, there is little difference between various β cases except for off-design conditions, where the efficiency drops dramatically as β increases. The S has a slight effect on PJP performance. Even though S decreases 34% relative to the original PJP, the rotor thrust and torque increase by less than 1%. In addition, we compare torque balance locations under various parameters, and each component force is analyzed in detail to explain the reason for performance variation. The present work is conducive to future optimization in PJP design.

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Section: Numerical Simulation Methodsmentioning

confidence: 99%

Influence of Various Stator Parameters on the Open-Water Performance of Pump-Jet Propulsion

Huang

Pan

et al. 2021

JMSE

Self Cite

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“…The no-slip wall boundary conditions include the rotor blades, stator blades, ducts, and hubs. According to Lu et al (2020), the time step is 5.208 × 10 −5 when the rotating speed R = 3200 rpm. Other time steps are 8.33 × 10 −5 , 6.41 × 10 −5 , 4.63 × 10 −5 , and 4.17 Compared to the study of the pure axial flow of the PJP, two methods can be used to numerically calculate the PJP under oblique flow conditions.…”

Section: Computational Domain and Meshmentioning

confidence: 99%

“…Other time steps are 8.33 × 10 −5 , 6.41 × 10 −5 , 4.63 × 10 −5 , and 4.17 Compared to the study of the pure axial flow of the PJP, two methods can be used to numerically calculate the PJP under oblique flow conditions. In the first method, the position of the rotating domain is adjusted to adapt to the different oblique flow angles of the PJP [20]. In the second method, the velocity component of the coming flow is adjusted [19,42].…”

Section: Computational Domain and Meshmentioning

confidence: 99%

“…The Bi-LSTM model can better consider the influence of before and after attributes of each attribute point in the sequence data than the single-LSTM model. The forward process of the Bi-LSTM model is denoted by Equations ( 15)- (20), and the backward process is the same as the forward process. Here, f () is the activation function, and y l k is the output of x l k .…”

Section: D-cnn Structure and Bi-lstm Structurementioning

confidence: 99%

“…The author used the panel method to establish a simulation model of the PJP steady-state flow field and proposed and verified a calculation model of the tip leakage vortex [18]. For the studies of oblique flow, they used the RANS method to analyze the velocity and pressure fields of PJPs under different oblique flow angles (0-20 • ) [19,20]. Their results showed that the oblique flow has a certain influence on the velocity field and torque of PJPs.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Prediction of Cavitation Performance over the Pump-Jet Propulsor Using Computational Fluid Dynamics and Hybrid Deep Learning Method

Qiu

Huang

Pan

2022

JMSE

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The cavitation performance of an oblique flow field is different from that under a pure axial flow field. This study analyzed the hydrodynamic performance, bearing force, and tip clearance flow field under different rotating speeds and different cavitation numbers in an oblique flow field. Furthermore, this study proposed a hybrid deep learning model CNN-Bi-LSTM to quickly and accurately predict the bearing force of a pump-jet propulsor (PJP), which will solve the problem of time-consuming calculation and consumption of considerable computing resources in traditional computational fluid dynamics. The Shear–Stress–Transport model and Reynolds-averaged Navier–Stokes equations were utilized to procure the training and testing datasets. The training and testing datasets were reasonably divided in the ratio of 7:3. The results show that the propulsion efficiency decreased more obviously under higher rotating speed conditions, with a maximum decrease of up to 13.59%. The small cavitation numbers 1.4721 and high oblique angle significantly impacted the efficiency reduction; the maximum efficiency loss exceeded 20%. Thus, a small cavitation number 1.4721 is extremely detrimental to the propulsion efficiency of the PJP due to the large cavitation area. Moreover, the intensity of the tip clearance vortex continuously increased with the rotating speed. The CNN-Bi-LSTM deep model successfully predicted the phase difference and trend change of the propulsor bearing force under different conditions. The prediction difference was large at the crest and trough of the bearing force, but it is within the acceptable error range.

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Attenuation of the Tip-Clearance Flow in a Pump-Jet Propulsor by Thickening and Raking the Tips of Rotor Blades: A Numerical Study

Dong

Yang

2021

Applied Ocean Research

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Numerical investigations of flow characteristics of a pumpjet propulsor in oblique inflow

Cited by 13 publications

References 24 publications

Influence of Various Stator Parameters on the Open-Water Performance of Pump-Jet Propulsion

Influence of Various Stator Parameters on the Open-Water Performance of Pump-Jet Propulsion

Prediction of Cavitation Performance over the Pump-Jet Propulsor Using Computational Fluid Dynamics and Hybrid Deep Learning Method

Attenuation of the Tip-Clearance Flow in a Pump-Jet Propulsor by Thickening and Raking the Tips of Rotor Blades: A Numerical Study

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