A semi-empirical multi-degree of freedom body force propeller model

Knight, Bradford G.; Maki, Kevin J.

doi:10.1016/j.oceaneng.2019.02.056

Cited by 10 publications

(11 citation statements)

References 13 publications

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“…This paper examines both a nonlinear regression model and a neural network that are trained and tested on unsteady RANS CFD results to determine the three-degree of freedom force of the propeller in open water and the behind condition. This is an extension of the authors' prior work in which a neural network was used to determine the three-degree of freedom force of the propeller [18].…”

Section: Introductionmentioning

confidence: 94%

“…The NMRI scale KCS propeller is examined in both open water and in the behind condition. The CFD used is very similar to that used by Knight & Maki [9,17], with the only difference of using an inlet condition on the sides of the domain when there is oblique flow. The OpenFOAM solver pimpleDyMFoam is used with the sliding mesh technique used for the unsteady rotating propeller simulations.…”

Section: Rans Cfd Setup and Specification Of Unsteady Motionmentioning

confidence: 99%

“…Therefore, a double body approximation is used. The mesh used in this study correlates to the coarse grid used by Knight & Maki [9,17]. The goal is for the model to be as accurate as the method that it is trained with.…”

Section: Rans Cfd Setup and Specification Of Unsteady Motionmentioning

confidence: 99%

“…The body force can be applied to the flow and to the vessel equations of motion. This approach can be used with a constant body force [6], a database driven model [7], Boundary Element Models [8], semi-empirical methods [9,10], or by using Blade Element Momentum Theory [5,[11][12][13]. Methods that ignore viscous effects may not be as accurate as modeling a fully discretized propeller with CFD in off design conditions.…”

Section: Introductionmentioning

confidence: 99%

“…The propeller examined is the National Maritime Research Institute (NMRI) scale Kriso Container Ship (KCS) propeller. Details of the CFD setup as well as the derivation for a semi-empirical propeller force method can be found in the authors' previous work [9,17]. This paper examines both a nonlinear regression model and a neural network that are trained and tested on unsteady RANS CFD results to determine the three-degree of freedom force of the propeller in open water and the behind condition.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Multi-Degree of Freedom Propeller Force Models Based on a Neural Network and Regression

Knight

Maki

2020

JMSE

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Accurate and efficient prediction of the forces on a propeller is critical for analyzing a maneuvering vessel with numerical methods. CFD methods like RANS, LES, or DES can accurately predict the propeller forces, but are computationally expensive due to the need for added mesh discretization around the propeller as well as the requisite small time-step size. One way of mitigating the expense of modeling a maneuvering vessel with CFD is to apply the propeller force as a body force term in the Navier–Stokes equations and to apply the force to the equations of motion. The applied propeller force should be determined with minimal expense and good accuracy. This paper examines and compares nonlinear regression and neural network predictions of the thrust, torque, and side force of a propeller both in open water and in the behind condition. The methods are trained and tested with RANS CFD simulations. The neural network approach is shown to be more accurate and requires less training data than the regression technique.

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Section: Introductionmentioning

confidence: 94%

Section: Rans Cfd Setup and Specification Of Unsteady Motionmentioning

confidence: 99%

Section: Rans Cfd Setup and Specification Of Unsteady Motionmentioning

confidence: 99%