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

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ID: 272620
2020
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Abstract
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.
Reference Key
knight2020journalmulti-degree Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Bradford Knight;Kevin Maki;Knight, Bradford;Maki, Kevin;
Journal journal of marine science and engineering
Year 2020
DOI
10.3390/jmse8020089
URL
https://www.mdpi.com/2077-1312/8/2/89
Keywords
Machine learning Neural network regression propeller body force propeller-hull interaction

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