Feedforward neural networks applied to problems in ocean engineering

The Maneuvering and Control Division of the Naval Surface Warfare Center, Carderock Div. (NSWCCD) along with Applied Simulation Technologies have been developing and applying feedforward neural networks (FFNN) to problems of naval interest in Ocean Engineering. A selection of these will be discussed. Together, they show the power of the nonlinear method as well as its utility in diverse applications. Experimental data describing a subset of the B-Screw series of propellers operating in all four quadrants have been reported by MARIN in the Netherlands. The data contain varying pitch to diameter ratios, expanded area ratios, number of blades and advance-angle. These four variables were used to train a FFNN to predict the four-quadrant thrust and torque characteristics for the entire B-screw series over a range of beta from 0 to 360 deg. The results show excellent agreement with the existing data and provide a means for estimating 4-quadrant performance for the entire series. For submarine simulation and design, knowledge of the total forces and moments acting on the hull as a function of angle-of-attack, sideslip angle and dimensionless turning rate across a large parameter space is required. This data is acquired experimentally and/or numerically and can be used to train a FFNN to act as a Virtual Tow Tank or Hirtual CFD Code. The network not only recovers the training data but also serves as a very fast, nonlinear six degree-of-freedom look-up table of the forces and moments acting on the hull throughout the parameter space described by the vehicle dynamics. Example solutions demonstrating this approach will be presented. Wave impact loads pose continuing problems for vessels in high sea states, with damage to hatches and appendages, suggesting that these loads may be greater than current design guidelines. Such forcing is complex and often difficult to estimate numerically. Experimental data were acquired at NSWC to measure the hydrodynamic loads of regular, non-breaking waves on a plate and a cylinder while varying incident wave height, wavelength, wave steepness, plate angle and immersion level of the plate/cylinder. Predictions of wave impact forces from a FFNN trained on the experimental data will be presented.