Fully Nonlinear Unsteady Three-Dimensional Boundary Element Method for Force Predictions on a Restrained Hull in Waves
There is a need from both academia and industry for more accurate and efficient methods for predicting the behavior of ships in a seaway. A large variety of methods with varying level of sophistication and accuracy already exists today. However, with the current trend of building very large ships and focus turning more and more towards safety, energy efficiency and fuel consumption, nonlinear phenomena are becoming more important and many of the traditional methods fail to capture the physics involved in the flow that determines the wave induced loads and ship motions. The overall objective of this project is the development and implementation of a fully nonlinear unsteady three-dimensional boundary element method (BEM) for ship motion analysis. As a first step, presented in this thesis, the performance of the method for the diffraction problem including forward speed effects is investigated before continuing with the second step: the inclusion of the equations of motion to allow for the analysis of the complete diffraction-radiation problem, i.e. ship motions. Results from two different validation cases are presented; the calm water wave resistance and wave making for the Series 60 hull and the wave induced loads on a Wigley hull making forward speed in regular long-crested waves. In the first case the results are compared with both experimental data as well as with numerical results from an existing and fully nonlinear steady-state method. In the second case the results are compared with experimental data only. The method has proved to perform well in the test cases employed. Concerning wave resistance, the current method performs equally well as the steady-state method. The performance in the prediction of wave induced forces is satisfactory and falls within the expectation. The trends are captured in a broad sense and force magnitudes are typically within 5% of experimental data although some data points show a larger discrepancy.