Development of a Methodology for Simulation-Based Design of Ship Hull Forms
Abstract
Development of a Methodology for Simulation-Based Design of Ship Hull FormsWe propose to extend our research on the development of a methodology and associatedalgorithms for innovative hydrodynamic design of ship hull forms. The proposed methodologywill enhance an integrated computational tool that is aimed to accomplishing early-stagesimulation-based design of ship hull forms in terms of" hydrodynamic performance. The maincomponents of this computational tool consist of a hydrodynamic module, a hull surface modeling"" module, and an optimization module. We will further develop new algorithms and methods for each module, so that we can achieve the"" goal to create a design tool that can be used at early design stage to explore both conventional and non-traditional hull forms, to"" design new hull forms that exhibit both superior seakeeping and low resistance, to investigate innovative new-type hull forms with"" specific design constraints, to study the effect of the hull forms on the operability of a ship in a seaway. The results of this re"search will be useful to educate the next generation of naval architects by providing the methodology and analytical tools to allow an improved investigation of the relative importance of different hull parameters to combined analyses of resistance and seakeeping performance in waves. The proposed research on the development of a methodology for the early-stage simulation-based hydrodynamic design optimization of hull forms can serve as a key component in the multi-disciplinary optimization of ship design.We will furthe"r develop new algorithms in the hydrodynamic module to enhance the prediction of hydrodynamic performance, including the fast total" drag prediction techniques that account for other drag components in addition to wave drag and friction drag and the effect of sink"age and trim on the total drag, and the fast ship motion prediction methods that predict seakeeping and the operability of a ship in"" a seaway. We will further develop hull form representation, modification, and reconstruction techniques in the hull surface modelin""g module so that we can develop new hull forms from scratch, reconstruct an existing hull form in terms of hull parameters, generate"" bulbous bow and/or parallel midbody automatically, and conduct global or local modifications for ship hull form under given constra"ints. We will study the effects of various hull form modification techniques on the generation of optimal hull forms in terms of hyd"rodynamic performance. In the optimization module, we will further exam various optimization algorithms, including gradient based al""gorithm, genetic algorithms (GA), artificial bee colony algorithms (ABC), particle swarm optimization algorithms (PSO), and differen"tial evolution algorithms (DE). The gradient based algorithm can be used for a single objective ship hull form optimization to find the local optimal solution. The other four methods can be used for both single and multi-objective ship hull form optimization problems to find the global optimal solutions. We will investigate the effects of local and global optimization algorithms and different global optimization algorithms on the optimal hull form obtained. We will also improve our current Radial Basis Function based surr"ogate model and Kriging surrogate model, and develop new learning surrogate models for simulation-based optimization to accelerate t""he optimization process, so that the computational tool developed with these methods can be used at early stage of ship design.We" will implement all new algorithms in the computational tool to conduct systematic studies. We will apply machine learning technique"s in our study. Specifically, we will explore the hull forms for reduced drag and improved seakeeping, the hull forms for optimal op""erational envelopes, the hull forms for better hull and propulsor interaction, and the hull forms for enhanced dynamic positioning.
Document Details
- Document Type
- DoD Grant Award
- Publication Date
- Jul 07, 2017
- Source ID
- N000141712691
Entities
People
- Chi Yang
Organizations
- George Mason University
- Office of Naval Research
- United States Navy