A Multi-Objective Collaborative Optimization Framework to Understand Trade-offs Between Naval Lifetime Costs Considering Production, Operation, and Maintenance.

摘要

The lifetime cost of naval vessels is an increasingly important factor to ship owners and, subsequently, to ship designers. A vessel's lifetime cost is composed of various cost categories such as production, operation, and maintenance. The impact of each of these categories is important and in many instances they may be competing with each other. Design decisions regarding the hull form and structure will dictate these costs, however, in what way decisions will impact them is difficult to understand. This is especially true for naval vessels as their service life is uncertain, and changes to the operational life of a vessel can have significant unforeseen costs with respect to maintenance and operation. In order to reduce the overall lifetime cost the trade-offs between these different categories must be understood. This thesis explores a linked resistance, production, and maintenance costing model and develops a novel enhanced multi-disciplinary optimizer capable of solving the resulting problem.;Most work in cost optimization has focused on reducing a single category of cost and considering other disciplines operational constraints at best. This type of sequential or single-discipline optimization does not reveal the trade-space to the designer and may result in non-optimal designs being developed when considering the full life-cycle cost of the vessel. Unfortunately understanding these trade-offs is difficult and traditional multi-objective optimization algorithms are unable to resolve the Pareto-fronts effectively. Presented here is a framework to aid designers in finding these trade spaces using a multi-disciplinary optimization environment.;In order to realistically represent the problem being solved a maintenance costing algorithm is developed that tracks physical damage throughout a ship's lifetime. Given that the design life of a vessel may be prolonged a probabilistic service life is implemented to account for this uncertainty. A hydrodynamic search method is also developed that facilitates efficiently searching large design spaces using a minimal number of design variables. These models allow for the development of trade-spaces that reflect the nuances of the naval design problem.;In order to utilize these models to understand the trade-offs in lifetime cost an enhanced multi-disciplinary optimization framework is developed. This algorithm uses novel techniques to facilitate solving this difficult design problem. The algorithm (eMOCO) is adopted from a multi-objective collaborative optimization framework with two enhancements. The first is the use of a decision support process, goal-programming, at the sub-system level in order to allow the discipline optimizers to reduce objective functions local to that discipline. This means that the discipline-level solutions that returned to the system-level optimizers are minimized with respect to their local variables. Secondly, a new single-objective genetic algorithm is developed specifically as a discipline-level optimizer in distributed MDO architectures. This novel GA, called the locally-elitist genetic algorithm (LEGA,) allows the discipline problem to be solved in a single execution of the discipline-level optimizer. These enhancements, tailored specifically to the naval design problem, facilitate solving for these difficult and unique trade-spaces.;This model is used to develop trade spaces between production, maintenance, and resistance in order to understand the interaction between the different categories of cost. The results show that the trade-spaces are difficult to fully resolve and the use of a multi-disciplinary environment is necessary. They also show that by developing the trade-spaces unique understanding into the interaction between cost categories can be found that allow an engineer to design ships that have minimal lifetime cost and are robust to changes in operation or service life.