This paper describes the development and application of a synthesis‐level multidisciplinary design and optimization (MDO) method for multihull ships. This method is unique in utilizing advanced multi‐objective optimization methods, neural network (NN)‐based response surface methods (RSM), and in its broad scope, integrating powering, stability, seakeeping, hullforms definition, structural weights, and cost and payload capacity into a single design tool. This MDO method is developed in the context of a multilevel hierarchy system approach where the results of the synthesis level optimization can be used for subsystem optimization and overall coordination of multilevel design system. NN‐based RSM for seakeeping and powering is developed and used in the optimization process. This RSM approach moves the computational cost of such performance evaluations out of the optimization cycle, substantially reduces the optimization cost, and allows for using results of physics‐based methods, such as advanced computational fluid dynamics, at the synthesis‐design level of design hierarchy. Details of these methods are delineated and multi‐objective optimization results are presented in the form of Pareto optimum solutions for multihull ship concepts such as trimaran sealift support ships and catamaran high‐speed connector ships.
The paper presents a method for reducing the cost of Computational Fluid Dynamics optimization by using a neural network to fill-in the design space. The method trains a network to approximate the aero-or hydrodynamic performance of vehicles with the Cascade Correlation algorithm. This network is coupled with a Genetic algorithm to optimize the hydrodynamic performance of the configuration.
I.U>, which, in CFD, is the aero-or hydrodynamic performance for a given configuration.A general optimization process is illustrated in Fig. 1. An initial set of design variables, which hight represent the configuration designed by experienced engineers, is supplied to the optimizer. Then, for this design, the objective function, f; is evaluated and the constraints, gi, are analyzed to check whether they are violated or not. If the optimum is not reached, these values are fed back to the optimizer that modifies the D.V.'s. The process is repeated until convergence.
This paper presents a neural network-based response surface method for reducing the cost of computer-intensive optimizations for applications in ship design. In the approach, complex or costly analyses are replaced by a neural network, which is used to instantaneously estimate the value of the function(s) of interest. The cost of the optimization is shifted to the generation of (smaller) data sets used for training the network. The focus of the paper is on the use and analysis of constructive networks, as opposed to networks of fixed size, for treating problems with a large number of variables, say around 30. The advantages offered by constructive networks are emphasized, leading to the selection and discussion of the cascade correlation algorithm. This topology allows for efficient neural network determination when dealing with function representation over large design spaces without requiring prior experience from the user. During training, the network grows until the error on a small set (validation set), different from that used in the training itself (training set), starts increasing. The method is validated for a mathematical function for dimensions ranging from 5 to 30, and the importance of analyzing the error on a set other than the training set is emphasized. The approach is then applied to the automated computational fluid dynamics-based shape optimization of a fast ship configuration known as the twin H-body. The classical approach yields a design improvement of 26%, whereas the neural network-based method allows reaching a 34% improvement at one fifth of the cost of the former. Based on the analysis of the results, areas for future improvements and research are outlined. The results demonstrate the potential of the method in saving valuable development cycle time and increasing the performance of future ship designs.
The paper presents a multi-disciplinary design/optimization method for the conceptual design of a hydrofoil based fast ship. The method is used to determine the maximum achievable lift-to-drag ratio (L/D) of an isolated foil-strut arrangement (hopefully greater than 50) at high transit speeds (greater than 75 knots) while lifting masses of 5,000 and 10,000 tons. First, the tools necessary for the study are presented. They comprise a panel method to compute three-dimensional flows around arbitrary configurations with a model for the free surface, a foil cross-section optimization tool, a strut cross-section design tool, and a structural analysis tool. The computational tools are then integrated into a multi-disciplinary design/optimization approach, which is applied to the design of single foil and biplane configurations. Results show that the goal of L/D = 50 is achievable for 75 knots (assuming that techniques can be developed for reducing the skin friction drag to a quarter of its nominal value) and, that for 90 knots, L/D ratios around 45 can be reached. The corresponding break horsepower requirements for 10,000 tons are around 130 khp and less than 200 khp, respectively.
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