A Pareto Frontier for Full Stern Submarines via Genetic Algorithm


An exploratory design and analysis code for underwater vehicles with ducted, multi-stage propulsion systems is developed from an existing version. Boundary layer modeling is added and used to predict flow separation as well as to estimate the effect of wake fraction on propulsive efficiency. Force balance (i.e., convergence to a self-propelled condition) is achieved by automatic variation of advance coefficient. The force and boundary layer calculations of the revised code are validated using published experimental data. A slightly modified version of the code is then used as the evaluator for an original Pareto genetic algorithm. The algorithm seeks the code's Pareto(non-dominated) frontier in terms of usable hull volume and propulsive efficiency, with the intention of investigating the feasibility of so-called ``full stern'' submarines. Three different methods of Pareto selection, drawn from the current literature, are installed in the algorithm and compared in terms of their ability to locate and define the frontier. A new concept in evolutionary computation---non-interbreeding competitive species---is introduced, allowing simultaneous optimization of four incompatible propulsor configurations. This produces a feasibility frontier with optimal propulsor configuration as a function of stern fullness. The ability of the algorithm to define a three-objective Pareto surface is demonstrated, by including minimal cavitation as a third objective. The results provide evidence for the viability of full stern submarines, demonstrate the utility of genetic algorithms in obtaining Pareto design frontiers, and show that Pareto optimization is preferred to scalarized multi-objective optimization in general decision-making.