Multi-fidelity simulation modelling in optimization of a hybrid submarine propulsion system

Multi-objective Genetic Algorithms have become a popular choice to aid in optimising the size of the whole hybrid power train. Within these optimisation processes, other optimisation techniques for the control strategy are implemented. This optimisation within an optimisation requires many simulations to be run, so reducing the computational cost is highly desired. This paper presents an optimisation framework consisting of a series hybrid optimisation algorithm, in which a global search optimizes a submarine propulsion system using low-fidelity models and, in order to refine the results, a local search is used with high-fidelity models. The effectiveness of the Hybrid optimisation algorithm is demonstrated with the optimisation of a submarine propulsion system. © 2011 EPE Association - European Power Electr.

Coherent design methodology using modelling, simulation and optimisation

There is an increasing demand for optimising complete systems and the devices within that system, including capturing the interactions between the various multi-disciplinary (MD) components involved. Furthermore confidence in robust solutions is esential. As a consequence the computational cost rapidly increases and in many cases becomes infeasible to perform such conceptual designs. A coherent design methodology is proposed, where the aim is to improve the design process by effectively exploiting the potential of computational synthesis, search and optimisation and conventional simulation, with a reduction of the computational cost. This optimization framework consists of a hybrid optimization algorithm to handles multi-fidelity simulations. Simultaneously and in order to handles uncertainty without recasting the model and at affordable computational cost, a stochastic modelling method known as non-intrusive polynomial chaos is introduced. The effectiveness of the design methodology is demonstrated with the optimisation of a submarine propulsion system.