The thesis proposes a methodology for a Surrogate Based Optimisation (SBO) study and its application in the Naval Architecture field using the open-source software DAKOTA. Surrogate Based Optimisation is a useful and powerful way of reducing time and costs in hull shape optimisation. It allows the employ of exploratory data analysis techniques and machine learning methods to get more insight, discover hidden patterns, and detect anomalies in the design space. However, an SBO consists of several interconnected steps, and each increases complexity and uncertainty to the overall process.

The main goal is the investigation of the effects of different surrogate models on ship geometry optimisation from a resistance point of view. Also, different sampling plans, infill techniques, and optimisation algorithms are analysed and compared, as elemental steps for an SBO. This is done by the use of test functions that emulate the problem of ship optimisation. Furthermore, the intent is to try and list general guidelines to follow when building up an SBO routine with CFD simulations involved, with a focus in the marine field.

This research led to the assembly of Halton sampling sequence, Kriging meta-model, Expected Improvement function, Genetic Algorithms, and Pattern Search tools to implement a working SBO routine. This routine is used to demonstrate the success of the SBO method for a Hull Vane and an aft ship optimisations. Moreover, it is used to validate the research outcomes of this thesis and to prove that this work can be safely used for every-day commercial work.

The two design applications provided a resistance reduction, with respect to a benchmark hull, of about 19% and 7%. Thus not only they were successful, but also they were excellent examples to show how a surrogate model and its correct visualisation can give the Naval Architect the right tools to critically analyse the results, gain more understanding on the problem, spot and correct anomalies, and provide creative solutions to a client.