Despite the widespread research and development of HAWTs in recent times, VAWTs are gaining in popularity due to certain critical advantages they provide, for example, wind direction independency. While most existing studies focused on analysing the performance of VAWT using NACA aerofoils, this study compares the performance of NACA0018 and S1046 aerofoil profiles for a range of Speed Ratios (TSRs) and blade pitch angles. It has been found that the S1046 is less sensitive to changes in wind speed, and is thus, a superior choice for urban applications where the wind speed is comparatively low and varies a lot. Three bladed VAWTs of solidity 0.1 was modelled using Solidworks for this study. The CFD simulations were then performed in ANSYS Fluent, utilising the k-ω SST turbulence model. The model was validated at first before analysing the VAWT performance with the intended aerofoils. Key results indicate that increasing the TSR leads to increases in aerodynamic performances for nearly all cases, and especially so, for lower blade pitch angles. However, this study concludes that VAWT consisting of S1046 aerofoils at -2 degrees of blade pitch and operating at TSR 4 will provide the optimum performance.

An Artificial Neural Network is a well-known AI technique for replicating human brain and offering suitable solution for any unpredictable complicated problem. Taking the advantage of it, this research will analyse the applicability of Neural Network Controller for ship manoeuvring, such as course changing. To train the controller, optimized teaching data are used to keep the consistency in the data as it could enhance the learning ability of the controller while training. A double layered feed-forward neural network and back propagation method are found suitable for this purpose. Later-on, simulations are done to justify the effectiveness of the trained controller for unknown situations

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