Development of fast computational methods for tsunami modelling

The work presented in this thesis focuses on the development of fast computational methods for modelling tsunamis. A large emphasis is placed on the newly redeveloped tsunami code, Volna-OP2, which is optimised to utilise the latest high performance computing architectures. The code is validated/verified against various benchmark tests. An extensive error analysis of this redeveloped code has been completed, where the occurrence and relative importance of numerical errors is presented. The performance of the GPU version of the code is investigated by simulating a submarine landslide event. A first of its kind tsunami hazard assessment of the Irish coastline has been carried out with Volna-OP2. The hazard is captured on various levels of refinement. The efficiency of the redeveloped version of the code is demonstrated by its ability to complete an ensemble of simulations in a faster than real time setting. The code also forms an integral part of a newly developed workflow which would allow for tsunami warning centres to capture the uncertainty on the tsunami hazard within warning time constraints. The uncertainties are captured by coupling Volna-OP2 with a computationally cheap statistical emulator. The steps of the proposed workflow are outlined by simulating a test case, the Makran 1945 event. The code is further utilised to validate and expand upon a new analytical theory which quantifies the energy of a tsunami generated by a submarine landslide. Some preliminary work on capturing the scaling relationships between the parameters of the set up and the tsunami energy has been completed. Transfer functions, which are based upon extensions to Green's Law, and machine learning techniques which quantify the local response to an incoming tsunami are presented. The response, if captured ahead of time, would allow a warning centre to rapidly forecast the local tsunami impact. This work is the only chapter in the thesis which doesn't draw upon Volna-OP2, but nevertheless showcases another fast computational method for modelling tsunamis