The available computational power is continuously increasing, however, the investigated systems in the engineering domain tend to get more complex as well. This makes it necessary to find other approaches for reducing the computational expenses of computer aided simula- tions. The emerging technology of meta modelling allows to handle computations more effi- ciently. It tries to minimize the used computational resources by reducing the required amount of sample points to get a level of prediction which is accurate enough for the given application. In this thesis, different methods and workflows were designed to optimize the techno-economic layout of hydro turbines in an early design phase. First, a Python model was developed to allow predictions of the turbines main dimensions based on empirical values. Further, a VBA code allows a quicker choice of a reference turbine, which is then used in a simulation model to maximize its efficiency. A workflow was then developed to enable the user to run simulations with different objectives. This workflow can be used to find a maxi- mum for certain hydro turbine parameters or to conduct sensitivity analysis to see which in- put parameter has the biggest effect on the desired output. By further refining the methods and by including the novel approach of machine learning a faster and less error prone predic- tion of turbine dimensions and its efficiency could be possible. All this can help decision mak- ers to satisfy the increasing demand of economic stability for investors and the need to esti- mate a possible ecological impact by knowing earlier what civil engineering works are re- quired due to the major factor of turbine dimensions.     «

The available computational power is continuously increasing, however, the investigated systems in the engineering domain tend to get more complex as well. This makes it necessary to find other approaches for reducing the computational expenses of computer aided simula- tions. The emerging technology of meta modelling allows to handle computations more effi- ciently. It tries to minimize the used computational resources by reducing the required amount of sample points to get a level of predictio...     »