These equations propagate causality backwards through the computation, from the power extracted back to the turbine positions and the tuning parameters.
The gradient of the power extracted with respect to the turbine positions and their tuning parameters is then computed in a fraction of the time taken for a flow solution by solving the associated adjoint equations. In each optimisation iteration, a two-dimensional finite element shallow water model predicts the flow and the performance of the current array configuration. The novel contribution of this paper is the formulation of this problem as an optimisation problem constrained by a physical model, which is then solved using an efficient gradient-based optimisation algorithm.
#Bump function gmsh meaning manual#
However, manual optimisation is difficult due to legal site constraints, nonlinear interactions of the turbine wakes, and the cubic dependence of the power on the flow speed. This naturally leads to the question of how these turbines should be configured to extract the maximum possible power: the positioning and the individual tuning of the turbines could significantly influence the extracted power, and hence is of major economic interest. In order to extract an economically useful amount of power, hundreds of tidal turbines must typically be deployed in an array.
Tidal stream generators are one of the key technologies for extracting and harnessing this potential. Oceanic tides have the potential to yield a vast amount of renewable energy.
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