Case study

Array layout

We aim to improve efficiency of design process for large offshore wind projects.


Until recently nobody had tried planning an offshore wind project the size of a small country. The Crown Estate announced the first round of UK offshore wind farm development in 2000, and in 2008 Round 3 was announced.

The announcement offered the potential for thousands of turbines spread across huge expanses of UK waters. But how do you design a turbine array for an area this large? This increase in scale has challenged conventional thinking around installation, operation and performance.

Ambitious project timescales have forced developers to find ways to quickly understand what the key design issues are for such large, remote wind farms. This understanding must be applied to establish the cost and energy yield predictions before projects can start.


Our Solution

Frazer-Nash has a wealth of experience in developing and applying computational models that can resolve many of these issues, and provide dependable insight, quickly.

Our multi-disciplinary approach to wind farm design1 demonstrates the impact that individual aspects can have on the design. For example, if you optimise the turbine layout will the energy yield exceed the cost increase of any additional cabling?

The approach works by parametrically defining array layouts, then calling a series of more specialised analytical models to evaluate their potential. Results are presented to the designer using techniques such as response surface modelling (below right) to highlight trends. This allows the design space to be explored quickly and thoroughly.

But these results are of no use without a sound scientific background to the modelling. We work hard to establish confidence in the models we use. For example, we have used data from some of today's largest offshore arrays to validate models that predict aerodynamic losses, caused by turbines operating in the wakes of others.

Using our knowledge of fluid dynamics to interpret the results in the context of larger arrays, we were able to identify the best models to use for new, larger designs and to suggest how these models could be developed to improve speed and accuracy.



  • Focus areas for design development are quantified making them easier to understand
  • Improved understanding of the significance of design compromises - making them quicker to resolve
  • Modelling can be readily adapted to refine fidelity as the design progresses
  • Multi-discipline design issues can be quickly explored delivering significant cost savings.


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