Optimisation of city structures with respect to high wind speeds using U-Net models

The design and placement of a new building in an urban environment is optimised so that pedestrians do not feel uncomfortable due to high wind speeds. Architects and city planners typically use Computational Fluid Dynamics (CFD) simulations to predict the effects of wind on new buildings. CFD is too time consuming to be used in generative design tools, which are necessary because they allow designers to visualise and iterate on their ideas, collaborate effectively and work more efficiently. These tools enhance the design process, facilitate communication and feedback, and save time by automating tasks. Finding a generative design model based on limited data is challenging due to issues such as insufficient representation, increased risk of overfitting, lack of statistical significance, potential bias and difficulties in validation and evaluation. In the design framework proposed in this paper, the computationally intensive CFD solver could be replaced by a fast machine learning surrogate predicting wind speed. The replacement is a U-Net convolutional neural network trained on high-precision Reynolds’ averaged Navier-Stokes (RANS) simulations (CFD simulations). The proposed workflow is demonstrated by optimising the placement of a hypothetical new building on a city square in Gothenburg, Sweden. The surrogate used in the optimisation process calculates an optimal building design within seconds instead of hours saving 11 h of simulation time. The actual area with strong wind effects is reduced by half.