A novel approach is demonstrated to approximate the effects of complex urban interference on the wind-induced surface pressure of tall buildings. This is achieved by decomposition of the domain into two components: the obstruction model (OM) of the static large-scale urban context, for which a single computational fluid dynamics (CFD) simulation is run; and the principal model (PM) of the isolated tall building under design, for which repeatable reduced-order model (ROM) predictions can be made. The ROM is generated with an artificial neural network (ANN), using a set of feature vectors comprising an input of local shape descriptors and a range of wind speeds from a training geometry, and an output response of pressure.
For testing, the OM CFD simulation provides the flow boundary condition wind speeds to the PM ROM prediction. The result is vertex-resolution surface pressure data for the PM mesh, intended for use within generative design exploration and optimisation. It is found that the mean absolute prediction error is around 5.0% (σ: 7.8%) with an on-line process time of 390 s, 27 times faster than conventional CFD simulation; considering full process time, only 3.2 design iterations are required for the ROM time to match CFD. Existing work in the literature focuses solely on creating generalised rules relating global configuration parameters and a global interference factor (IF). The work presented here is therefore a significantly alternative approach, with the advantages of increased geometric flexibility, output resolution, speed, and accuracy.