###### Abstract  
The human migration from rural to urban areas has triggered a chain reaction causing the spiking energy demand of cities worldwide. High-rise buildings filling the urban skyline could potentially provide a means to improve the penetration of renewable wind energy by installing wind turbines at their rooftop. However, the above roof flow region has not received much attention and most results deal with low-rise buildings. This study investigates the flow pattern above the roof of a high-rise building by analysing velocity and pressure measurements performed in an atmospheric boundary layer wind tunnel, including four wind directions and two different roof shapes. Comparison of the surface pressure patterns on the flat roof with available low-rise building studies shows that the surface pressure contours are consistent for a given wind direction. At 0\({}^{\circ}\) wind direction, a separation bubble is detected, while cone vortices dominate at 30\({}^{\circ}\) and 45\({}^{\circ}\). The determining factor for the installation of small wind turbines is the vicinity to the roof. Thus, 45\({}^{\circ}\) wind direction shows to be the most desirable angle by bringing the substantial amplification of wind and keeping the turbulence intensity low. Decking the roof creates favourable characteristics by overcoming the sensitivity to the wind direction while preserving the speed-up effect. wind tunnel experiments; velocity measurements; pressure measurements; urban wind energy harvesting +
Footnote †: journal: Applied Soft  
standards and sustainability [2]. This unprecedented societal change goes in parallel with a meteoric rise in the energy demand, estimated to increase by 56% before 2040 [3]. Renewable energy needs to play a crucial role to meet the demand without affecting urban resilience and sustainability. Wind energy represents the most successful source of renewable energy, due to its steadily increasing reliability, ever-reducing costs and economical and environmental feasibility [4]. While the predominant wind energy production comes from large wind farms, having received enormous resources to optimise their output [5], an opportunity may arise from distributed generation [6], with small wind turbines installed in optimal locations close to consumers. A recent study showed that, in a hypothetical scenario, having small wind turbines installed on 1500 high-rise buildings in major cities in the Netherlands, urban wind energy has the potential to annually yield 150 GWh of electricity, i.e., enough to power up \(\sim\)60,000 typical Dutch households [7]. Advocates of urban wind energy harvesting support it due to the perspective of having ready-to-use energy at the consumption place, eliminating a significant cost contribution in wind farms [8,9]. Some authors have attempted to estimate the potential of generating wind energy in cities, using the annual wind speed variability and the typical installation height as reference [10,11]. Although results suggest that small-scale wind turbines are only to be installed on the outskirts of cities, the local flow conditions are normally not considered for such evaluations, hence these studies have a limited scope. The ideal method to assess the local wind flow pattern above buildings would require to directly perform measurements on site, as done for wind farms. This is a rather expensive solution, which is not yet justified in terms of the expected yield of urban wind energy [12,13]. However, other approaches can be used for the assessment of the wind resource in urban areas, including atmospheric boundary layer wind tunnel testing or computational fluid dynamics (CFD). In recent years, CFD has successfully provided a way for the determination of mean flow conditions and it has contributed to the understanding of the wind flow above buildings. However, the accuracy and reliability of the CFD solutions remain a concern that requires further validation efforts [13,14]. Thus, wind tunnel experimentation is still considered to be the most effective approach [13].