This study presents the experimental validation of regenerative wind farms (RGWFs), a novel wind farm concept designed to enhance overall wind farm performance. RGWFs employ multi-rotor systems with lifting devices (MRSLs), an innovative wind energy harvester engineered to stimulate strong vertical energy entrainment, thereby accelerating wake recovery. In the experiments, MRSLs are scaled for wind tunnel testing, with their rotors modeled using porous disks and their lifting devices represented by wings. The tested RGWFs comprise up to 3 × 3 MRSLs. Flow quantities within RGWFs and aerodynamic loads on MRSLs are measured using volumetric particle tracking velocimetry and strain gauges. Compared to conventional wind farms, flow analysis indicates that vertical energy entrainment is significantly enhanced in RGWFs, as evidenced by a more than 200 % increase in thrust on the second-row MRSLs and so on. These experimental results, which are in line with the previous numerical predictions, highlight the promising potential of RGWFs.

Accurate modelling of wind turbine wakes is critical for optimizing wind farm performance, but the complexity of wake interactions poses significant challenges. This study presents a two-phase blind test campaign, part of the Horizon Europe TWEET-IE project, designed to benchmark numerical models and investigate wake control strategies using wind tunnel experiments. Conducted with tandem wind turbine models at the Technical University of Munich and the National Technical University of Athens, the tests include inflow, load, power, and wake velocity measurements under controlled conditions. Phase I serves as an open-data benchmarking exercise for a baseline scenario without wake control, while Phase II introduces active individual blade pitch control to the upstream turbine, challenging participants to simulate advanced wake dynamics. This paper reviews Phase I results and details the experimental framework for Phase II, providing a foundation for advancing wake modelling and control in wind energy research.

The Reynolds number in an Urban Street Canyon is a difficult parameter to match between reduced-scale experiments and full-scale measurements. It is possible to overcome this mismatch in Reynolds numbers by satisfying the Reynolds number independence criterion, which states that above a certain critical Reynolds number, the flow field remains invariant with increasing Reynolds numbers. For an urban canyon with an aspect ratio 1, this critical Reynolds number is often reported to be 12000 for the mean flow quantities. This critical Reynolds number, however, is not applicable for higher-order quantities such as turbulence intensity and Reynolds stresses. In this study, 3D robotic particle tracking velocimetry was used to study the flow in an urban street canyon with an aspect ratio of 1 at several Reynolds numbers to estimate the critical Reynolds number for mean flow quantities and turbulence quantities. It was found that the critical Reynolds number for mean flow quantities was between Re = 13000 and Re = 17000. For the higher-order quantities, it was found that the critical Reynolds number was above 22000.