Investigation of radial turbines for wind energy harvesting

Abstract

Wind energy has been a research focus of the last few decades, promoting the topic to a much more matured level. However, the body of literature is built around a limited number of turbine types and it is still beneficial to increase turbine diversity for the opportunity to select optimal turbine for a given purpose. The radial (inflow) turbines known in gas and hydro turbine industries for pressurized pipe flows are undiscovered turbines for free-stream wind harvesting both in air and underwater. In such case, the wind tangentially interacts with each blade by an external casing; therefore, all blade angular positions may, at least in theory, contribute equally to power production. Enclosed compact architecture with low rotational speeds and narrow intake ports may possess advantages in enabling the use of much weaker but more recyclable and long-lasting blades and in applications such as integrated wind harvesting for blunt bodies like buildings, vessels and devices or for any application where an open rotor is not feasible. Using experimentally validated Reynolds-averaged Navier–Stokes simulations, mass swallowing capacities and corresponding efficiencies of such turbines are parametrically investigated at low Reynolds numbers, Re chord=11 × 103, and finally enhancement is shown in 88 × 103. Findings provide first insights into such turbines. Considering the investigated very low Reynolds numbers and three-dimensional effects, comparable performance with other small turbines are observed. Moreover, undiscovered potential for performance optimization persists.

A Comprehensive Review on Development and Applications of Cross-Flow Wind Turbines

“…Respectively, 26% and 40% increases in rotor angular velocity were observed. A 3D study [143] (Figure 23) investigated mass swallowing capacities and corresponding efficiencies to the proposed casings and blade profiles at low Reynolds numbers. Another design investigation [144] was pursued by machine learning algorithms coupled with CFD, and at first, sensitivity analysis revealed the most important parameters and then determined their optimal value.…”