In this work, the objective was to investigate the influence of Active Flow Control on the improvement of a DU96-W-180 airfoil aerodynamic performance. A numerical simulation was done for incompressible unsteady low Reynolds Number flow at high angle of attack. The innovative approach was the use of an "Active Slat" where the periodic blowing effect was achieved by periodically opening and closing the slat passage. The major benefit of this concept is being flexible to a desired operating condition. A new OpenFOAM ® solver was developed from the existing pisoFoam solver to simulate the active slat flow control technique. To get the best aerodynamic performance, the active slat should operate at the domain dominant frequencies. A Fast Fourier Transform (FFT) was performed to achieve the optimum slat excitation frequency. These frequencies will help in controlling the inherent instabilities in the boundary layer and thus improving the aerodynamic performance. Finally, active flow control simulations were applied using different excitation. Using the optimum FFT excitation frequency ( 0.68 f = in the wake region) yields the best aerodynamic improvement of all tested frequencies. Improvements in lift coefficient were achieved up to 8%. Hence, the slatted airfoil is superior to the conventional clean configuration airfoil.

Kites can be used to harvest wind energy at higher altitudes while using only a fraction of the material required for conventional wind turbines. In this work, we present the kite system of Kyushu University and demonstrate how experimental data can be used to train machine learning regression models. The system is designed for 7 kW traction power and comprises an inflatable wing with suspended kite control unit that is either tethered to a fixed ground anchor or to a towing vehicle to produce a controlled relative flow environment. A measurement unit was attached to the kite for data acquisition. To predict the generated tether force, we collected input–output samples from a set of well-designed experimental runs to act as our labeled training data in a supervised machine learning setting. We then identified a set of key input parameters which were found to be consistent with our sensitivity analysis using Pearson input–output correlation metrics. Finally, we designed and tested the accuracy of a neural network, among other multivariate regression models. The quality metrics of our models show great promise in accurately predicting the tether force for new input/feature combinations and potentially guide new designs for optimal power generation.

Abstract. In wind energy research, airborne wind energy systems are one of the promising energy sources in the near future. They can extract more energy from high altitude wind currents compared to conventional wind turbines. This can be achieved with the aid of aerodynamic lift generated by a wing tethered to the ground. Significant savings in investment costs and overall system mass would be obtained since no tower is required. To solve the problems of wind speed uncertainty and kite deflections throughout the flight, system identification is required. Consequently, the kite governing equations can be accurately described. In this work, a simple model was presented for a tether with a fixed length and compared to another model for parameter estimation. In addition, for the purpose of stabilizing the system, fuzzy control was also applied. The design of the controller was based on the concept of Mamdani. Due to its robustness, fuzzy control can cover a wider range of different wind conditions compared to the classical controller. Finally, system identification was compared to the simple model at various wind speeds, which helps to tune the fuzzy control parameters.

In this study, the aim was to reduce the complexity of the costly non-linear unsteady partial differential equations governing the aerodynamic flows into a simpler lower-dimensional model. Modal decomposition method; namely Proper Orthogonal Decomposition (POD) was applied in conjunction with the Modified Linear Stochastic Measurement (MLSM) to achieve a reduced order model with high accuracy and low computational cost. The methods were applied to the surface pressure values of a DU96-W180 Wind Turbine Airfoil with emphasis on stall control application. It was found that using only three POD modes, most of the system energy (up to 99%) was captured where the reconstructed pressure distribution matched the CFD one obtained from OpenFOAM simulations. Besides, using only two pressure probes, one upstream and the other downstream, the surface pressure field was reconstructed with high accuracy. This application is important in reducing the computational time from several hours to just few seconds for applications involving recursive solution of the Navier-Stokes equations.