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This study aims to short-therm forecasting of power generation output for this purpose, an adaptive neuro-fuzzy inference system (ANFIS) is designed to forecast the output power of power plant based on climate factors considering wind speed and wind direction simultaneously.

Several methods and algorithms have been proposed for systems forecasting in various fields. One of the strongest methods for modeling complex systems is neuro-fuzzy that refers to combinations of artificial neural network and fuzzy logic. When the system becomes more complex, the conventional algorithms may fail for network training. In this paper, an integrated approach, including ANFIS and metaheuristic algorithms, is used for increasing forecast accuracy.

Power generation in power plants is dependent on various factors, especially climate factors. Operating power plant in Iran is very much influenced because of climate variation, including from tropical to subpolar, and severely varying temperature, humidity and air pressure for each region and each season. On the other hands, when wind speed and wind direction are used simultaneously, the training process does not converge, and the forecasting process is unreliable. The real case study is mentioned to show the ability of the proposed approach to remove the limitations.

First, ANFIS is applied for forecasting based on climate factors, including wind speed and wind direction, that have rarely been used simultaneously in previous studies. Second, the well-known and more widely used metaheuristic algorithms are applied to improve the learning process for forecasting output power and compare the results.

In this paper, experimental and numerical results of a lambda wing have been compared. The purpose of this paper is to study the behaviour of lambda wings using a CFD tool and to consider different numerical models to obtain the most accurate results. As far as the consideration of numerical methods is concerned, the main focus is on the evaluation of computational methods for an accurate prediction of contingent leading edge vortices’ path and the flow separation occurring because of the burst of these vortices on the wing.

Experimental tests are performed in a closed-circuit wind tunnel at the Reynolds number of 6 × 105 and angles of attack (AOA) ranging from 0 to 10 degrees. Investigated turbulence models in this study are Reynolds Averaged Navior–Stokes (RANS) models in a steady state. To compare the accuracy of the turbulence models with respect to experimental results, sensitivity study of these models has been plotted in bar charts.

The results illustrate that the leading edge vortex on this lambda wing is unstable and disappears soon. The effect of this disappearance is obvious by an increase in local drag coefficient in the junction of inner and outer wings. Streamlines on the upper surface of the wing show that at AOA higher than 8 degrees, the absence of an intense leading edge vortex leads to a local flow separation on the outer wing and a reverse in the flow.

Results obtained from the behaviour study of transition (TSS) turbulence model are more compatible with experimental findings. This model predicts the drag coefficient of the wing with the highest accuracy. Of all considered turbulence models, the Spalart model was not able to accurately predict the non-linearity of drag and pitching moment coefficients. Except for the TSS turbulence model, all other models are unable to predict the aerodynamic coefficients corresponding to AOA higher than 10 degrees.

The presented results in this paper include lift, drag and pitching moment coefficients in various AOA and also the distribution of aerodynamic coefficients along the span.

The presented results include lift, drag and pitching moment coefficients in various AOA and also aerodynamic coefficients distribution along the span.