AIRFOIL DATABASE SERIES

angle_of_attack, in degrees, used as input.

In that way, this problem has the 6 following variables: Here the number of variables (columns) is 6, and the number of instances (rows) is 1503. The file airfoil_self_noise.csv contains the data for this example. Which is the source of information for the approximation problem. The first step is to prepare the data set, The fundamental goal is to model the sound pressure level as a function of the airfoil features and airspeed. Therefore, this is an approximation project. The variable to be predicted is continuous (sound pressure level). To follow it step by step, you can use the free trial. This example is solved with Neural Designer. The span of the airfoil and the observer position was the same in all of the experiments. The NASA data set comprises different NACA 0012 airfoils at various wind tunnel speeds and angles of attack.

AIRFOIL DATABASE SERIES

It was obtained from a series of aerodynamic and acoustic tests of two and three-dimensional airfoil blade sections conducted in an anechoic wind tunnel. NASA processed the self-noise data set used in this example. Performance optimization can be applied to understand the behavior of airfoils and make designs with reduced noise. Which is due to the interaction between an airfoil blade and the turbulence produced in its boundary layer and near wake. The noise generated by an aircraft is an efficient and environmental matter for the aerospace industry.ДЄ vital component of the total airframe noise is the airfoil self-noise,

Predict the noise generated by airfoil blades using Neural Designer Airfoil self-noise prediction machine learning example

Airfoil database