The noise generated by an aircraft is an efficiency and environmental matter for the aerospace industry. An important component of the total airframe noise is the airfoil self-noise, which is due to the interaction between an airfoil blade and the turbulence produced in its own boundary layer and near wake. The next figure illustrates the noise generated by an aircraft.
The first step is to prepare the data set, which is the source of information for the approximation problem.
The self-noise data set used in this example was processed by NASA, and so it is referred here to as the NASA data set. 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. The NASA data set comprises different size NACA 0012 airfoils at various wind tunnel speeds and angles of attack. The span of the airfoil and the observer position were the same in all of the experiments.
The file airfoilselfnoise.dat contains the data for this example. The input to Neural Designer is a data set, which can have different formats (CSV, XLS, etc.). Decimal marks should be points, not commas. A preview of the contents of the airfoilselfnoise.dat file are listed below. Here the number of instances (rows) is 1503, and the number of variables (columns) is 6.
The first step is to edit the data set information. It is composed of:
The following figure shows the data set page in Neural Designer.
In that way, this problem has the following variables:
The NASA data set contains 1503 instances. For that purpose, the data is divided at random into training, validation and testing subsets, containing 60%, 20% and 20% of the instances, respectively. More specifically, 753 samples are used here for training, 375 for validation and 375 for testing. Note that, as the number of instances is big (more than 1000), the instances information table is not shown in the data set page. Indeed, that table would need too much space and memory.
Once all the data set information has been set, we are ready to run some related tasks, in order to check for the quality of the data.
The "Calculate data statistics" task calculates the minimums, maximums, mean and standard deviation of all variables.
On the other hand, the "Calculate data histograms" task shows how the data is distributed. The next figure depicts the histogram for the frequency. As we can see, most of the NASA data set contains noise at low frequencies.
The next figure is the histogram for the only target variable, the sound level. We can see that it is well distributed.
For this approximation example, the deep architecture page is composed by:
The next figure shows the neural network page in Neural Designer.
The scaling layer section contains the statistics on the inputs calculated from the data file and the method for scaling the input variables. Here the minimum and maximum method has been set. Nevertheless, the mean and standard deviation method would produce very similar results.
Here a neural network with a sigmoid hidden layer and a linear output layer is used. This neural network must have 5 inputs and 1 output neuron. The number of hidden neurons has been chosen to be 5. The resulting number of parameters in this neural network is 36.
The unscaling layer contains the statistics on the outputs calculated from the data file and the method for unscaling the output variables. Here the minimum and maximum method will also be used.
The outputs from the neural network are those variables set as target in the data set page:
The neural network for this example can be plotted as a graph as follows.
This neural network defines a function of the form
That function is parameterized by the biases and synaptic weighs of the neural network.
The objective term is to be the normalized squared error. It divides the squared error between the outputs from the neural network and the targets in the data set by a normalization coefficient. If the normalized squared error has a value of unity then the neural network is predicting the data 'in the mean', while a value of zero means perfect prediction of the data. This objective term does not have any parameters to set.
The neural parameters norm is used as regularization term. It is applied to control the complexity of the neural network by reducing the value of the parameters. The weight of this regularization term in the loss index is 0.001.
The learning problem can be stated as to find a neural network which minimizes the loss index, i.e., a neural network that fits the data set (objective) and that does not oscillate (regularization).
The fourth step is to edit the training settings. The next screenshot shows the training strategy page for this example.
As we can see, the training strategy chosen is the quasi-Newton method.
The "Perform training" task trains the neural network. The following chart shows how the performance decreases with the iterations during the training process. The initial value is 9.47968, and the final value after 93 iterations is 0.330033.
The next table shows the training results for this application. Here the final parameters norm is not very big, the final performance and generalization performance are small, and the final gradient norm is next to zero.
The next step is to test the generalization performance of the trained neural network. Testing compares the values provided by this technique to the actually observed values.
A possible testing technique for the neural network model is to perform a linear regression analysis between the predicted and their corresponding experimental residuary resistance values, using and independent testing set. This analysis leads to a line y = a + bx with a correlation coefficient R2. In this way, a perfect prediction would give a = 0, b = 1 and R2 = 1. The "Perform linear regression analysis" task does that. The following table shows the three parameters given by this testing analysis.
The next figure illustrates a graphical output provided by this testing analysis. The predicted residuary resistances are plotted versus the experimental ones as open circles. The solid line indicates the best linear fit. The dashed line with R2 = 1 would indicate perfect fit.
From the table and the figure above we can see that the neural network is predicting well the entire range of sound level data. Indeed, the a, b and R2 values are close to 0, 1 and 1, respectively.
The model is now ready to estimate the self-noise of airfoils with satisfactory quality over the same range of data.
The "Calculate output" task calculates the output value for a given set of input values.
The "Plot directional output" task plots the sound level as a function of a given input, for all other inputs fixed. The next plot shows the output Sound level as a function of the input Frequency, through the point (*\10\0.15\50\0.03). The x and y axes are defined by the range of the variables Frequency and Sound level, respectively. Note that some directional outputs fall outside the range of Sound level, and therefore they are not plotted.
The explicit expression for the residuary resistance model obtained by the neural network is obtained by clicking the "Write expression" neural networtk task. It gives the following results: