A novel approach based on artificial neural network for calibration of multi-hole pressure probes

Imperfections in the manufacturing process of flow measuring probes affect their measuring behavior. Nevertheless, in order to provide the highest possible accuracy, each individual multi-hole pressure probe has to be calibrated before using them in turbomachinery. This paper presents a novel method based on artificial neural networks (ANN) to predict the flow parameters of multi-hole pressure probes. A two-stage ANN approach using multilayer perceptron (MLP) is proposed in this study. The two-stage prediction approach involves two MLP networks, which represent the calibration data and the prediction error. For a given set of inputs, outputs from both networks are combined to estimate the measured value. The calibration data of a 5-hole probe at RWTH Aachen was used to develop and validate the proposed ANN models and two-stage prediction approach. The results showed that the ANN can predict the flow parameters with high accuracy. Using the two-stage approach, the prediction accuracy was further improved compared to polynomial functions, i.e. a commonly used method in probe calibration. Furthermore, the proposed approach offers high interpolation capabilities while preventing overfitting (i.e. failure to fit new data). Unlike polynomials, it is shown that the ANN based method can provide accurate predictions at intermediate points without large oscillations.