Improvement of the five-hole probe calibration using artificial neural networks. (August 2022)

• Record Type: Journal Article
• Authors: Fathi, Saeed; Sadeghi, Hosein
• Is Part Of: Flow measurement and instrumentation. Volume 86(2022)
• Publication Date: 2022-08
• Subjects: Five-hole probe -- Calibration -- Artificial neural networks -- Data reduction -- Wind tunnel -- Radial basis function; Fluid dynamic measurements -- Periodicals; Flow meters -- Periodicals; Fluides, Dynamique des -- Mesure -- Périodiques; Débitmètres -- Périodiques; 681.2805
• DOI: 10.1016/j.flowmeasinst.2022.102189
• Languages: English
• ISSNs: 0955-5986

Abstract

Abstract: In the present study, the artificial neural networks (ANNs) technique was implemented to link non-dimensional pressure coefficients and flow characteristics to calibrate a five-hole probe. The experimental data of this work were obtained from a subsonic open-circuit wind tunnel at the velocity of 10 m/s. Here, the efficiency of ANNs was compared with two conventional data reduction methods, including linear interpolation technique and 5 th -order polynomial surface fit algorithm. Based on the statistical parameters of calibration data, it was concluded that the radial basis function (RBF) algorithm was more accurate and had more flexibility compared to the multi-layer perceptron (MLP) regression algorithm, the linear interpolation and 5 th -order polynomial methods. In the RBF method, the mean absolute errors of 0.11, 0.64, 0.02 and 0.03 were achieved for α, β, Cp t and Cp s, respectively. Furthermore, the effects of training data reduction and data selection on the performance of RBF were studied. The accuracy of the proposed RBF method was analyzed at different α angles and for random test data. Finally, the influence of increasing number of test data on the efficiency of calculated RBF method was evaluated. Highlights: Performance of RBF and MLP neural networks were compared in five-hole probe calibration. RBF method showed better performance than MLP. RBF method was further compared with conventional data reduction methods. Test data had nonlinear relation with training data. Effects of reduction of training data on accuracy of calibration methods were investigated.