Void Fraction Measurement of Gas-Liquid Two-Phase Flow Based on Empirical Mode Decomposition and Artificial Neural Networks

A new void fraction estimation method for gas–liquid two-phase flow combining two differential pressure (DP) signals acquired from a single Venturi tube and based on Empirical Mode Decomposition (EMD) and Artificial Neural Networks (ANN) was experimentally investigated. In order to study gas–liquid distribution in horizontal pipes, two DP signals from the top and bottom sections of the Venturi tube are acquired and EMD is adopted to extract stable and fluctuating components of the DP signals. Experimental data revealed that fluctuating index increases nearly linearly with increasing void fraction when void fraction is less than 0.4. When void fraction is larger than 0.4, this near-linearity ceases. A combination of ANN method and the fluctuating index of DP signals is developed to estimate void fraction. Experimental results show that void fraction based on DP signal from top section of Venturi tube is overestimated because of clustering of bubbles and the scarcity of liquid information when gas–liquid mixture velocity is low. Void fraction is underestimated when the mixture velocity is high. A high gas–liquid slip ratio results in void fraction underestimation. Void fraction prediction performance is satisfactory when void fraction is less than 0.4 and fluctuating index of DP signal less than 1.2.