Genetic Algorithm and Machine Learning Based Void Fraction Measurement of Two-Phase Flow

Machine learning and Genetic Algorithm based void fraction measurement method is provided in this paper. Because there are some relationships between the void fraction and the differential pressure (DP) signal acquired near the pipe wall when the two phases are flowing along the pipeline, it is possible to measure the void fraction according to the DP signal. However, the expression between the void fraction and the DP signal is complicated and is not easy to be developed because of the complexity of the characteristics of two-phase flow. In this paper, SVM is adopted to investigate the relationship between the void fraction and the DP signal. GA is used to estimate the parameters involved in SVM. The experimental results show that machine learning and genetic algorithm based void fraction measurement method provided in this paper is available.