A stochastic game theory approach for the prediction of interfacial parameters in two-phase flow systems

The prediction of interfacial area properties in two-phase flow systems is difficult and challenging. In this paper, a conceptual idea of using single-agent reinforcement learning for the behaviors of two-phase flows and IAC behaviors is proposed. The basic assumption for this application is that the development of two-phase flow is considered to be a stochastic process with Markov property. The details of the design of simple Markov games are described and approaches of gaming solutions are adapted. The experiment shows that both of the steam fraction and IAC prediction processes converge. The model predictions are compared with the experimental results, and the tendency matches although some oscillations exist. The performances and prediction results can be improved by elaborating the game environment setup.

Two-phase flow regime prediction using LSTM based deep recurrent neural network

Long short-term memory (LSTM) and recurrent neural network (RNN) has achieved great successes on time-series prediction. In this paper, a methodology of using LSTM-based deep-RNN for two-phase flow regime prediction is proposed, motivated by previous research on constructing deep RNN. The method is featured with fast response and accuracy. The built RNN networks are trained and tested with time-series void fraction data collected using impedance void meter. The result shows that the prediction accuracy depends on the depth of network and the number of layer cells. However, deeper and larger network consumes more time in predicting.