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