PROBABILISTIC OPTIMIZATION OF TWO-PHASE FLOW USING BAYESIAN MODELS

Gas-liquid two-phase flows are encountered in a variety of applications such as turbo-machinery flows, gas-turbines, ramjet and scram-jets, automotive engines and aircraft engines. Designing systems to control such flows is enormously challenging owing to the addition of new non-dimensional groups that characterize the two-phase flow system compared to a single-phase flow. Additionally, two-phase flows can exhibit non-linear hydrodynamic instabilities that determine the overall behavior of the system. In this study, we choose a generic two-phase flow configuration that exhibits known complexities in realistic two-phase flow systems. The goal of the study is to optimize the geometry of the two-phase flow configuration with minimal computational cost. We propose a probabilistic approach to model the stochastic system and optimize the two-phase flow system under uncertain inputs. The potential benefits of the approach are highlighted along with future directions for using probabilistic design techniques to optimize two-phase flow systems.