Multi-objective optimisation towards thrust and fuel efficiency of adaptive cycle engine based on 0D-1D coupled method

Abstract As a revolutionary aviation propulsion system, the adaptive cycle engine (ACE) provides more powerful capabilities than conventional engines. To accurately exploit the ACE performance potential in a wide and complicated design space, the multi-objective optimisation towards enhancing the thrust and fuel efficiency based on the zero-dimensional and one-dimensional coupled (0D-1D) method is carried out. For the adaptive fan and variable geometry low-pressure turbine (LPT), the physics-based 1D mean-line models are established and coupled with the 0D engine model. Then, a multi-objective optimisation method is developed, which combines the back-propagation neural network (BPNN) and the non-dominated sorting genetic algorithm II (NSGA-II). At off-design conditions, the performance differences between the 0D-1D model and the 0D model are notable. In particular, when the 0D model is inaccurate in predicting the LPT characteristics with variable geometry, the ACE performance trends predicted by the 0D model may be opposite to reality. The Pareto-optimal thrust and fuel efficiency of the 0D-1D model are superior to those of the 0D model. Within the Pareto-optimal solutions, the subsonic specific fuel consumption (SFC) and thrust from the 0D-1D model can be 1.7% lower and 4.5% larger than those from the 0D model, respectively. The optimal design parameters and control laws between the two models are distinct.