Abstract: Systems and methods that include and/or leverage a neural network to approximate the steady-state performance of a turbine engine are provided. In one exemplary aspect, the neural network is trained to model a physics-based, steady-state cycle deck. When properly trained, novel input data can be input into the neural network, and as an output of the network, one or more performance indicators indicative of the steady-state performance of the turbine engine can be received. In another aspect, systems and methods for approximating the steady-state performance of a “virtual” or target turbine engine based at least in part on a reference neural network configured to approximate the steady-state performance of a “fielded” or reference turbine engine are provided.

Abstract: A combustor dome assembly for a gas turbine engine having a longitudinal centerline axis extending therethrough, including: an annular dome plate having an inner portion, an outer portion, a forward surface, and a plurality of circumferentially spaced openings formed therein, wherein a radial section defined between each of the openings includes a cooling trough formed therein; an outer cowl connected to the dome plate outer portion at a downstream end thereof; an inner cowl connected to the dome plate inner portion at a downstream end thereof; and, a deflector plate connected to and positioned aft of each opening in the dome plate.