UC Irvine

In this work, neural network (NN)-based models are generated to replace flamelet tables for sub-grid modeling in large-eddy simulations of a single-injector liquid-propellant rocket engine. The NN training process presents an extraordinary challenge. The multi-dimensional combustion instability problem involves multi-scale lengths and characteristic times in an unsteady flow problem with nonlinear acoustics, addressing both transient and dynamic-equilibrium behaviors, superimposed on a turbulent reacting flow with very narrow, moving flame regions. Accurate interpolation between the points of the training data becomes vital. Computationally efficient and accurate flamelet models for a turbulent combustor are needed for useful large eddy simulations (LES). Promise is offered through the use of deep learning NN. Here, a well-studied configuration through prior LES and experiment is used but now with NN providing the sub-grid model for the flamelets. It is of special interest to extend the use of NN modeling for unsteady behavior of mean pressure and velocity fields. Two different approaches for NN design are proposed, based on the source of training data used in the NN development. In the first approach, data in the flamelet libraries are used as a source for training NNs to replace those flamelet libraries in the CFD simulation of a turbulent diffusion flame with unsteady pressure. The models are validated on those libraries and verified by being implemented into CFD simulations. Both transient and dynamic equilibrium oscillatory conditions are considered by varying initial conditions of the simulations. Two different geometrical configurations are also tested. The NN-based simulations are favorably compared with their table-based counterparts. The NNs proposed based on the second approach are trained based on data processed from a few CFD simulations of a single-injector liquid-propellant rocket engine with different dynamical configurations to reproduce the information stored in a flamelet table. The training set is also enriched by data from the physical characteristics and considerations of the combustion model. Flame temperature is used as an extra input for other flame variables to improve the NN-based model accuracy and physical consistency. The trained NNs are first tested offline on the flamelet table. These physics-aware NN-based closure models are successfully implemented into CFD simulations and verified by being tested on various dynamical configurations. The results from those tests are in good agreement with their counterpart table-based CFD simulations.