Projection-based reduced order modeling of multi-species mixing and combustion

Abstract

High-fidelity simulations of mixing and combustion processes are computationally demanding and time-consuming, hindering their wide application in industrial design and optimization. This study proposes projection-based reduced order models (ROMs) to predict spatial distributions of physical fields for multi-species mixing and combustion problems in a fast and accurate manner. The developed ROMs explore the suitability of various regression methods, including kriging, multivariate polynomial regression (MPR), k-nearest neighbors (KNN), deep neural network (DNN), and support vector regression (SVR), for the functional mapping between input parameters and reduced model coefficients of mixing and combustion problems. The ROMs are systematically examined using two distinct configurations: steam-diluted hydrogen-enriched oxy-combustion from a triple-coaxial nozzle and fuel-flexible combustion in a practical gas-turbine combustor. The projected low-dimensional manifolds are capable of capturing important combustion physics, and the response surfaces of reduced model coefficients present pronounced nonlinear characteristics of the flowfields with varying input parameters. The ROMs with kriging present a superior performance of establishing the input–output mapping to predict almost all physical fields, such as temperature, velocity magnitude, and combustion products for both test problems. The accuracy of DNN is less encouraging owing to the stringent requirement on the size of training database. KNN performs well in the region near the design points but its effectiveness diminishes when the test points are distant from the sampling points, whereas SVR and MPR exhibit large prediction errors. For the spatial prediction at unseen design points, the ROMs achieve a prediction time of up to eight orders of magnitude faster than conventional numerical simulations, rendering an efficient tool for the fast prediction of mixing and combustion fields and potentially an alternative of a full-order numerical solver.