Incremental sampling methods for multi-fidelity surrogate modeling: Application on a furnace operating in MILD combustion conditions

Abstract

This study introduces a framework for developing a multi-fidelity reduced order model (MF-ROM) of a combustion furnace operating under Moderate and Intense Low-oxygen Dilution (MILD) conditions. It integrates Proper Orthogonal Decomposition for data compression, Procrustes manifold alignment for fidelity transfer, and CoKriging for interpolation. Design parameters such as air injector diameter, fuel composition, and equivalence ratio were used to generate two- and three-dimensional simulations and build the MF-ROM. Additionally, the key question concerning the optimal number of high-fidelity simulations to balance accuracy and training cost is addressed when building the MF-ROM. Through incremental sampling strategies, it is demonstrated that around half of the training cost can be conserved while maintaining comparable error values.