Digital twin of a combustion furnace operating in flameless conditions: reduced-order model development from CFD simulations

Abstract

The present paper presents the first-of-its-kind digital twin for a furnace operating in flameless combustion conditions. A methodology combining data compression, by means of Proper Orthogonal Decomposition (POD), and interpolation, using Kriging, was developed to design physics-based, reduced-order models (ROMs) for the prediction of combustion data at unexplored operating conditions. Three-dimensional simulations with detailed chemistry were carried out, spanning a wide range of operating conditions in terms of fuel composition (methane-hydrogen mixtures from pure methane to pure hydrogen), equivalence ratio (from 0.7 to 1) and air injector diameter (to adjust the air jet entrainment). Based on the available simulations, a ROM was developed, to predict both spatial fields, local and integral values of thermochemical variables at working conditions not included in the ROM development. Results showed that the developed ROM could reliably predict the temperature and main chemical species distribution in the furnace with an overall error below 10%, proving the effectiveness of the approach for the development of digital twins of combustion systems. A remarkable accuracy was observed for the prediction of specific quantities, including wall temperatures, OH decay length, OH peak value and location and exhaust gas composition, including pollutants, with prediction errors always below 5%, showing the potential of the approach to develop soft sensors.