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The objective of this work is to build a Digital Twin of a semi-industrial furnace using Gaussian Process Regression coupled with dimensionality reduction via Proper Orthogonal Decomposition. The Digital Twin is capable of integrating different sources of information, such as temperature, chemiluminescence intensity and species concentration at the outlet. The parameters selected to build the design space are the equivalence ratio and the benzene concentration in the fuel stream. The fuel consists of a H2/CH4/CO blend, doped with a progressive addition of C6H6. It is an H2-rich fuel mixture, representing a surrogate of a more complex Coke Oven Gas industrial mixture. The experimental measurements include the flame temperature distribution, measured on a 6×8 grid using an air-cooled suction pyrometer, spatially resolved chemiluminescence measurements of OH* and CH*, and the species concentration (i.e., NO, NO2, CO, H2O, CO2, O2) measured in the exhaust gases. The GPR-based Digital Twin approach has already been successfully applied on numerical datasets coming from CFD simulations. In this work, we demonstrate that the same approach can be applied on heterogeneous datasets, obtained from experimental measurements.

The goal of this work is to perform parameter estimation by comparing a Reduced Order Model (ROM), built using Proper Orthogonal Decomposition (POD) and Gaussian Process Regression (GPR), with a Sparse Sensing (SpS) model. This framework is demonstrated by selecting the optimal set of the Partially Stirred Reactor (PaSR) coefficients used in the modelling of the Cabra flame. The Cabra flame is a methane flame in a vitiated coflow, consisting of the combustion products of hydrogen and air. The PaSR model necessitates the knowledge of 4 scalar coefficients, which are unknown a priori. To select the optimal set of coefficients, 57 simulations were performed with a different combination of PaSR coefficients. These simulations were used to build the ROM via POD and GPR. To compare the numerical solution with the experimental data, the SpS technique has been employed. SpS is a framework that leverages dimensionality reduction to predict the state of the system given few measurements. The optimal coefficients have been estimated by applying an optimization algorithm to the ROM, using the solution provided by SpS as target. Finally, the data assimilation framework has been used to provide a solution with lower uncertainty bounds. The results show that this framework is able to estimate the optimal set of coefficients, and it can be used to identify residual sources of uncertainty in the numerical model by highlighting the difference between the optimized model and the experimental values.

This study examines the flow field dynamics of bluff-body stabilized swirling and non-swirling flames produced from the Cambridge/Sandia Stratified Swirl Burner. This burner has been used in previous studies as a benchmark for high-resolution scalar and velocity measurements and for validating numerical models. The burner was designed to create reacting flow conditions that are representative of turbulent flows in modern combustion systems, including sufficiently high turbulence levels, and to operate under both premixed and stratified conditions. High-speed stereoscopic Particle Image Velocimetry (PIV) was used to acquire time-resolved velocity data for a series of turbulent methane/air flames at both premixed and stratified conditions.We employ the Multi-Scale Proper Orthogonal Decomposition (mPOD) to identify the main flow patterns in the velocity field and isolate the coherent structures linked to various flow instabilities. The results show that the most energetic structures in the flow are consistent with the Bénard-von Kármán (BVK) instability due to the presence of the bluff-body and the Kelvin-Helmholtz (KH) instability caused by the shear layer between the inner and outer flow. In both the swirling and non-swirling cases, the BVK is suppressed by the combustion, except for the most stratified swirling case. Moreover, the results show that combustion does not affect the KH instability because the shear layer does not coincide with the flame position.