Adaptive digital twins of combustion systems using sparse sensing strategies

Abstract

This work proposes to implement a sparse sensing framework to build a hybrid numerical-experimental Digital Twin of a practical combustion system. The goal is to find the optimal sensor placement that minimizes the prediction error, and to predict the distribution of reacting scalars using few measurements. Three-dimensional CFD simulations with detailed chemistry were used to build the design space by varying the fuel composition (from pure methane to pure hydrogen), the equivalence ratio (from 0.7 to 1) and the air velocity. The Proper Orthogonal Decomposition (POD) was applied to the numerical data to find a tailored basis for dimensionality reduction. Then, the QR decomposition with column pivoting was applied to the tailored basis to find the optimal sensor placement. Finally, the model was employed to predict the three-dimensional temperature distribution in the unexplored part of the design space, using the experimental samples as input. The optimal placement of the sensors provides valuable information on the key locations and features, which can then be used in the design of reactor network models, for example. Also, the results show that the hybrid Digital Twin could predict an adjusted temperature distribution which reduces the error with the experimental measurements, when compared to the original CFD temperature distribution.