Conditional Source-term Estimation using dynamic ensemble selection and parallel iterative solution

Abstract

A modified version of the Least-Square QR-factorisation (LSQR) algorithm has been implemented in conjunction with Conditional Source-term Estimation (CSE) for lean, turbulent premixed methane–air combustion via Large Eddy Simulation (LES). The iterative solver can reduce computational times by an order of magnitude during the inversion phase of CSE in comparison with the conventional LU-decomposition method. The advantages of iterative and parallel iterative solvers become more prominent as the size of the system increases. The ensemble selection procedure for computing averages within localised regions of the simulation domain has also been updated to a dynamic routine. This allows for more flexible and efficient allocation of computational resources along with reduced input from the user, especially for complex geometries. Preliminary LES calculations have shown that the implementation of an iterative solver and a dynamic ensemble selection algorithm will reduce computational times significantly with negligible error contribution for one-condition CSE, which is applicable to purely premixed or non-premixed turbulent combustion problems. In addition, these algorithms provide the foundation for exceptional computational cost savings for the inversion in two-condition CSE, or Doubly Conditional Source-term Estimation (DCSE), which has shown promise for predicting partially-premixed combustion. Parallel computation of the inverse solution is particularly beneficial to DCSE as the computational cost of the inversion process is considerably larger than in one-condition CSE.