Treatment of efficiency for temperature and concentration profiles reconstruction of soot and metal-oxide nanoparticles in nanofluid fuel flames

Abstract

The choice of the step size plays an important role in one dimensional searching (ODS) algorithm in the inverse radiation problem to simultaneously estimate temperature and concentration distributions of soot and metal-oxide nanoparticles in nanofluid fuel flames. In practice, it is difficult to determine the optimal step size and the inappropriate step size can introduce significant reconstruction error and cause high computation time. This paper adopts a nonlinear optimization (NLP) technique without the needing of setting up the step size to improve the reconstruction efficiency. The reconstruction performances of NLP technique were compared with the ODS algorithm with different step sizes. It was found that the NLP technique can reach the same or better reconstruction accuracy and experience much lower computation time in comparison with the ODS algorithm even with the optimal step size.