  Digital twin is a modern technology for many advanced applications. To
construct a digital twin of a thermal system, it is required to make online
estimations of unknown time-varying boundary conditions from sensor measured
data, which needs to solve inverse heat transfer problems (IHTPs). However, a
fast and accurate solution is challenging since the measured data is normally
contaminated with noise and the traditional method to solve IHTP involves
significant amount of calculations. Therefore, in this work, a rapid yet robust
inversion algorithm called ANN-based extended Kalman smoothing algorithm is
developed to realize the online prediction of desired parameter based on the
measurements. The fast prediction is realized by replacing the conventional
CFD-based state transfer models in extended Kalman smoothing algorithm with
pre-trained ANN. Then, a two-dimensional internal convective heat transfer
problem was employed as the case study to test the algorithm. The results have
proved that the proposed algorithm is a computational-light and robust approach
for solving IHTPs. The proposed algorithm can achieve estimation of unknown
boundary conditions with a dimensionless average error of 0.0580 under noisy
temperature measurement with a standard deviation of 10 K with a drastic
reduction of computational cost compared to the conventional approach.
Moreover, the effects of training data, location of sensor, future time step
selection on the performance of prediction are investigated.

Systems and Control;Systems and Control;Data Analysis, Statistics and Probability;Fluid Dynamics