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Digital twin is a modern technology for many advanced applications. Toconstruct a digital twin of a thermal system, it is required to make onlineestimations of unknown time-varying boundary conditions from sensor measureddata, which needs to solve inverse heat transfer problems (IHTPs). However, afast and accurate solution is challenging since the measured data is normallycontaminated with noise and the traditional method to solve IHTP involvessignificant amount of calculations. Therefore, in this work, a rapid yet robustinversion algorithm called ANN-based extended Kalman smoothing algorithm isdeveloped to realize the online prediction of desired parameter based on themeasurements. The fast prediction is realized by replacing the conventionalCFD-based state transfer models in extended Kalman smoothing algorithm withpre-trained ANN. Then, a two-dimensional internal convective heat transferproblem was employed as the case study to test the algorithm. The results haveproved that the proposed algorithm is a computational-light and robust approachfor solving IHTPs. The proposed algorithm can achieve estimation of unknownboundary conditions with a dimensionless average error of 0.0580 under noisytemperature measurement with a standard deviation of 10 K with a drasticreduction of computational cost compared to the conventional approach.Moreover, the effects of training data, location of sensor, future time stepselection on the performance of prediction are investigated.

Rapid online solution of inverse heat transfer problem by ANN-based extended Kalman smoothing algorithm