A machine learning approach to the prediction of heat-transfer
  coefficients in micro-channels
Tullio Traverso Francesco Coletti Luca Magri Tassos G. Karayiannis Omar K. Matar
The accurate prediction of the two-phase heat transfer coefficient (HTC) as a
function of working fluids, channel geometries and process conditions is key to
the optimal design and operation of compact heat exchangers. Advances in
artificial intelligence research have recently boosted the application of
machine learning (ML) algorithms to obtain data-driven surrogate models for the
HTC. For most supervised learning algorithms, the task is that of a nonlinear
regression problem. Despite the fact that these models have been proven capable
of outperforming traditional empirical correlations, they have key limitations
such as overfitting the data, the lack of uncertainty estimation, and
interpretability of the results. To address these limitations, in this paper,
we use a multi-output Gaussian process regression (GPR) to estimate the HTC in
microchannels as a function of the mass flow rate, heat flux, system pressure
and channel diameter and length. The model is trained using the Brunel
Two-Phase Flow database of high-fidelity experimental data. The advantages of
GPR are data efficiency, the small number of hyperparameters to be trained
(typically of the same order of the number of input dimensions), and the
automatic trade-off between data fit and model complexity guaranteed by the
maximization of the marginal likelihood (Bayesian approach). Our paper proposes
research directions to improve the performance of the GPR-based model in
extrapolation.