Abo Bibliothek: Guest

ISSN Online: 2377-424X

International Heat Transfer Conference 12
August, 18-23, 2002, Grenoble, France

Forced convective heat transfer to supercritical carbon dioxide inside tubes. Analysis through neural networks

Get access (open in a dialog) DOI: 10.1615/IHTC12.1430
6 pages

Abstrakt

In literature a variety of correlations has been proposed to predict the coefficients for heat transfer to fluids in the near-critical region, but discrepancies are reported. In fact in the near-critical region the thermophysical properties such as density, heat capacities, enthalpy, viscosity, thermal conductivity, etc. present intense variations for limited changes of temperature or pressure. As it is usual, the correlations have been developed assuming an initial approximate model in which successive modifications are introduced to correct discrepancies with a "trial and error" like procedure. The present work aims instead at developing an analytical expression directly from experimental evidences regressing organized experimental data.
Neural networks have been assumed for this task, because they are a very versatile and powerful function approximator tool. A neural network has been trained on a limited amount of data covering homogeneously the operative conditions range. Once the network has been successfully trained, it is able to represent the behaviour of the whole data set. Besides, the extrapolation beyond the training range proves to be very regular.
Three different correlation architectures are proposed for the neural networks, based both on dimensionless groups as variables of the neural network function and on directly accessible physical quantities. In all the three architectures the definition of the optimal functional form for the correlation is obtained through a completely heuristic procedure only based on experimental data, and the reached accuracy is comparable with the claimed experimental uncertainties.