RAD-NNET, a neural network based correlation developed for a realistic simulation of the non-gray radiative heat transfer effect in three-dimensional gas-particle mixtures

Walter W. Yuen
<span title="">2009</span> <i title="Elsevier BV"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/o7dklecsfnfrrgrtok5dd4teey" style="color: black;">International Journal of Heat and Mass Transfer</a> </i> &nbsp;
A neural network correlation, RAD-NNET, is developed to simulate the realistic effect of non-gray radiative absorption by a homogeneous mixture of combustion gases (CO 2 and H 2 O) and soot using numerical data generated by RADCAL. RAD-NNET is then applied to assess the accuracy of some commonly accepted approximate approaches to evaluate radiative heat transfer in three-dimensional non-gray media. Results show that there are significant errors associated with the current approximate
more &raquo; ... RAD-NNET can be readily implemented in commercial CFD codes to greatly enhance the accuracy of simulation of radiative heat transfer in practical engineering systems.
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1016/j.ijheatmasstransfer.2009.01.041">doi:10.1016/j.ijheatmasstransfer.2009.01.041</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/fge7a673zngvrdjtrcznllrfwq">fatcat:fge7a673zngvrdjtrcznllrfwq</a> </span>
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