Application of NARX neural networks in thermal dynamics identification of a pulsating heat pipe

The pulsating heat pipe (PHP) receiving much attention in industries is a novel type of cooling device. The distinguishing feature of PHPs is the unsteady flow oscillations formed by the passing non-uniform distributions of vapour plugs and liquid slugs. This study introduces a methodology of a non-linear auto-regressive with exogenous (NARX) neural network to analyze the thermal dynamics of a PHP in both the time and frequency domains. Three heating powers: 30, 70, and 110 W are tested, and all the predicted results are presented in quite good agreement with the measured results. Herein, the harmonic analysis of the non-linear structure can be equivalently conducted with generalized frequency response functions (GFRFs). Based on the non-linear coupling between the various input spectral components, the interpretations of the higher order GFRFs have been extensively presented for demonstrating the non-linear effects on the heat transfer of a PHP at different operating conditions.