Chaotic index analysis of ethanol-based graphene nanofluid pulsating heat pipe

Abstract

Pulsating heat pipes utilizing ethanol-based graphene nanofluids hold significant promise for cooling large electronic devices. However, traditional methods fall short in fully characterizing their properties due to internal phase transitions, latent heat, multiphase flow, and multi-field coupling. To address this, nonlinear chaotic analysis methods are introduced. This study constructs a vertical closed-loop pulsating heat pipe platform and applies Takens embedding theorem, phase space reconstruction theory, mutual information recursive selection method, and associative dimensionality algorithm to analyze ethanol-based graphene nanofluids with various concentrations. The investigation focuses on correlation dimensions and Kolmogorov entropy to provide numerical feedback on nanofluid concentration. Notably, ethanol-based graphene nanofluids concentrations between 0.04 wt% and 0.07 wt% exhibit an average heat transfer thermal resistance of approximately 0.74 K/W, corresponding to correlation dimension and Kolmogorov entropy values of 1.36 and 0.035, respectively. Additionally, reconstructed three-dimensional Singular Attractor phase diagrams offer morphological feedback on the pulsating heat pipe's working conditions. As the ethanol-based graphene nanofluids concentration reaches 0.35 wt%, an increase in the number of free vector points within the three-dimensional coordinate Attractor indicates localized unstable conditions in the heat pipe. Employing a wavelet noise reduction method with a soft threshold of 80 preserves the macroscopic vector flow pattern while eliminating irrelevant local information. Overall, this paper introduces novel approaches for analyzing heat transfer characteristics of graphene nanofluids in pulsating heat pipes.