## 5 Results and Discussion  
### Fluid Oscillatory Behavior  
The local thermal interactions between the working fluid and the FPPHP wall are representative of the fluid motion, i.e., fluid oscillations, inside the device, as demonstrated in [13]. Hence, the wavelet approach was used for the evaluation of dominant frequencies \(f_{d}\) in the overall studied device, i.e., every FPPHP channel, by adopting wall-to-fluid heat flux distributions for single axial coordinates over time. In Figure 6, the post-processed heat flux signal referred to \(z=0.02\) m for channel 8 (reference of Figure 1b) and \(Q=100\) W in the horizontal orientation is shown as a sample for the wavelet method outcomes understanding of experimental data.  
Figure 5: Restored heat flux, used as input for the wavelet method (**a**); corresponding scalogram (**b**) and power spectrum (**c**).  
The experimental-based signal (Figure 6a) highlights non-fixed amplitude and oscillation frequency over time. In fact, the scalogram of Figure 6b exhibits scattered spots having different power in the range from 0.8 to 2 Hz. From 0 to about 10 s, the high-power spots are mainly present around 1.5 Hz, denoting that the heat flux, i.e., the working fluid, oscillates with great amplitude at that frequency. From 10 to 14 s, the scalogram does not present significant power spots, thus suggesting the occurrence of weak heat flux oscillations. From 14 to 18 s, a high-power spot is perceivable around 1 Hz, denoting a substantial change in terms of oscillation frequency of the heat flux. Such a time change in oscillation frequency is also clear in the power spectrum (Figure 6c), where two peaks are present: one at about 1 Hz and one at about 1.5 Hz. Here, although there is a short-time change in oscillatory behavior, the heat flux mainly oscillates with variable amplitudes at about 1.5 Hz over the entire observation window, and the absolute maximum of the power spectrum is located around such frequency. In particular, the dominant oscillation frequency \(f_{d}\) of the signal is found at about 1.45 Hz.  
It has to be pointed out that since the heat flux distributions evaluated in the present device significantly vary with space [29], the wavelet method may provide different results depending on the considered axial coordinate. To investigate such dependency, the wall-to-fluid heat flux signals related to a single channel at different \(z\) coordinates were used as inputs for the wavelet approach, and the resulting power spectra were therefore compared. In Figure 7, such a comparison was carried out for channel 9 at \(z=0.01\) m, \(z=0.02\) m and \(z=0.03\) m, and both device orientations at \(Q=250\) W. Here, despite different amplitudes of the wall-to-fluid heat fluxes of Figure 7a-c, their oscillatory trends over time do not present any dissimilarities from axial point to axial point. In fact, all the power spectra  
Figure 6: Heat flux for a single axial coordinate referred to channel 8 (\(z=0.02\) m), \(Q=100\) W, horizontal orientation (**a**), corresponding scalogram (**b**) and power spectrum (**c**).  
of Figure 7b-d exhibit absolute peaks in correspondence with the same frequency, thus highlighting same the dominant frequency regardless of the considered axial position. For this reason, every processed heat flux was chosen, for the present analysis, to be at \(z=0.02\) m for every channel, representative of all other \(z\) coordinates.