# Boiling Flow Pattern Identification Using a Self-Organizing Map
## 3 Results and Discussion  
In Figure 7, the average hit histogram obtained from 10 results of _SOM_ is presented. The points of the hit histogram were located according to the similarity of each output layer weight. Neurons of similar weight locate themselves close to one another, while dissimilar neurons are far from each other. In such a way the _SOM_ algorithm created the 2D map identifying the differences between system dynamics (dynamics of pressure drop oscillations). The hit histogram was divided into five areas that were matched to the five two-phase flow patterns identified during a single _PDO_ cycle. The following two-phase flow patterns were identified (Figure 7): _I_--liquid flow; _II_--liquid flow with small vapor bubbles; _III_--confined bubble flow; _IV_--wavy annular flow; and _V_--vapor slug flow. The history of building the hit histogram allows us to identify which part of the pressure drop time series is assigned to each part of the hit histogram. The result is shown in Figure 8b and was compared with the pressure drop time series (Figure 8a). On the _y_-axis in Figure 8b, specific flow patterns were determined, where \(I\) is liquid flow, _II_ is bubble flow, _III_ is long slug flow, _IV_ is confined bubble flow, and \(V\) is slug flow. The character of the oscillations between the different flow patterns (Figure 8b) allows us to distinguish four different boiling regimes during a single _PDO_ cycle. Figure 6: Flowchart of the pressure drop oscillation analysis using the recurrence quantification analysis (_RQA_) and a self-organizing map (_SOM_) network. We defined the following boiling regimes: _A_--liquid flow, bubbles flow and confined bubble flow, _B_--long slug flow and confined bubble flow, _C_--long slug flow and slug flow, and _D_--long slug flow and confined bubble flow. Examples of flow observed in such defined boiling regimes are shown in Figure 8c. In boiling regime \(A\), the boiling starts when the channel is filled with water. Due to heat being supplied to the minichannel, the small bubbles start to appear in the glass channel. Next, the confined bubble flow is also filled with water. The bubbles are filled with water. Due to heat being supplied to the minichannel, the small bubbles start to appear in the glass channel. bubbles appear. It causes the pressure drop increases that are accompanied by a water flow rate increase. Finally, the higher pressure at the inlet of the minichannel causes the removal of the confined bubbles and pressure drop decreases. In boiling regime \(B\), the oscillations start when the channel is filled by the confined bubbles. Next, the long slug appear in the glass pipe. In the condenser section, the long slug starts to shrink and the confined bubble flow appears in the minichannel again. In boiling regime \(C\), the oscillations between the long slug flow and the slug flow appear in the glass channel. In boiling regime \(D\), oscillations appear between the long slug' flow and the bubble flow in a short period of time. The accuracy of the postulated approach is determined by the length of the moving window in windowed \(RQA\). We set the moving window length on 5000 samples (5 s), which means that all phenomena during less than 5 s are not identified, while processes that are longer than 5 s are identified. The accuracy of two-phase flow pattern identification using \(SOM\) analysis depends on the number of data and the size of the hit histogram map. Because of a limited number of data, it is difficult to determine the accuracy of the \(SOM\) analysis. The method proposed in this paper was also used to a preliminary analysis of pressure drop fluctuations in a single minichannel (diameter of 1 mm) obtained at different water flow rates and heating electrical power. Figure 9 shows the results of the analysis made at \(G=61.65\) kg/m\({}^{2}\)s and \(P=75.6\) W (Figure 9a-c) and at \(G=38.8\) kg/m\({}^{2}\)s and \(P=55.8\) W (Figure 9d-f).