This study experimentally investigates the combined impact of several parameters on the two-phase flow distribution in an evaporator header for an air/water mixture, in flow pattern similarity with a low Global Warming Potential refrigerant. The test were performed at isothermal conditions under the assumption that phase change is negligible in an evaporator header. The water and air flow rates were varied and three inlet qualities were targeted (x=0.04,0.1and0.25). Total mass fluxes G ranging from 42 kg/(sm²) to 513 kg/(sm²) were covered. The impact of the fluid properties on the flow patterns was preliminary and theoretically evaluated by means of flow maps for all pipe directions: horizontal, vertical upward and vertical downward. A rectangular header connected to eight parallel channels of internal diameter (I.D.) of 10 mm was used to mimic an evaporator. Four header and channels orientations were investigated. The inlet position and the diameter of the feeding tube (23 mm or 56 mm) could be changed as well as the channels intrusion inside the header height. A flow pattern breaking device, also called splashing grid, was also tested at the inlet of the header. A Design Of Experiment (DOE) technique was used to build an optimized test matrix ensuring that the impact of each parameter individually as well as their combinations could be assessed in a balanced manner and within a minimum amount of tests. Forty-eight tests were needed. The standard deviation of the water flow distribution among the channels is set as comparative variable. Based on the experimental results, a ranking of the most influential parameters was established. The study highlights that the orientation of the header and channels is the most significant parameter impacting the flow distribution, followed the tube inlet position. The combined influence of the inlet tube position and diameter, the tube intrusion and the presence/absence of the splashing grid is evaluated for each of the four orientations. Based on these conclusions, design rules were established for each header and channel orientation. The findings of this research represent a significant advancement in the field and can serve as a foundation for greatly improving flow distribution within evaporators, thereby enhancing their thermal performance.

The uneven distribution of flow phases in evaporator channels can drop the heat exchanger efficiency up to 30%. Due to its dependence on the interaction of several coexisting variables – both geometry, operating conditions, and fluid properties – it is a complex phenomenon to analyze. Most studies focus on the effect of single parameters: this is an inefficient and expensive way of doing experiments, and the results lack in understanding how the combination of variables affects the flow distribution. This paper presents a methodology to optimally characterize and predict the distribution of flow phases in the channels of an evaporator header based on Design of Experiment (DoE) techniques. Despite the proven potential of DoE methods, they have never been applied in this field. Tests were conducted with an air–water mixture in the configuration horizontal header with vertical channels with downward flow, varying inlet pipe position, channels intrusion, presence of a splashing grid at the header inlet, and air and water flow rates. Results prove that, when working with complex processes, interaction effects between variables cannot be neglected as they significantly affect the response. The most affecting parameter was found to be the air flow rate, followed by the combination between inlet pipe position and presence of the splashing grid. With horizontal inlet, the optimal response was given by absence of intrusion, presence of the splashing grid, lowest water, and highest air flow rate. Instead, for the vertical case, the distribution was enhanced with the highest intrusion, absence of the grid, and highest water and air flow rates. Lastly a first attempt to model the process was performed. Even if a universal regression model has low accuracy (51%), restricting the area of analysis can result in valid predictive relations, with accuracies up to 91.4%.