Comprehensive modeling of frictional pressure drop during carbon dioxide two-phase flow inside channels using intelligent and conventional methods

Environmentally friendly nature of CO₂, associated with its safety and high efficiency, has made it a widely used working fluid in heat exchangers. Since CO₂ has strange thermophysical features, specific models are required to estimate its two-phase characteristics, particularly frictional pressure drop (FPD). Herein, a widespread dataset, comprising 1195 experimental samples for two-phase FPD of CO₂ was adopted from 10 sources to fulfill this requirement. The literature correlations failed to provide satisfactory precisions and exhibited the average absolute relative errors (AAREs) between 29.29% and 67.69% from the analyzed data. By inspiring the theoretical method of Lockhart and Martinelli, three intelligent FPD models were presented, among which the Gaussian process regression approach surpassed the others with AARE and R² values of 5.48% and 98.80%, respectively in the test stage. A novel simple correlation was also derived based on the least square fitting method, which yielded opportune predictions with AARE of 19.76% for all data. The truthfulness of the newly proposed models was assessed through a variety of statistical and visual analyses, and the results affirmed their high reliability over a broad range of conditions, channel sizes and flow patterns. Furthermore, the novel models performed favorably in describing the physical attitudes corresponding to two-phase FPD of CO₂. Eventually, the importance of operating factors in controlling the FPD was discussed through a sensitivity analysis.