This work presents and analyses the results of an experimental activity aimed at the characterization of stratified air–water flow conditions, which have been poorly analyzed in previous studies although they are significant for industrial applications. Tests were performed in a 24 m long, 60 mm inner diameter PMMA pipe; the superficial velocities ranged between 0.03 m/s and 0.06 m/s for the water and between 0.41 m/s and 2.31 m/s for air. The pressure gradient along the pipeline was determined and compared to the one obtained implementing two-fluid models available in the literature. Fair agreement with the models was found only at high values of the superficial gas velocities, i.e., above 1.31 m/s. Moreover, the void fraction was measured through a resistive probe and compared with the values predicted by available models. Since none of them was able to satisfactorily predict the void fraction in the whole range of superficial velocities, a drift flux model was successfully implemented. Eventually, with both the measured pressure gradient and the void fraction, a two-fluid model was implemented in order to determine the interfacial shear stress and to compare the outcome with the literature, emphasizing the influence of the operating conditions on the prediction performance.

Environmental concerns are forcing the replacement of commonly used refrigerants, and finding new fluids is a top priority. Soon the R134a will be banned, and the hydro-fluoro-olefin (HFO) R1234ze(E) has been indicated as an alternative due to its smaller global warming potential (GWP) and shorter atmospheric lifetime. Nevertheless, for an optimal replacement, its thermo-fluid-dynamic characteristics have to be assessed. Flow boiling experiments (saturation temperature T_sat = 5 °C, mass flux G = 65 ÷ 222 kg·m⁻²·s⁻¹, mean quality x_m = 0.15 ÷ 0.95, quality changes ∆x = 0.06 ÷ 0.6) inside a microfin tube were performed to compare the pressure drop per unit length and the heat transfer coefficient provided by the two fluids. The results were benchmarked for some correlations. In commonly adopted operating conditions, the two fluids show a very similar behavior, while benchmark showed that some correlations are available to properly predict the pressure drop for both fluids. However, only one is satisfactory for the heat transfer coefficient. In conclusion, R1234ze(E) proved to be a suitable drop-in replacement for the R134a, whereas further efforts are recommended to refine and adapt the available predictive models.

The present study is focused on identifying the most suitable sequence of machine learning-based models and the most promising set of input variables aiming at the estimation of heat transfer in evaporating R134a flows in microfin tubes. Utilizing the available experimental data, dimensionless features representing the evaporation phenomena are first generated and are provided to a machine learning-based model. Feature selection and algorithm optimization procedures are then performed. It is shown that the implemented feature selection method determines only six dimensionless parameters ($S{u}_l$: liquid Suratman number, $Bo$: boiling number, $F{r}_g$: gas Froude number, $R{e}_l$: liquid Reynolds number, $Bd$: Bond number, and $e/D$: fin height to tube’s inner diameter ratio) as the most effective input features, which reduces the model’s complexity and facilitates the interpretation of governing physical phenomena. Furthermore, the proposed optimized sequence of machine learning algorithms (providing a mean absolute relative difference (MARD) of 8.84% on the test set) outperforms the most accurate available empirical model (with an MARD of 19.7% on the test set) by a large margin, demonstrating the efficacy of the proposed methodology.

Since the Montreal Protocol (dated 1987), the reduction of the environmental impact has been one of the main goals in the HVAC sector, which has led to the replacement of widely used fluids with new environmentally friendly ones. Nevertheless, only new fluids with suitable heat transfer features can be used. The refrigerant mixture R449a, one of the fourth-generation refrigerants, was tested during flow boiling inside a horizontal smooth tube. The experiments were carried out at six different mass fluxes G ∈ [175;400] kg·m⁻²·s⁻¹ and four different bubble temperatures T_b ∈ [2.5;10] °C, while the nominal values for inlet and outlet quality were selected as x_Ti = 0.1 and x_To = 0.9, respectively. The results highlighted that, as the bubble temperature increases, it has an opposite effect on the pressure drop per unit length and the heat transfer coefficient: the former decreases while the latter grows. The comparison between experimental results and the correlations showed that the Zhang and Webb formula provides the best prediction of pressure drop, while the models provided by Bertsch yield the most reliable predictions for the heat transfer coefficient. Nevertheless, for both quantities, other correlations with similar performances are available.

The study presents the results of an experimental investigation aimed at evaluating the performance of a water-to-water heat pump utilising R1234ze(E) and R515B in a drop-in application. Several operating conditions are tested, varying the mass flow rates and temperatures of the secondary fluids that pass through the heat exchangers while maintaining the compressor shaft rotational frequency and the vapour superheating at the evaporator outlet constant. Overall, when compared to R1234ze(E), the utilisation of R515B results in capacity and COP variations within −6.81% to +2.46% and −2.41% to +6.29%, respectively. Regarding the performance of the compressor, R515B exhibits comparable volumetric and overall efficiency, while a slightly lower refrigerant temperature at the compressor discharge is found, with differences ranging from −3.1 °C to −0.5 °C. Overall, R515B appears to be more suitable than R1234ze(E) for applications in the high-temperature range.