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The understanding of flow regimes in helical coils is essential in the design and operation of modern heat exchangers and has been studied therefore intensively over the past decades. However, due to the subjective nature in defining these flow regimes, previous studies showed conflicting results. The scope of the present study is to perform automatic flow regime recognition for helical two-phase flows by a novel two-step machine learning approach without any prior knowledge. The machine learning algorithms are trained on X-ray radiography based high-resolution high-speed void fraction measurements at adiabatic conditions. The trained machine learning models successfully predict the number of flow regimes as well as the flow regimes themselves. The classifier models show excellent classification accuracy >98%. Comparison of the experimental results with available flow regime models revealed significant discrepancies >2 orders of magnitude in the superficial liquid velocity. The novel machine learning based flow regime recognition methodology presented herein shows not only a significant improvement in the classification accuracy compared to previous studies but offers also a novel data-driven way to define and investigate the very nature of the flow regimes themselves in a more consistent and objective way., International Journal of Multiphase Flow, 161, ISSN:0301-9322, ISSN:1879-3533

The droplet size distributions in an industrial self-priming Venturi scrubber with liquid film injection have been investigated for varying gas flow rates and different submergence levels. For this purpose, imaging method analysis, commonly applied in spray measurements, was adopted and validated by means of synthetic data. The droplet size distributions characterized by probability density functions show a unimodal shape and can be satisfactorily represented by Nukiyama-Tanasawa distribution functions. For an increase in the gas flow rate, the cumulative distribution function shifts consistently to smaller equivalent droplet diameters. On the other hand, no statistically significant difference in the droplet size distribution was found for a change in the submergence level. The present results are compared with two droplet size correlations based on spray injection and commonly applied to Venturi scrubbers. Both correlations significantly overpredict the experimental results by a wide margin. In contrast, droplet size correlations developed for annular flows in straight tubes show a superior consensus in magnitude with the experimental data. The pronounced decreasing trend in droplet size with increasing gas superficial velocity, however, is weaker in our results, possibly due to the geometry and the self-priming characteristics of the Venturi scrubber., International Journal of Multiphase Flow, 142, ISSN:0301-9322, ISSN:1879-3533

Chemical Engineering and Processing, 69, ISSN:0255-2701, ISSN:1873-3204

Experiments in Fluids, 51 (3), ISSN:0723-4864, ISSN:1432-1114