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The bubbling of particle-contaminated gases through a liquid pool, called aerosol scrubbing, is a reliable, robust, and efficient collection technique to retain harmful aerosols from industrial processes or hazardous incidents. In this multiphase mass transfer phenomenon, the two-phase flow mechanics strongly influences the particle transport from the gas in the bubble to the surrounding liquid. The numerical and experimental studies have primarily focused on ideal flows and separate test effects. Here we developed a new experimental approach to assess the aerosol mass transfer and two-phase flow hydrodynamics simultaneously via tomographic conductivity measurements using a Wire-mesh sensor and an electrolytic aerosol scrubbed in a prototypical water column. The bubble phenomenology and size distribution can be effectively captured, and the aerosol deposition coefficient can be determined by measuring the electrolytic concentration increase in the liquid phase. Our integral results of the total retained mass in the column are compared with the aerosol mass concentration determined with filter measurements showing good agreement. We study the influence of the position and presence of the wire-mesh sensor on the results. The location has minor effects due to the high mixing level of the liquid phase. It is observed that the wire-mesh sensor could increase the mass transfer by not more than 5–10 %. We finally compare our mass transfer results with algebraic model predictions and suggest improvements to detect and study the mass transfer at the local bubble scale. • Two-phase flow and aerosol transfer simultaneous measurements with Wire-mesh sensor. • Bubble and aerosol size distribution characterization of a pool scrubbing process. • Calculation of an experimental aerosol deposition coefficient using void fraction and mass flux. • Assessment of the Wire-mesh sensor accuracy and intrusiveness on aerosol retention. [ABSTRACT FROM AUTHOR]

• A high-resolution gamma tomography system (HRGTS) with an Ir-192 source is described. • Simulations and experiments thermal-hydraulic research facilty mock-ups. • Subchannel void fraction accuracy was found to be 1 The High-Resolution Gamma Tomography System (HRGTS) facility is under development at the University of Michigan. This system is planned generally for application to two-phase flow loops representing nuclear fuel bundles. These measurements should provide insight into fluid dynamics phenomena and high resolution data for validation of computational fluid dynamics (CFD) codes. The HRGTS is planned to be deployed in the Michigan Advanced Rod Bundle fLow Experiment (MARBLE). an adiabatic test loop consisting of an 8x8 square lattice arrangement. Simulations modeling MARBLE were performed to study how the counting statistics of the imaging process propagate into void fraction uncertainty. The simulation results showed that an accuracy of around 1% is expected with imaging times of 1 min. Computed tomography measurements were performed on partial mockups of the bundle geometry, which confirmed that the technique can be used to obtain accurate subchannel void fraction data in complicated and challenging measurement scenarios. [ABSTRACT FROM AUTHOR]

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. • Automatic two-phase flow pattern recognition in helical coils was performed. • X-ray radiography based void fraction measurement data was used. • Unsupervised and supervised machine learning algorithms were trained. • Bubbly, plug, slug, wavy, annular flow regimes were identified by clustering. • KNN, RF, SVM classifier models achieved excellent classification accuracy. [ABSTRACT FROM AUTHOR]

• Adiabatic air–water two-phase flows in a helically coiled tube were investigated. • Void fraction measurements were performed by means of X-ray radiography. • Flow regimes were identified and characterized. • Bubbly, plug, slug, slug-annular and annular flow patterns were encountered. • Identified flow regimes are in line with published transition criteria. Helical coil heat exchangers are known for their superior heat transfer, compactness and robustness against thermal expansion compared to systems with straight pipes. Due to all these benefits, they became quite popular in the last few decades in various industries such as pharmaceutical, chemical or nuclear. Generally, they are used in a single-phase environment or with evaporation taking place on the shell side. However, in most of the steam generator designs adopted in advanced nuclear reactors, the two-phase mixture flows upward within the vertically oriented helical coils. In addition, physical two-phase phenomena taking place inside helical tubes differ significantly from those in straight pipes due to inherent centrifugal and torsion effects. To improve the computational models used to predict the thermal hydraulic performance of such steam generators, experimental characterization and identification of different two-phase flow patterns in helical coils are essential. In the present paper, high-resolution measurements of the void fraction distribution for an adiabatic upward air–water two-phase flow were conducted in a transparent helically coiled acrylic pipe by means of high-speed X-ray radiography at ambient conditions. Various void fraction based features such as probability density function estimates, profiles over the height of the pipe cross-section and temporal evolutions were used to identify and characterize flow regimes. A total of 13 measurements are presented in this paper, classified into six flow regimes, namely: bubbly, plug, wavy, slug, slug-annular and annular flow. The obtained flow regimes are in accordance with flow regime characterizations and transition criteria already reported in literature. [ABSTRACT FROM AUTHOR]