Search Results (3)

A new methodology for implementing radioactive particle tracking (RPT) in bubble columns with intense vertical rod internals was developed and implemented to investigate the effect of dense internals on hydrodynamics. The methodology utilizes a hybrid of Monte Carlo N-Particle (MCNP) simulation and an automated RPT calibration device to generate a large number of calibration points for accurate reconstruction of the instantaneous positions of radioactive particles using a similarity algorithm. Measurements were conducted in a 6-inch (15.24 cm) Plexiglas column using an air–water system at a superficial gas velocity of 40 cm/s. Vertical Plexiglas rods 0.5 in (1.27 cm) in diameter were used to cover ~25% of the total cross-sectional area of the column to represent the effect of a heat-exchanging tube in industrial Fisher–Tropsch synthesis. The results showed that the internals increased liquid velocity near the center of the column by more than 30%, resulting in enhanced liquid circulation and frequency of liquid eddy movement. In addition, turbulence parameters decreased noticeably when using vertical internals in the bubble column due to a reduction in velocity fluctuations. Reliable data can help validate computational fluid dynamics (CFD) models to predict hydrodynamic parameters at other various conditions. Full article

This study focused on detailed investigations of selected local hydrodynamics in split airlift reactor by using an unconventional measurements facility: computed tomography (CT) and radioactive particle tracking (RPT). The local distribution in a cross-sectional manner with its radial’s profiles for gas holdup, liquid velocity flow field, shear stresses, and turbulent kinetic energy were studied under various gas velocity 1, 2 and 3 cm/s with various six axial level z = 12, 20, 40, 60, 90 and 112 cm. The distribution in gas–liquid phases in the whole split reactor column, the riser and downcomer sides, including their behavior at the top and bottom sections of the split plate was also described. The outcomes of this study displayed an exemplary gas–liquid phases dispersion approximately in all reactor’s zones and had large magnitude over the ring of the sparger as well as upper the split plate. Furthermore, the outcomes pointed out that the distribution of this flow may significantly impacts the performance of the split reactor, which may have essential influence on its performance particularly for microorganisms culturing applications. These outcomes are dependable as benchmark information to validate computational fluid dynamics (CFD) simulations and other models. Full article

In the past two decades, the radioactive particle tracking (RPT) measurement technique has been proven to visualize flow fields of most multiphase flow systems of industrial interest. The accuracy of RPT, and hence the data obtained, depend largely on the calibration process, which stands here as a basis for two subsequent processes: tracking and reconstruction. However, limitations in the RPT calibration process can be found in different experimental constrains and in assumptions made in the classical Monte Carlo approach used to simulate number of counts received by the detectors. Therefore, in this work, we applied a GEANT4-based Monte Carlo code to simulate the RPT calibration process for an investigated multiphase flow system (i.e., gas–liquid bubble column). The GEANT4 code was performed to simulate the number of counts received by 28 scintillation detectors for 931 known tracer positions while capturing all the types of photon interaction and overcoming solids’ angle limitations in classical approaches. The results of the simulation were validated against experimental data obtained using an automated RPT calibration device. The results showed a good agreement between the simulated and experimental counts, where the maximum absolute average relative deviation detected was about 5%. The GEANT4 model typically predicted the number of counts received by all the detectors; however, it over-estimated the counts when the number of primary events applied in the model was not the optimal. Full article