Interfacial area measurement with new algorithm for grouping bubbles by diameter

Many industrial systems make use of two-phase flows for processing or safety applications. Modeling these flows is essential for ensuring safety and engineering optimization. In general, these flows are modeled using the two-fluid model. Two key parameters in the two-fluid model are void fraction and interfacial area concentration. To improve model accuracy, the bubbles are typically broken up into groups based on transport properties and modeled separately. Such models must be validated using experimental data, which is often collected using intrusive probes such as electrical conductivity or optical void probes. Current algorithms for converting the signals from these instruments into void and interfacial area measurements struggled with missing interfaces due to signal rise and fall time. These types of instruments also use the chord length to classify the "group" of a bubble, which can lead to incorrect grouping behavior. In this paper, a new dynamic signal processing method and a grouping algorithm based on calculating bubble diameter have been introduced in an attempt to correct these inaccuracies. The ability of the new algorithm is to correctly identify smaller bubbles and to more accurately identify bubble signals is demonstrated by comparison of the output logic pulse for both the old and new algorithms with the same input signal. The new bubble size calculation means that a number of bubbles that were previously classified as "spherical/distorted" are now classified as "cap/slug/churn" bubbles. This leads to changes in average bubble properties. While these changes were expected in several cases, the increase was larger than the reported uncertainty of the instruments. This may indicate significant shortcomings in data analyzed using the previous algorithms. Additional data collection and analysis is required in order to evaluate this possibility. However, the new algorithm has a significant weakness: the bubble diameter calculation increases the computational time by an order of magnitude.