Statistical simulator for synthesizing disturbance waves in downward vertical annular flow

  • Ana Luiza Beltrão Santana*
  • , Natan Schieck Reginaldo
  • , Moises A. Marcelino Neto
  • , Rigoberto E. M. Morales
  • , Marco Da Silva
  • , Eduardo Nunes dos Santos*
  • *Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

Annular flow is a gas-liquid flow regime commonly encountered in industrial applications, such as those in nuclear power and oil and gas production. While upward configurations have received substantial attention, detailed investigations of downward vertical annular flow remain comparatively limited. This study presents a statistical synthesis simulator capable of generating representative annular flow time series using only supervisory process parameters typically available in industrial environments, including pipe diameter, volumetric flow rates, and fluid properties (density and viscosity). Experiments were performed in two test sections with internal pipe diameters of 26 mm and 50 mm, where film thickness time series were recorded using a non-intrusive, high-speed conductance sensor. The acquired data were analyzed to extract key characteristics of the disturbance waves, including frequency, amplitude, velocity, and wave shape, which were then used to formulate empirical correlations governing the generation of synthetic unit waves. Using the combined 26-mm and 50-mm databases, the fitted correlations achieved R2 values of 0.91–0.99 for the main unit-wave geometrical parameters. These correlations were incorporated into the simulator to construct the synthetic film-thickness time series, from which the mean liquid fraction is computed and compared to experimental data. The results fall predominantly within the ±30 % band, with overall errors of approximately MAPE ≈21 %.
Original languageEnglish
Article number103190
Number of pages16
JournalFlow Measurement and Instrumentation
Volume108
Early online date06 Jan 2026
DOIs
Publication statusE-pub ahead of print - 06 Jan 2026

Fields of science

  • 202016 Electrical engineering
  • 202015 Electronics
  • 202012 Electrical measurement technology
  • 202027 Mechatronics
  • 202037 Signal processing
  • 202036 Sensor systems

JKU Focus areas

  • Digital Transformation

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