Statistical Characterisation of Pressure Drop Fluctuation in Two-Phase Air-Water Flow Downstream of a Minichannel T-Junction

Abstract:

The movement of gases and liquids in a minichannel with a T-junction during two-phase flow is also important in fields like medicine, chemistry, and thermal management. The dynamics of the influence of the bend radius on the downstream pressure difference are not well comprehended. This paper examines the statistical characterisation of the pressure drop downstream of a horizontal T-junction in a minichannel, considering the observed flow patterns. The geometric parameters of the T-junction involved are variations of the ratio of the bend radius to the hydraulic diameter (r/D_h = 0.5, 0.7 and 1.0). Air and water are used as test fluids, with superficial velocities ranging from 0.59 to 2.96 m.s^-1 for air (J_g) and 0.63 to 3.19 m.s^-1 for water (J_l). The pressure sensors are used to measure pressure drop signals (ΔP_2-3), which are recorded by a data collection device at 1000 Hz. A high-speed camera is also used to record the flow and verify the flow regime. There are six downstream flow regimes identified, and they include: Bubbles, Bubble to Slug, Slug, Elongated Slug, Churn to Elongated Slug, and Churn. These flow patterns are characterised using statistical, spectral, and nonlinear analysis methods. The findings suggest that as the bend radius increases, the amplitude of fluctuations also increases and the probability distribution becomes wider. However, there is a possibility that larger bend radii decrease chaos levels, resulting in a characteristic regime pattern. Additionally, there are artificial neural networks (ANN) that utilise wavelet energy variance as input, achieving a classification accuracy of 85.5%. The ordered association between statistical characterisation and regime classification through ANN is useful in comprehending the impact of the instability due to the bend radius in multiphase flow. These results complement the basic knowledge and predictive modelling of pressure drops in minichannels with horizontal T-junctions.

• Periodical: Defect and Diffusion Forum (Volume 453)
• Pages: 123-145
• DOI: https://doi.org/10.4028/p-E43uIA
• Online since: July 2026
• Authors: Untung Surya Dharma*, I. Gusti Ngurah Bagus Catrawedarma, Indarto Indarto, Deendarlianto Deendarlianto
• Keywords: ANN, Classification of Flow Patterns, Minichannel T-Junction, Pressure Drop, Statistical Characteristic, Two-Phase Flow

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