Machine learning based models for pressure drop estimation of two-phase adiabatic air-water flow in micro-finned tubes: Determination of the most promising dimensionless feature set

Recent advances in the applications of machine learning methods for heat exchanger modeling—a review

“…In the context of pressure drop predictions discussed thus far, it is of considerable importance to recognize the frictional pressure drop as a major component contributing to the overall pressure losses. Some studies have focused on investigating the frictional pressure drop in heat exchangers, such as Najafi et al (Najafi et al, 2021), Xie et al (Xie et al, 2022), Skrypnik et al (Skrypnik et al, 2022), Peng and Ling et al (Peng and Xiang, 2015) and Du et al (X. Du et al, 2020), introduced the estimation model of the friction factor using different machine learning methods.…”

“…Du et al, 2020), introduced the estimation model of the friction factor using different machine learning methods. Najafi et al (Najafi et al, 2021) demonstrated that data-driven estimation of frictional pressure drop provides greater prediction accuracy compared to theoretical physical models (Chisholm, 1967) for two-phase adiabatic air-water flow in micro-finned tubes using the Random Forest model. Their research focused on five dimensionless features (xv, Re, (1 − x)/ x, Re f , Re go ) selected from 23 features which are slightly different features compared to selection of Ardam et al (Ardam et al, 2021).…”

Review of Machine Learning for Hydrodynamics, Transport, and Reactions in Multiphase Flows and Reactors

“…Persistent lack of physical comprehension continuously stymies preferable prediction performance of the key parameters in multiphase flow and reactor systems, although scientists have made systematic contributions to experimentally formulated correlations throughout the past decades. − The correlations of the key parameters in multiphase units are commonly expressed by gas/liquid/solid phase properties, operating conditions (e.g., phase concentration, velocity, and temperature), devices configurations (e.g., height and diameter), or a combination of them in dimensionless forms such as Archimedes, Froude, Nusselt, Reynolds, Sherwood, and Weber numbers. However, the prediction discrepancies between the existing empirical correlations of key parameters such as the particle entrainment and minimum fluidization velocity in gas-particle riser flows can reach several orders of magnitude. , Fortunately, the advanced research and development of flexible ML tools have the potential to complement the incomplete knowledge to boost the prediction ability of key multiphase field parameters such as mass flow rate/flux, − minimum fluidization velocity, , mixing rate/index, , overall/local hold-up, − pressure/pressure drop, − velocity, ,− temperature, − and other parameters − in multiphase/particulate flows and reactors. Note that interested readers may be referred to a relatively comprehensive list of the existing literature summarized in Table S4.…”

Correlated-informed neural networks: A new machine learning framework to predict pressure drop in micro-channels

“…In the proposal of new methods, in the last decade (2010-2020), researchers have proposed alternative phenomenological models based on machine learning to assist the mechanical engineering in complex phenomena related to fluid dynamics, being the Artificial Neural Networks (ANN) the most studied tool. Some applications of ANNs for pressure drop prediction include flow pattern recognition and pressure drop of two-phase flow [7], air-water pressure drop in vertical channels [8], pressure drop in horizontal channels [9], pressure drop in fluidized beds [10], pressure drop in venturi scrubbers [11], pressure drop during condensation in inclined smooth tubes [12], and convective heat transfer and pressure drop in boiling and condensation [9,13,14]. A common feature of these works is the excellent correlation degree reported by the ANN models.…”