Search

• 1 Flow and boiling heat transfer in a 5 mm tube were studied experimentally. • 2 Heat transfer enhancement is obviously founded in the region where x e < 0.05. • 3 New empirical correlations are developed based on the experimental data. • 4 New correlations have higher prediction accuracy than the existing correlations. Experimental investigation on flow boiling heat transfer in a small tube under high system pressure has been conducted in this study. The test-section is constructed with a 316L stainless steel tube. The inner diameter and heated length of the test-section are 5 mm and 930 mm, respectively. The working fluid is deionized water. The operating conditions of the experiments are as follows, the mass flux from 548 to 978 kg/(m2·s), the wall heat flux from 136 to 238 kW/m2, and the system pressure from 1.0 to 2.9 MPa. The experimental results showed that the inlet subcooling mainly affects the onset of nucleate boiling, but its impact on the heat transfer coefficient at the same vapor quality is almost negligible. The heat transfer coefficient increases with the increase of mass flux and wall heat flux, especially in the region where x e < 0.02. The average heat transfer coefficient is higher under high system pressure owing to the variation of the thermophysical properties. Additionally, the existing flow and heat transfer correlations cannot predict the flow boiling heat transfer coefficient and frictional pressure drop in the experiments accurately enough. Therefore, the new flow and heat transfer correlations are developed based on the experimental data in this paper, and the mean absolute deviation of the flow boiling heat transfer correlation and frictional pressure drop correlation are 13.3% and 12.1%, respectively. [ABSTRACT FROM AUTHOR]

The study of bubble dynamics is important in various engineering applications as it can provide a fundamental understanding of the bubbly flow behavior. This paper presents a novel method for tracking bubble trajectories and classifying bubble behaviors, including formation, extinction, continuous movement, fragmentation, and coalescence. The method is based on the theory of perfect matching in bipartite graphs. By establishing the perfect matching between bubbles identified at adjacent time instants, judging the continuity of bubble motion, and simply calculating the number of bubbles in the neighbor of matching bubbles at adjacent time instants, the method can deal with the challenging situation where the bubble position and volume significantly change between two adjacent time instants. The method is validated in a simulated vertical pipe boiling flow, showing that the tracking accuracy is 100% and classification accuracies for continuous movement, fragmentation, and coalescence events are estimated to be larger than 90% in a wide range of time steps between two adjacent time instants. • A Kuhn-Munkres-based algorithm for precise multi-bubble tracking is proposed. • Multi-bubble behavior through changes in bubble position and quantity is classified accurately. • A quantitative analysis of tracking accuracy in a 2D pipe bubble flow by LBM is conducted. • 100% tracking accuracy and >90% classification accuracy over wide time steps are achieved. [ABSTRACT FROM AUTHOR]

• Flow boiling of refrigerants in small to micro tubes and channels. • Two-phase pressure was obtained for different refrigerants, geometries and operating conditions. • Past pressure drop correlations did not compare well with current data bank on two-phase pressure drop. • A new correlation for predicting two-phase pressure drop in flow boiling in small to micro passages is proposed. An accurate and acceptable correlation for the prediction of two-phase pressure drop is considered a crucial step in heat exchanger design. Although many existing models and correlations were developed and proposed in the past, their ability to predict within acceptable error bands when applied in general to flow boiling flows is limited, even within their originally recorded ranges. The discrepancies are worse when predicting two-phase pressure drop in small to micro-scale passages. Therefore, the aim of the work described in this paper is to assess the most well-known models and correlations using a large experimental data-bank. The data-bank includes four refrigerants, namely R134a, R245fa, HFE-7100 and HFE-7200, small to micro heat exchangers made of different metals and different channel configurations. In addition, the data cover a large range of operating conditions, which can allow generally applicability of new correlations developed. This range covers channel hydraulic diameter of 0.46−4.26 mm, heated length of 20−500 mm, system pressure of 1 − 14 bar, mass flux of 50−700 kg/m2 s, wall heat flux ranging from 2 to 234 kW/m2 and exit vapour quality up to one. Twenty six existing models and correlations that were developed and proposed for vertical/horizontal flow, single/multi-channels and circular/non-circular channels were evaluated. Moreover, the effect of using different equations for calculating the two-phase mixture viscosity, void fraction, Fanning friction factor and Lockhart–Martinelli parameter was assessed and discussed. The mean absolute error of the existing correlations when compared with our data-bank was more than 30 %. Therefore, a new correlation for calculating the two-phase multiplier, which was strongly dependent on the Boiling number and the Lockhart–Martinelli parameter, was developed and then the frictional component and total two-phase pressure drop relationships were completed. [ABSTRACT FROM AUTHOR]

Closure models for transient cryogenic quench flow boiling heat transfer prediction are finding significant attention to support the design of energy-efficient, cost-effective, and reliable cryogenic fluid management systems. In this regard, the characterization of the film boiling crisis, which indicates the beginning of transient wetting of the wall by liquid, is essential due to the significant heat transfer change past this stage. In the current work, a new rewetting database is developed from 112 chill-down experiments conducted using LCH 4 , LOX, LAr, and LN 2 in feed lines having 0.11 m to 11 m length, 0.4 cm to 2.2 cm inner diameter, 0.5 mm to 5 mm wall thickness, and low-thermal conductive coating thickness up to 157 μm, under several pipe orientations. Additionally, the dataset comprises a mass flux from 10.5 kg / m 2 s to 1805 kg / m 2 s , pressure from 109.8 kPa to 1153.8 kPa and gravitational acceleration from terrestrial condition to ±0.01 g. Subsequently, 30 existing seminal rewetting correlations are analyzed for their applicability to transient cryogenic rewetting prediction. Moreover, the importance of feed line and flow parameters in this phenomenon is also studied. Accordingly, a new correlation for rewetting temperature is proposed with 4.15% mean absolute percentage error (MAPE). Further, applying a low-thermal conductive coating to the feed line inner wall surface has already been proven to enhance the chill-down efficiency up to 80%. For modeling such feed systems, determining bulk wall rewetting point is necessary. Hence, a new apparent rewetting temperature correlation to characterize the film boiling crisis in cryogenic coated tubes is proposed for the first time with a MAPE of 4%. Finally, the paper presents the predictions from a non-homogeneous two-phase thermal-hydraulic code, employing the previously reported film boiling correlations and the rewetting correlations from the present work. The obtained model performance indicates considerable improvement in prediction while using the proposed closure models. This shows their potential to support optimization tasks to achieve higher chill-down efficiency. • LCH 4 , LOX, LAr, and LN 2 quench rewetting database is developed from 112 experiments. • 30 existing rewetting temperature correlations are comprehensively reviewed. • The effect of the feed line and flow parameters on the rewetting phenomenon is studied. • Proposed rewetting temperature correlations for bare and coated tubes with 4.15% and 4% MAPE, respectively, employing statistical methods and genetic algorithm. • Thermal-hydraulic code prediction performance with proposed closure models is presented. [ABSTRACT FROM AUTHOR]

• Optimize the thermosyphon loop thermal performance by controlling loop wettability. • Long-term effective hydrophilic and hydrophobic modification method was determined. • The optimum wall wettability under different filling ratios was clarified. • The optimized R loop is reduced by 6.9% to 53.5% under different filling ratios. The behavior of vapor-liquid two-phase transport within the riser of the two-phase thermosyphon loop significantly impacts the heat transfer process from the evaporation to the condensation, as well as the circulation velocity, thus playing a crucial role in the heat transfer performance. However, existing research has predominantly focused on optimizing the performance of the evaporator, overlooking the optimization of vapor-liquid two-phase flow patterns and transport characteristics within the loop. This paper aims to optimize the heat transfer performance of the two-phase thermosyphon loop by controlling the wall wettability of the loop. The strategy employed involves modifying the wettability of the riser to regulate the two-phase flow within the loop, while visualization techniques are utilized to investigate the influence of riser wettability on the vapor-liquid two-phase distribution and heat transfer performance of the two-phase thermosyphon loop. The findings reveal that the impact of wall wettability on flow behavior and heat transfer performance within the loop varies significantly with changes in the filling ratio. This paper sheds some light on the riser wettability of two-phase thermosyphon loops to optimize the thermal performance. [ABSTRACT FROM AUTHOR]

• Artificial Intelligence to predict two-phase frictional pressure gradient. • Collection of a pressure drop database for a wide range of operating conditions. • Effect of the output and input type and numerosity on prediction accuracy. • Good accuracy with non-dimensional output and mixed input values. • Conventional methods always preserve physical trend with respect to neural networks. This paper presents a discussion on several possibilities to predict the frictional pressure gradient during two-phase flow, with both the application of artificial intelligence and the implementation of conventional correlations and predictive methods. To this purpose, a huge database of approximately 8000 data points has been collected from 49 sources available in scientific literature, including 23 working fluids and the following ranges of parameters: mass fluxes from 32.7 to 2000 kg/m2s, saturation temperatures from -190°C to +120°C (reduced pressures from 0.021 to 0.780), tube diameters from 0.5 to 14.0 mm. This consolidated database has been used to train several artificial neural networks (ANNs), by using only two hidden layers (shallow neural networks) and evaluating the effect of: training and testing datasets choice (either test data included or outside the training domain), the number of neurons for each hidden layer (from 1 to 50), the type of output (either dimensional or non-dimensional), the type and number (from 1 to 22) of input parameters. The best results (MAPE of 16.8% and 88% of data within ±30%) have been obtained by using the liquid-only two-phase multiplier Φ L O 2 as non-dimensional output and 12 mixed input parameters. Compared to the statistics of well-established literature correlations for frictional pressure drop (best MAPE of 22% and 73% of data points predicted within a ±30% error range, provided by Mauro et al. mechanistic method), the ANN demonstrates therefore a higher general accuracy. However, the use of Artificial Neural Networks does not guarantee a physical trend, which is instead preserved with conventional prediction methods. [ABSTRACT FROM AUTHOR]

• Physics-Informed neural networks (PINNs) are used for single and two-phase flows. • The study investigates the rising bubble problem with and without heat transfer. • The pertinent physics of wake interaction and thermal boundary layer are discussed. • Physics-Informed neural networks are agnostic to geometry and fluid properties. • The maximum error in the position of the centre of mass by PINN is less than 2.8 %. This paper presents data-driven simulations of two-phase fluid processes with heat transfer. A Physics-Informed Neural Network (PINN) was applied to capture the behaviour of phase interfaces in two-phase flows and model the hydrodynamics and heat transfer of flow configurations representative of established numerical test cases. The developed PINN approach was trained on simulation data derived from physically based Computational Fluid Dynamics (CFD) simulations with interface capturing. The present study considers fundamental problems, including tracking the rise of a single gas bubble in a denser fluid and exploring the heat transfer in the wake of a bubble rising close to a heated wall. Tracking of a rising bubble phase interface of fluids with disparate properties was performed, revealing a maximum error of only 5.2% at the interface edge and a maximum error of 2.8% at the position of the centre of mass. Inferred (hidden variable) flows are studied in addition to a purely extrapolative inverse isothermal bubble case. When no velocity data was supplied, velocity field predictions remained accurate. Rise of an inferred isothermal bubble with unseen fluid properties was found to produce a maximum mean-squared error of 0.28 and centre of mass error of 1.25%. For the case of the rising bubble with a hot wall, the maximum error in the temperature domain using specified boundary conditions was 6.8%, while the bubble position analysis reveals a maximum positional error of 3.6%. These results demonstrate that PINN is agnostic to geometry and fluid properties when studying the combined effects of convection and buoyancy on two-phase flows for the first time. This work serves as a starting point for PINN in multiphase cases involving heat transfer over a range of geometries. Eventually, PINN will be used in such cases to provide solutions for forward, inverse, and extrapolative cases. Each of which represent a dramatic saving in computational cost compared to traditional CFD. Contours of volume fraction (α) and temperature (T*) predicted by the CFD (Top) and PINN (Bottom) for the evolution of flow showing how the rising bubble influences the thermal boundary layer on the wall. [Display omitted] [ABSTRACT FROM AUTHOR]

• A review of experimental and CFD methods used to analyze the thermal-hydraulic characteristics of sub-channels in rod bundle geometries is presented. • Both adiabatic and boiling two-phase flow studies are examined. • The effects of the design and flow parameters on the two-phase flow hydrodynamics and heat transfer performance are summarized. • Based on previous numerical studies, a basic CFD model for two-phase flows in rod bundle geometries is proposed. Thermal-hydraulic analysis of two-phase flow plays a crucial role in the optimal design of nuclear fuel assemblies and in nuclear safety. This paper presents the state of the art in two-phase flow hydrodynamics and heat transfer in nuclear fuel assemblies. A comprehensive review of experimental and CFD methods used in the last 30 years to analyze the thermal-hydraulic characteristics of sub-channels in rod bundle geometries under different flow conditions (adiabatic and boiling two-phase flow) is presented. The effects of design parameters, e.g. rod-pitch length, mixing vane angle, and flow parameters, e.g. pressure, mass flux and heat flux, on flow hydrodynamics and heat transfer performance are summarized. In addition, the capabilities of CFD modelling of two-phase in rod bundles are discussed. Moreover, based on the existing studies, some recommendations for future studies are given. [ABSTRACT FROM AUTHOR]

• Obtained local surface temperature (T w), boiling curves and liquid loss rate (m loss). • Heat transfer rate (Q w) and m loss increase markedly at onset of nucleate boiling. • Jet outlet velocity (v) affects Q w and m loss more obviously than impact height (H). • Effects of v and H on T w are more significant at non-phase transition stage. • Growing of Q w and M loss slows down at anaphase of nucleate boiling. Combining the benefits of jet impact and phase-transition heat transfer, jet impact boiling has been widely adopted in industrial scenarios. Notably, owing to limitations of high-velocity jets, such as high impact stress and pumping pressure in liquid circuits, low-velocity jet impacting deserves more attention and explorations. In this paper, under an atmospheric environment with wall heating temperatures ranging from 36 °C to 128 °C, the two-phase flow and heat transfer characteristics (local surface temperature, boiling curves and liquid loss rate) of low-velocity jet impacting on a cylindrical surface are investigated by experiment, followed by the analysis on the effects of jet outlet velocity and impact height. The findings reveal that, with the increase of heat transfer rate, the heat transfer regimes in sequence are non-phase transition (single-phase convection and air-liquid two-phase convection) and nucleate boiling. The influences of jet impact height and outlet velocity on local surface temperatures are pronounced at non-phase transition stage. In impact region, along with average superheat, the growth rates of heat transfer rate and liquid loss rate increase significantly from non-phase transition to nucleate boiling stage, and slow down at anaphase of nucleate boiling stage. Interestingly, the increase in outlet velocity delays the onset of slowdown in growth of liquid loss rate to some extent. Overall, the heat transfer rate and liquid loss rate are significantly enhanced at higher jet outlet velocity of 0.60 m/s, but not affected significantly by impact height. [ABSTRACT FROM AUTHOR]

• A revised self-induced jet impingement device is introduced in pool boiling using R1336mzz(Z). • Liquid-vapor separation promoted by the revised device is confirmed from the visualization results. • The revised device does nearly no effort on the nucleate boiling heat transfer coefficient until a relatively high heat flux. • Parametric investigations on the characteristics of the revised device are conducted. • The revised device gains up to 69.5 % and 36.7 % in the critical heat flux and the maximum nucleate boiling heat transfer coefficient, respectively. Pool boiling performance enhancement has attracted considerable interest on high power electronic cooling. In this paper, we introduce a self-induced jet impingement device for practical boiling enhancement, achieved by integrating the guidance tube with an orifice plate. With R1336mzz(Z) as the working fluid, we conduct visualization and parametric investigations on the pool boiling performance, considering the varying characteristics of this device. Visualization snapshots confirm the liquid-vapor separation and indicate that the two-phase flow pattern in the guidance tube eventually stabilizes as a churn-annular flow. Our findings suggest that self-induced liquid jet impingement has a minimal effect on the nucleate boiling heat transfer coefficient (h NB) under a certain heat flux, around 75 % CHF of the standard pool boiling, as the single-phase heat transfer is weakened due to the low thermal conductivity of R1336mzz(Z). As the heat flux intensifies, we observe substantial enhancements in both the Critical Heat Flux (CHF) and the Maximum Nucleate Boiling Heat transfer coefficient (h MNB) resulted from the delayed onset of boiling crisis, owing to the additional liquid supply and the promotion on vapor exhausting. The parametric studies reveal that the flow rate of the liquid jet, amplified by either extending the length or the inner diameter of the guidance tube, or by increasing the number of jet holes, positively impacts the boiling performance. However, these improvements show diminishing returns. Our study shows enhancements in CHF and h MNB of up to 69.5 % and 36.7 %, respectively, compared to the standard pool boiling. [ABSTRACT FROM AUTHOR]