Experimental investigation of non-uniform heating effect on flow boiling instabilities in a microchannel-based heat sink

Copyright @ 2011 ElsevierTwo-phase flow boiling in microchannels is one of the most promising cooling technologies for coping with high heat fluxes produced by the next generation of central processor units (CPUs). If flow boiling is to be used as a thermal management method for high heat flux electronics it is necessary to understand the behaviour of a non-uniform heat distribution, which is typically the case observed in a real operating CPU. The work presented is an experimental study of two-phase boiling in a multi-channel silicon heat sink with non-uniform heating, using water as the cooling liquid. Thin nickel film sensors, integrated on the back side of the heat sinks were used in order to gain insight related to temperature fluctuations caused by two-phase flow instabilities under non-uniform heating. The effect of various hotspot locations on the temperature profile and pressure drop has been investigated. It was observed that boiling inside microchannels with axially non-uniform heating leads to high temperature non-uniformity in the transverse direction.This research was supported by the UK Engineering and Physical Sciences Research Council through grant EP/D500109/1

Experimental two-phase heat transfer study of R245fa in horizontal mini-channels at high saturation temperatures

Heat transfer measurements for R254fa were conducted. The heat transfer coefficient was determined for a smooth stainless steel tube with an inner tube diameter of 3 mm. The experiments were conducted for three heat fluxes (10, 30 and 50 W/m^2), five mass fluxes (100, 300, 500, 700 and 1000 kg/(m^2.s)) and at three saturation temperatures (40°C, 70°C and 125°C). The experimental data was used to determine the influence of the saturation temperature, mass flux, heat flux and vapour quality on the heat transfer coefficient. At a low saturation temperature, the heat transfer coefficient increases with an increasing mass flux. However, at a high saturation temperature the heat transfer coefficient decreases with an increasing mass flux. Furthermore, the heat transfer coefficient increases with increasing vapour quality at a low saturation temperature. On the contrary, the heat transfer coefficient decreases at higher saturation temperatures

Flow regime based heat transfer correlation for R245fa in a 3 mm tube

241 heat transfer measurements for R254fa were conducted. The heat transfer coefficient was determined for a smooth stainless steel tube with an inner tube diameter of 3 mm. The experiments were conducted for five mass fluxes (100, 300, 500, 700 and 1000 kg/(m2 s)), three heat fluxes (10, 30 and 50 kW/m2) and at three saturation temperatures (40 °C, 70 °C and 125 °C). The experiments were used to determine the influence of the saturation temperature, mass flux, heat flux, vapour quality and flow regime on the heat transfer coefficient. At a low saturation temperature, the heat transfer coefficient increases with an increasing mass flux. However, at a high saturation temperature the heat transfer coefficient decreases with an increasing mass flux. Furthermore, the heat transfer coefficient increases with increasing vapour quality at a low saturation temperature. On the contrary, the heat transfer coefficient decreases at higher saturation temperatures. Due to the fact that most heat transfer models found in literature are developed for low saturation temperatures and one flow regime, the heat transfer coefficients predicted by the existing models do not comply very well with the experimental data. Thus, a new heat transfer correlation for R254fa was proposed. The new correlation has a Mean Absolute Error of 11.7% for the experimental data of a tube with an inner tube diameter of 3 mm. Finally, this new correlation was also verified with R245fa datasets of other authors

Development and assessment of performance of artificial neural networks for prediction of frictional pressure gradients during two-phase flow

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 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

Experimental two-phase fluid flow in microchannels

Micro or mini heat spreaders are used in the interest of providing higher cooling capability for microtechnologies. Heat spreaders using micro or mini channels are not yet well studied, for this the fundamentals of two-phase heat transfer in microchannels are being studied. Here, a comprehensive experimental two-phase flow study has been carried out on two single round tubes (D = 0.509 and 0.790 mm) and for two different fluids: R-134a and R-245fa. An optical measurement method for two-phase flow characterization in microtubes has been applied to determine the frequency of bubbles exiting a microevaporator, the coalescence rates of these bubbles and their lengths as well as their mean two-phase vapor velocity. Four principal flow patterns (bubbly flow, slug flow, semi-annular flow and annular flow) with their transitions (bubbly/slug flow and slug/semi-annular flow) were observed. A new type of flow pattern map for evaporating flow in microchannel has been developed. The first zone corresponds to the isolated bubble regime. It includes both bubbly flow or/and slug flow and is present up to the onset of coalescence. The second zone is the coalescing bubble regime. It is present up to the end of coalescence process. The third zone is the annular zone and is limited by the fourth zone of this diabatic map, the onset of critical heat flux. This flow pattern map can be used for heat transfer model and design of micro evaporator. The vapor velocity or cross sectional void fraction have been measured. For R-134a, the flow can be considered to be homogeneous (or near homogeneous). For R-245fa, more tests exhibit instabilities and surprisingly show vapor velocities below those of homogeneous flow. Frictional two-phase pressure drops have been measured over a wide range of conditions for the two microchannels and two fluids. Three regimes are distinguishable when regarding to the variation of the adiabatic frictional pressure drop with the vapor quality or the two-phase friction factor with the two-phase Reynolds number: a laminar regime for ReTP < 2000, a transition regime for 2000 ≤ ReTP ≤ 8000 and a turbulent regime for ReTP ≥ 8000. The turbulent two-phase flows are best predicted by the Müller-Steinhagen correlation. New accurate CHF data have been measured with the test facility. A new microchannel version of the Katto-Ohno correlation has been developed to predict the CHF in circular, uniformly heated microchannels. Moreover, a new transition curve from annular flow to dryout has been proposed

Factors Influencing Auditor Switching: With Covid-19 as A Moderating Variable (Empirical Study on BUMN Companies Listed on The IDX in the 2015-2022 Period)

This study aims to determine the factors that can influence auditor switching. The independent variables used in this research are modified audit opinion, ownership concentration, management change, and financial distress. This research also uses Covid-19 as a moderating variable in financial distress and auditor switching. The population in this research is Badan Usaha Milik Negara (BUMN) listed on the Indonesia Stock Exchange (IDX) for the 2015-2022 period. The sample was determined by purposive sampling method and obtained 21 companies. Data analysis conducted with binary logistic regression analysis with moderated regression analysis and using SPSS Version 27 software. The results of this study indicate that modified audit opinion and ownership concentration do not have a significant effect on auditor switching. Management change has a significant positive effect and financial distress has a significant negative effect on auditor switching. Covid-19 is not able to moderate the relationship between financial distress and Auditor Switching

Kinetic Modeling of Vacuum Gas Oil Hydrotreatment using a Molecular Reconstruction Approach

International audienceVacuum Gas Oils (VGO) are heavy petroleum cuts (boiling points ranging from 350 to 550 ˚C) that can be transformed into valuable fuels (gasolines, diesels) by fluid catalytic cracking or hydrocracking. Prior to these conversion processes, hydrotreating is required in order to eliminate the impurities in VGOs. The hydrotreatment process enables to meet the environmental specifications (total sulfur contents) and to prevent nitrogen poisoning of conversion catalysts. In order to develop a kinetic model based on an accurate VGOs molecular description, innovative analytical tools and molecular reconstruction techniques were used in this work. A lumped model using a Langmuir-Hinshelwood representation was developed for hydrodearomatization, hydrodesulfurization and hydrodenitrogenation of the VGO. This lumped model was successfully applied to the experimental feed pretreatment data and was able to predict evolution of concentration of the aromatics, nitrogen and sulfur species

Notion de position du modèle GENELEX et structuration d’une base de données syntaxiques issue des Tables du LADL

Les données syntaxiques du LADL, riches tant par leur large couverture de la langue française que par leur finesse de description, étaient jusqu’à présent restées difficiles d’accès, en raison d’un formalisme de représentation opaque. Notre travail rend directement lisibles les informations syntaxiques contenues dans les tables de verbes et unifie leur représentation par l’usage d’un cadre formel structurant : le modèle GENELEX. La base de données obtenue constitue un fonds de premier intérêt pour toute la communauté linguistique, alliant, à la richesse des données sur les comportements verbaux du français, les capacités de manipulation et de consultation d’une base de données.Syntactical data from LADL detail with an extremely fine granularity French verb syntactic behavior. Up until now, these data have been difficult to process due to a representation framework unsuited to computer processing. Our work renders these syntactic data readable and unifies their format by the use of a highly structuring framework : the GENELEX model. The resulting database is highly interesting for linguistics : it combines the richness of data on French verb behavior with database abilities

Notion de position du modèle GENELEX et structuration d’une base de données syntaxiques issue des Tables du LADL

Les données syntaxiques du LADL, riches tant par leur large couverture de la langue française que par leur finesse de description, étaient jusqu’à présent restées difficiles d’accès, en raison d’un formalisme de représentation opaque. Notre travail rend directement lisibles les informations syntaxiques contenues dans les tables de verbes et unifie leur représentation par l’usage d’un cadre formel structurant : le modèle GENELEX. La base de données obtenue constitue un fonds de premier intérêt pour toute la communauté linguistique, alliant, à la richesse des données sur les comportements verbaux du français, les capacités de manipulation et de consultation d’une base de données.Syntactical data from LADL detail with an extremely fine granularity French verb syntactic behavior. Up until now, these data have been difficult to process due to a representation framework unsuited to computer processing. Our work renders these syntactic data readable and unifies their format by the use of a highly structuring framework : the GENELEX model. The resulting database is highly interesting for linguistics : it combines the richness of data on French verb behavior with database abilities

Estimation of flow boiling heat transfer coefficient in enhanced tubes. Benchmark correlations and ANN approach

This paper addresses the critical gap in predicting the heat transfer coefficient during flow boiling in enhanced tubes, where the use of conventional correlations and predictive methods developed for smooth surfaces do not usually provide satisfactory results. For such purpose, a comprehensive database was collected from existing literature, including a wide range of operating conditions and enhanced tube geometries from several independent sources. The dataset includes mass flow rates spanning from 50 to 1000 kg/m2s, vapor qualities from the onset of boiling (x=0.0) to the dry-out occurrence and beyond (x=0.99), reduced pressures from 0.05 to 0.80, and tube diameters (measured up to the fin tip) from 0.7 to 11.9 mm, for a total amount of approximately 3000 data points. Existing flow boiling heat transfer coefficient predictive methods for enhanced tubes were implemented and tested with the present dataset, proving a limited accuracy for most of them mainly in case of testing beyond the specific parameter ranges they were developed for. Extrapolation frequently resulted in statistically poor or even non-physical outcomes. Several artificial neural network models were then developed, according to sensitivity analysis approach to look for potential input parameters and network structures. Specifically, two approaches were employed: a standard neural network model and a correlated informed neural network (CINN), integrating physical correlations into the network's architecture, thus informing the model with physical principles that govern the heat transfer process. Despite a lower overall accuracy, the correlated informed neural network demonstrated superior reliability than standard one, resulting in an instrument to improve the accuracy of existing correlations