Genetic Algorithm and Deep Learning to Explore Parametric Trends in Nucleate Boiling Heat Transfer Data

Exploring parametric effects in pool boiling is challenging because the dependence of the resulting surface heat flux is often non-linear, and the mechanisms can interact in complex ways. Historically, parametric effects in nucleate boiling processes have been deduced by fitting relations obtained from physical models to experimental data and from correlated trends in non-dimensionalized data. Using such approaches, observed trends are often influenced by the framing of the analysis that results from the modeling or the collection of dimensionless variables used. Machine learning strategies can be attractive alternatives because they can be constructed either to minimize biases or to emphasize specific biases that reflect knowledge of the system physics. The investigation summarized here explores the use of machine learning methods as a tool for determining parametric trends in boiling heat transfer data and as a means for developing methods to predict boiling heat transfer. Results are presented that demonstrate how a genetic algorithm and an artificial neural network (ANN) can be used to extract heat flux dependencies of a binary mixture on wall superheat, gravity, Marangoni effects, and pressure. The results provide new insight into how gravity and Marangoni effects interact in boiling processes of this type. The results also demonstrate how machine learning tools can clarify how different mechanisms interact in the boiling process, as well as directly providing the ability to predict heat transfer performance for nucleate boiling. Each technique demonstrated clear advantages depending on whether speed, accuracy, or an explicit mathematical model was prioritized.

Controlling uniform patterns by evaporation of multi-component liquid droplets in a confined geometry

A drying multi-component liquid droplet in a confined geometry leaves a uniform dried pattern. The evaporated vapors are stagnated inside the closed chamber, which induce Marangoni effects that contribute to suppress the coffee-ring pattern.

Use of Genetic Algorithms and Machine Learning to Explore Parametric Trends in Nucleate Boiling Heat Transfer Data

Exploring parametric effects in pool boiling is particularly challenging because the dependence of the resulting surface heat flux on many parameters is non-linear, and the mechanisms can interact in complex ways. Historically, parametric effects in nucleate boiling processes have most often been deduced by fitting relations obtained from physical models to experimental data, or looking for correlated trends in non-dimensionalized data. Using such approaches, observed trends are often influenced by the framing of the analysis that results from the modeling or the collection of dimensionless variables used. Machine learning strategies can be attractive alternatives because they can be constructed either to minimize biases or to emphasize specific biases that reflect knowledge of the physics of the system. The investigation summarized here explored the use of machine learning methods as a tool for determining parametric trends in boiling heat transfer data, and as a means for developing methods to predict boiling heat transfer. Results are presented that demonstrate how genetic algorithms and other machine learning tools can be used to extract heat flux dependencies on system parameters. A key element of the machine learning analysis process is preparation of the data used. Use of raw data and use of dimensionless rescaled data are explored, and the advantages and disadvantages of each are assessed. Data for nucleate boiling of a binary mixture are analyzed to determine the heat flux dependence on wall superheat, gravity, Marangoni effects and pressure. The results provide new insight into how gravity and Marangoni effects interact in boiling processes of this type. The results also demonstrate how machine learning tools can clarify how different mechanisms interact in the boiling process, as well as directly providing the ability to predict heat transfer performance for design of heat transfer devices that involve nucleate boiling. Potential use of machine learning tools on big data collections for nucleate boiling processes to more broadly assess parametric effects is also discussed.

Impact of solutal Marangoni convection on oil recovery during chemical flooding

In this study, the impacts of solutal Marangoni phenomenon on multiphase flow in static and micromodel geometries have experimentally been studied and the interactions between oil droplet and two different alkaline solutions (i.e. MgSO4 and Na2CO3) were investigated. The static tests revealed that the Marangoni convection exists in the presence of the alkaline and oil which should carefully be considered in porous media. In the micromodel experiments, observations showed that in the MgSO4 flooding, the fluids stayed almost stationary, while in the Na2CO3 flooding, a spontaneous movement was detected. The changes in the distribution of fluids showed that the circular movement of fluids due to the Marangoni effects can be effective in draining of the unswept regions. The dimensional analysis for possible mechanisms showed that the viscous, gravity and diffusion forces were negligible and the other mechanisms such as capillary and Marangoni effects should be considered in the investigated experiments. The value of the new defined Marangoni/capillary dimensionless number for the Na2CO3 solution was orders of magnitude larger than the MgSO4 flooding scenario which explains the differences between the two cases and also between different micromodel regions. In conclusion, the Marangoni convection is activated by creating an ultra-low IFT condition in multiphase flow problems that can be profoundly effective in increasing the phase mixing and microscopic efficiency.

Experimental study of the internal flow in freezing water droplets on a cold surface

The study of a freezing droplet is interesting in areas, where the understanding of build up of ice is important, for example, on wind turbines, airplane wings and roads. In this work, the main focus is to study the internal motion inside freezing water droplets using particle image velocimetry and to reveal if mechanisms such as natural convection and Marangoni convection have a noticeable influence on the flow within the droplet. The flow has successfully been visualized and measured for the first 25% of the total freezing time of the droplet when the velocity in the water is the highest and when the characteristic vortices can be seen. After this initial time period, the high amount of ice in the droplet scatters the PIV light sheet too much and the images retrieved are not suitable for analysis. Initially, it can be seen that the Marangoni effects have a large impact on the internal flow, but after about 15% of the total freezing time, the flow turns indicating increased effects of natural convection on the flow. Shortly after this time, almost no internal flow can be seen. Graphic abstract

Electrical switching of a surfactant coated drop in Poiseuille flow

Electrical effects can impart a cross-stream component to drop motion in a pressure-driven flow, due to either an asymmetric charge distribution or shape deformation. However, surfactant-mediated alterations in such migration characteristics remain unexplored. By accounting for three-dimensionality in the drop motion, we analytically demonstrate here a non-trivial switching of drop migration with the aid of a surfactant coating on its surface. We establish this phenomenon as controllable by exploiting an interconnected interplay between the hydrodynamic stress, electrical stress and Marangoni stress, manifested so as to achieve a net interfacial force balance. Our results reveal that under different combinations of electrical conductivity and permittivity ratios, the relative strength of the electric stress with respect to the hydrodynamic stress, the applied electric field direction and the surfactants alter the longitudinal and cross-stream velocity components of the droplets differently. The effect of drop deformation on its speed is found to be altered with the increased sensitivity of the surface tension to the surfactant concentration, depending on the competing effects of the electrohydrodynamic flow modification and the tip stretching phenomenon. Further, with a suitable choice of electrical property ratios, the Marangoni effects can be exploited to direct the drop in reaching a final transverse position towards or away from the channel centreline. These results may turn out to be of immense consequence in providing an insight to the underlying complex physical mechanisms dictating an intricate control on the drop motion in different directions.