Session F21: Bubbles: Growth, Heat Transfer and Boiling

Losing your bubbles: decarbonation rate of bubbly drinks

When a can or bottle of a carbonated drink is opened, the dissolved CO2 comes out of solution forming gas bubbles. These bubbles are responsible for making these drinks special: the perception of flavour is enhanced by bubbles. Although the diffusion-dominated bubble growth process is well understood for pure fluids, the effect of 'other' solutes is not.   Our study investigates how acidity, sweetness, alcohol content influence the rate of bubble growth and bubbling rate of CO2 supersaturated water. Experiments used deionized water with various carbon dioxide partial pressures, ranging from 3.0 bars to 4.0 bars. Synthetic 'soda' and 'beer' samples were prepared using varying concentrations and mixtures of sucrose, citric acid, ethanol, and SDS surfactant. Each carbonated fluid sample was placed in a petri dish and observed via a camera for one hour. The videos were processed to extract data showing the growth of the bubbles as a function of time. The effective diffusion coefficient was inferred by fitting the  measurements to the Epstein-Plesset prediction. We found that sweetness  is the factor that most significantly impacts the decarbonation rate and the frequency of bubble formation. Tests were also conducted with commercial soda and beer, corroborating the results found with synthetic drinks.   

Learning hidden physics from reduced-order modeling of bubble dynamics in boiling heat transfer

Boiling heat transfer is a highly effective but stochastic process where the working fluid undergoes vigorous liquid to vapor transition. Physics-based modeling of bubble dynamics during boiling is challenging due to the drastic changes in system parameters, such as nucleation, bubble morphology, temperature, pressure, and velocity. Principal component analysis (PCA), an unsupervised machine learning (ML) approach, is used to extract hidden physics from boiling heat transfer processes using in-house pool boiling experimental images. PCA relies purely on reduced order representations of bubble images and can effectively extract the dominant physical dynamics with a significant reduction in data size. The behaviors of the dominant frequency and its associated amplitude of the PCA versus the heat flux can be linked to the bubble nucleation site densities, bubble departure, coalescence, and morphology. The current approach automatically detects the boiling regimes, without data labeling and human errors.    

Nonintrusive investigation of CHF and heat transfer mechanisms associated with silica-water nanofluids and varying wetting surfaces in the context of pool boiling phenomenon

Critical heat flux (CHF) limits the performance of the pool boiling heat transfer by controlling the maximum heat flux that can be dissipated under efficient and safe operating conditions. Recent developments have seen a surge in the application of nanofluids and nanostructured surfaces to achieve enhancement in CHF. The present study focusses on understanding the boiling characteristics, CHF and heat transfer mechanisms, under the application of dilute nanofluids and varying wetting surfaces. In this direction, dilute silica-water nanofluids and silica-coated varying wetting surfaces were employed. Nonintrusive, measurements namely high speed videography and IR thermography were implemented for simultaneous mapping of bubble dynamics and temperature of the heater substrate. The recorded measurements revealed that the nanofluids significantly influence the growth and coalescence dynamics as well as the bubble base evaporation mechanisms on plain surfaces. The boiling experiments with water on varying wetting surfaces showed similar observations. The CHF was seen to increase with the employed nanofluids concentration and the increasing level of surface wettability.  A comparative performance analysis between the nanofluids and the wetting surfaces has been presented.   

Effect of surface wettability on nucleate boiling – A DNS study

Complexity in nucleate boiling arises due to non-equilibrium thermodynamics intertwined with heat, mass and momentum transport and surface processes. Wettability of surfaces plays an important role in the nucleate boiling heat transfer coefficient. We develop a direct numerical simulation model framework using our in-house TPLS Solver. The so-called Diffuse Interface Method is implemented via Cahn-Hilliard Equation to determine the vapour-liquid interface position. This method also removes stress singularity at the contact line and enables the use of contact-angle boundary conditions to prescribe surface wettability. We present simulations to analyse the effect of the superheat and bubble size for different wettabilities, especially on growth-rates and departure stages of the bubble. Our analysis shows the importance of surface tension on the departure conditions. Our simulations show that inertia-controlled growth is extremely rapid and more likely to be observed in small embryo bubble with high wettability. Conversely, the limited growth rate in the heat transfer-controlled growth is the dominant effect for low wettability cases. We compare our simulations against our nucleate boiling experiments using FC-72 on silicon surfaces.   

Nanobubble nucleation in pool boiling systems via non-equilibrium molecular dynamics simulations

Pool boiling is a complex and out of equilibrium process and key phenomena take place at different scales. Molecular interactions are particularly important as they strongly affect the resulting nano-bubble nucleation as a result of the combined effect of wettability, wall superheat and surface roughness. The interplay of these parameters is here investigated at the nanometre scale through non-equilibrium molecular dynamics simulations for a Lennard-Jones system. A rectangular cavity is considered as nucleation spot and its width-to-depth ratio is taken as a measure of the defect size effects. Heat is uniformly provided from the bottom through a tethered solid wall. Nucleation times are directly extracted from the MD data and fitted to a classical nucleation theory-based model, resulting in a good agreement. The systematic analysis of the effect of all the aforementioned parameters is summarized in a phase diagram and interesting insights into the boiling process are achieved by analysing the heat flux and temperature fields inside the nucleation spot. The conducted analysis shows a promising way to link the molecular scale to higher-scale models in a more general multiscale framework.   

Nucleate boiling heat transfer enhancement using active controlled base plate trained through deep reinforcement learning and coarse grid simulations

We demonstrate deep reinforcement learning (DRL) for two problems. We first study  drag reduction for flow past circular cylinder using transverse jets whose mass flow rate is controlled by artificial neural network. During training of the deep reinforcement learning agent, which is the jets in this case, we use coarser grid simulations for faster computations. We show that the model trained using coarser grid simulations works well with simulations at finer grid, thus saving significant computational time during the training phase. We next study the problem of nucleate boiling heat transfer enhancement at low superheats, with base plate actively controlled via DRL. Considering that Direct Numerical Simulations of nucleate boiling requires very fine grid size, during training of the DRL agent, we again perform coarser grid simulations, and then apply the learned model to finer grid independent simulations. The DRL agent shows good performance by trying to control the size and departure time of bubbes, leading to increase in convection, decrease in thermal boundary layer thickness and significant enhancement in Nusselt number.