Nucleate boiling is perhaps one of the most efficient cooling methodologies due to its large heat flux with a relatively low superheat. Nucleate boiling often occurs on surfaces oriented at different angles; therefore, understanding the behavior of bubble growth on various surface orientations is of importance. Despite significant advancement, numerous questions remain regarding the fundamentals of bubble growth mechanisms on oriented surfaces, a major source of enhanced heat dissipation. This work aims to accurately measure three-dimensional (3D), space- and time-resolved, local liquid temperature distributions surrounding a growing bubble on oriented surfaces that quantify the heat transfer from the superheated liquid layer during bubble growth. The dual tracer laser-induced fluorescence thermometry technique combined with high-speed imaging captures transient 2D temperature distributions within a 0.3 ºC accuracy at a 30 μm resolution. The results show that the temperature close to the heated surface and bubble interface exhibits an acute transient behavior at the time of bubble departure, and the growing bubble works as a pump to remove heat from the surface with a temperature difference of up to 10 °C during its growth and departure. The experimental results are compared with data available in the literature to validate the accuracy of the technique. It was found that the heat transfer coefficient close to the bubble interface and heater is approximately 1.3 times higher than the heat transfer coefficient in the bulk liquid.
more »
« less
Evolution of Heat Transfer in Pool Boiling in Contaminated Water
Abstract Boiling heat transfer serves as an efficient mechanism to dissipate large amounts of thermal energy due to the latent heat of phase change. In academic studies, typically ultra-pure deionized (DI) water is used to avoid contamination. However, in industrial and commercial settings, the working fluid might be contaminated with sediments, dust, salts, or organic matter. Long-term boiling processes in non-DI water cause substantial build-up of a stable layer of deposit that dramatically reduces the heat transfer coefficient. Therefore, heating applications in a contaminated medium demand strategies to prevent such fouling. Here, we studied the use of lubricant infused surfaces (LIS) and their ability to possibly minimize the deposition of calcium sulfate. Aluminum samples were infused with Krytox 102 oil and the heat transfer coefficient was investigated at a vertical and horizontal surface orientation. Fouling effects were introduced by pool boiling for 7.5 hours in a 6.97 mM calcium sulfate solution at constant heat flux. Heat flux curves for both plain aluminum and LIS were calibrated before contamination. Initially, the LIS was unable to support a nucleate phase and transitioned directly from liquid convection to film boiling heat transfer. Upon partial degradation of the lubricant layer during long-run experiments, nucleate boiling ensued. Over 7.5 hours, the heat transfer coefficient of each sample (Al and LIS) degraded between 5.4% and 7.9% with no significant correlation with either lubricant treatment or surface orientation. Post boiling profilometry was conducted on each sample to characterize the thickness and distribution of the calcium sulfate layer. In these experiments, the plain aluminum surface outperformed the LIS at both orientations in minimizing calcium layer thickness. The LIS oriented vertically outperformed the LIS oriented horizontally.
more »
« less
- Award ID(s):
- 1856722
- PAR ID:
- 10218708
- Date Published:
- Journal Name:
- Proceedings of the ASME 2020 18th International Conference on Nanochannels, Microchannels, and Minichannels
- Page Range / eLocation ID:
- ICNMM2020-1041
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
Extensive research has been conducted to resolve small-scale microlayer and bubble nucleation and departure processes in flow boiling, building on controlled pool boiling studies. Large-scale two-phase flow structures, such as Taylor bubbles, are known to locally modify transport due to their wakes and varying surrounding liquid film thickness. However, the effect of interaction of such large-scale flow processes with bubble nucleation is not yet well characterized. Wakes may drive premature nucleating bubble departure, or conversely, suppress boiling due to boundary layer quenching, significantly affecting overall heat transfer. To explore such phenomena, a two-phase flow boiling visualization facility is developed to collect simultaneous high-speed visualization and infrared (IR) thermal imaging temperature distribution data. The test cell channel is 420 mm long with a 10 mm × 10 mm internal square-cross section. A transparent conductive indium tin oxide (ITO) coated sapphire window serves as a heater and IR interface for measuring the internal wall temperature. The facility is charged with a low boiling point fluid (HFE7000) to reduce uncertainties from heat loss to the laboratory environment. Vertical saturated flow boiling wake-nucleation interaction experiments are performed for varying liquid volume flow rates (0.5 − 1.5 L min-1, laminar-to-turbulent Re) and heat fluxes (0 − 100 kW m-2). Discrete vapor slugs are injected to explore interactions with nucleate boiling processes. By measuring film heater power, surface temperature distributions, and pressures, local instantaneous heat transfer coefficients (HTC) can be obtained. Results will be applied to assess simulations at matched conditions for void fraction, and size statistics of flow structures.more » « less
-
Abstract As technology becomes increasingly miniaturized, thermal management becomes challenging to keep devices away from overheating due to extremely localized heat dissipation. Two-phase cooling or flow boiling in microspaces utilizes the highly efficient thermal energy transport of phase change from liquid to vapor. However, the excessive consumption of liquid-phase by highly localized heat source causes the two-phase flow maldistribution, leading to a significantly reduced heat transfer coefficient, high-pressure loss, and limited flow rate. In this study, flow boiling in a two-dimensional (2D) microgap heat sink with a hydrophilic coating is investigated with bubble morphology, heat transfer, and pressure drop for conventional (nonhydrophilic) and hydrophilic heat sinks. The experiments are carried out on a stainless steel (SS) plate, having a microgap depth of 170 μm using de-ionized (DI) water at room temperature. Two different hydrophilic surfaces (partial and full channel shape) are fabricated on the heated surface to compare the thermal performance with the conventional surface. Vapor films and slugs are flushed quickly on the hydrophilic surfaces, resulting in heat transfer enhancement on the hydrophilic heat sink compared to the conventional heat sink. The channel hydrophilic heat sink shows better cooling performance and pressure stability as it provides a smooth route for the incoming water to cool the hot spot. Moreover, the artificial neural network (ANN) prediction of heat transfer coefficient shows a good agreement with the experimental results as data fit within ±5% average error.more » « less
-
Abstract Jet impingement can be particularly effective for removing high heat fluxes from local hotspots. Two-phase jet impingement cooling combines the advantage of both the nucleate boiling heat transfer with the single-phase sensible cooling. This study investigates two-phase submerged jet impingement cooling of local hotspots generated by a diode laser in a 100 nm thick Hafnium (Hf) thin-film on glass. The jet/nozzle diameter is ∼1.2 mm and the normal distance between the nozzle outlet and the heated surface is ∼3.2 mm. Novec 7100 is used as the coolant and the Reynolds numbers at the jet nozzle outlet range from 250 to 5000. The hotspot area is ∼ 0.06 mm2 and the applied hotspot-to-jet heat flux ranges from 20 W/cm2 to 220 W/cm2. This heat flux range facilitates studies of both the single-phase and two-phase heat transport mechanisms for heat fluxes up to critical heat flux (CHF). The temporal evolution of the temperature distribution of the laser heated surface is measured using infrared (IR) thermometry. This study also investigates the nucleate boiling regime as a function of the distance between the hotspot center and the jet stagnation point. For example, when the hotspot center and the jet are co-aligned (x/D = 0), the CHF is found to be ∼ 177 W/cm2 at Re ∼ 5000 with a corresponding heat transfer coefficient of ∼58 kW/m2.K. While the CHF is ∼ 130 W/cm2 at Re ∼ 5000 with a jet-to-hotspot offset of x/D ≈ 4.2.more » « less
-
Abstract Combining high-speed video cameras and optical measurement techniques with digital sensors controlled by a data acquisition system can provide an effective means of exploring boiling process thermophysics and heat transfer mechanisms. Imaging can provide qualitative and quantitative information that complements data provided by temperature, pressure, and other sensors. This paper summarizes the results of an exploration of machine learning strategies to optimally combine and analyze boiling process images and digital sensor information from experiments. We specifically sought a convolution neural network (CNN) to analyze the vaporization of deposited water droplets on superheated surfaces that may have varying degrees of nucleate boiling effects. Two specialized CNN models were developed in this study that can simultaneously analyze both image and digital data. One of our CNN model designs (case B) was trained to take an image of the vaporization process and nonthermal digital data as input and predict thermal heat transfer performance. This model predicts performance remarkably well given its nonthermal inputs, matching independent heat flux test data to a root-mean-square percent error (RMSPE) of 10.3%. This model appears to learn how the variations of nucleate boiling, vapor recoil activity, and local dryout over the surface vary with surface temperature and/or heat flux from changes in boiling system images. We also describe a CNN model (case C) that takes digital nonthermal data, digital thermal data, and image information and provides a high-fidelity prediction of vaporization heat transfer performance. This model predicted performance very well—better than our conventional fit to data (case A) and on par with best fits to quality nucleate boiling heat transfer data in the literature. This type of trained model fit independent heat flux test data to an RMSPE of 5.8%. Our results indicate that training this type of model which predicts performance from input image information and digital operating condition thermal data makes the resulting predictive model more accurate and robust. The successful use of the hybrid CNN models described here suggests that there is a strong correlation between two-phase morphology variations and changes in heat transfer performance. The hybrid CNN modeling approach developed in this research appears to be a promising strategy for analyzing experimental data for physical systems that are best investigated experimentally with combined use of imaging and digital sensor instrumentation. Possible use of this type of modeling in other systems is also discussed.more » « less
An official website of the United States government

