Two-phase thermal management offers cooling performance enhancement by an order of magnitude higher than single-phase flow due to the latent heat associated with phase change. Among the modes of phase-change, boiling can effectively remove massive amounts of heat flux from the surface by employing structured or 3D microporous coatings to significantly enlarge the interfacial surface area for improved heat transfer rate as well as increase the number of potential sites for bubble nucleation and departure. The bubble dynamics during pool boiling are often considered to be essential in predicting heat transfer performance, causing it to be a field of significant interest. While prior investigations seek to modulate the bubble dynamics through either active (e.g., surfactants, electricity) or passive means (e.g., surface wettability, microstructures), the utilization of an ordered microporous architecture to instigate desirable liquid and vapor flow field has been limited. Here, we investigate the bubble dynamics using various spatial patterns of inverse opal channels to induce preferential heat and mass flow site in highly-interconnected microporous media. A fully-coated inverse opal surface demonstrates the intrinsic boiling effects of a uniform microporous coating, which exhibits 156% enhancement in heat transfer coefficient in comparison to the polished silicon surface. The boiling heat transfer performances of spatially-variant inverse opal channels significantly differ based on the pitch spacings between the microporous channels, which dictate the bubble coalescent behaviors and bubble departure characteristics. The elucidated boiling heat transfer performances will provide engineering guidance toward designing optimal two-phase thermal management devices.
more »
« less
Experimental study of interactions between wakes and nucleate boiling in intermittent flow patterns
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
- Award ID(s):
- 1652578
- NSF-PAR ID:
- 10357326
- Date Published:
- Journal Name:
- 7th Thermal and Fluids Engineering Conference
- Page Range / eLocation ID:
- 687 to 690
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
The boiling efficacy is intrinsically tethered to trade-offs between the desire for bubble nucleation and necessity of vapor removal. The solution to these competing demands requires the separation of bubble activity and liquid delivery, often achieved through surface engineering. In this study, we independently engineer bubble nucleation and departure mechanisms through the design of heterogeneous and segmented nanowires with dual wettability with the aim of pushing the limit of structure-enhanced boiling heat transfer performances. The demonstration of separating liquid and vapor pathways outperforms state-of-the-art hierarchical nanowires, in particular, at low heat flux regimes while maintaining equal performances at high heat fluxes. A deep-learning based computer vision framework realized the autonomous curation and extraction of hidden big data along with digitalized bubbles. The combined efforts of materials design, deep learning techniques, and data-driven approach shed light on the mechanistic relationship between vapor/liquid pathways, bubble statistics, and phase change performance.more » « less
-
more » « less
Abstract Power intensification and miniaturization of electronics and energy systems are causing a critical challenge for thermal management. Single-phase heat transfer mechanisms including natural and forced convection of air and liquids cannot meet the ever-increasing demands. Two-phase heat transfer modes, such as evaporation, pool boiling, flow boiling, have much higher cooling capacities but are limited by a variety of practical instabilities, e.g., the critical heat flux (CHF), aka departure from nucleate boiling (DNB) in the nuclear industry, flow maldistribution, flow reversal, among others. These instabilities are often triggered suddenly during normal operation, and if not identified and mitigated in time, will lead to overheating issues and detrimental device failures. For example, when CHF is triggered during pool boiling, the device temperature can ramp up in the order of 150 °C/min. It is thus critical to implement real-time detection and mitigation algorithms for two-phase cooling. In the present work, we have developed an accurate and reliable technology for fault detection of high-performance two-phase cooling systems by coupling acoustic emission (AE) with multimodal fusion using deep learning. We have leveraged the contact AE sensor attached to the heater and hydrophones immersed in the working fluid to enable non-invasive fault detection.
-
more » « less
Abstract Boiling is a high-performance heat dissipation process that is central to electronics cooling and power generation. The past decades have witnessed significantly improved and better-controlled boiling heat transfer using structured surfaces, whereas the physical mechanisms that dominate structure-enhanced boiling remain contested. Experimental characterization of boiling has been challenging due to the high dimensionality, stochasticity, and dynamicity of the boiling process. To tackle these issues, this paper presents a coupled multimodal sensing and data fusion platform to characterize boiling states and heat fluxes and identify the key transport parameters in different boiling stages. Pool boiling tests of water on multi-tier copper structures are performed under both steady-state and transient heat loads, during which multimodal, multidimensional signals are recorded, including temperature profiles, optical imaging, and acoustic signals via contact acoustic emission (AE) sensors, hydrophones immersed in the liquid pool, and condenser microphones outside the boiling chamber. The physics-based analysis is focused on i) extracting dynamic characteristics of boiling from time lags between acoustic-optical-thermal signals, ii) analyzing energy balance between thermal diffusion, bubble growth, and acoustic dissipation, and iii) decoupling the response signals for different physical processes, e.g., low-to-midfrequency range AE induced by thermal expansion of liquids and bubble ebullition. Separate multimodal sensing tests, namely a single-phase liquid test and a single-bubble-dynamics test, are performed to reinforce the analysis, which confirms an AE peak of 1.5 kHz corresponding to bubble ebullition. The data-driven analysis is focused on enabling the early fusion of acoustic and optical signals for improved boiling state and flux predictions. Unlike single-modality analysis or commonly-used late fusion algorithms that concatenate processed signals in dense layers, the current work performs the fusion process in the deep feature domain using a multi-layer perceptron regression model. This early fusion algorithm is shown to lead to more accurate and robust predictions. The coupled multimodal sensing and data fusion platform is promising to enable reliable thermal monitoring and advance the understanding of dominant transport mechanisms during boiling.
-
more » « less
Abstract Bubble nucleation was investigated in a 20-mm-long, wickless heat pipe on the International Space Station. Over 20 h of running experiments using pentane as the working fluid, more than 100 nucleation events were observed. Bubble nucleation at the heater end temporarily boosted peak pressures and vapor temperatures in the device. At the moment of nucleation, the heater wall temperature significantly decreased due to increased evaporation and the original vapor bubble collapsed due to increased pressure. A thermal model was developed and using the measured temperatures and pressures, heat transfer coefficients near the heater end of the system were extracted. Peak heat transfer coefficients during the nucleation event were over a factor of three higher than at steady-state. The heat transfer coefficient data were all collapsed in the form of a single, linear correlation relating the Nusselt number to the Ohnesorge number.
- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1615/TFEC2022.mpp.040970
