Real-time thermal monitoring and regulation are critical to the mitigation of thermal runaways and device failures in two-phase cooling systems. Compared to conventional approaches that rely on the Joule effect, thermal gradient or transverse thermoelectric effect, acoustic emission (AE)-based remote sensing is more promising for robust and non-intrusive thermal monitoring. Nevertheless, due to the high stochasticity and noise of acoustic signals, existing implementations of AE in thermal systems have been limited to qualitative state monitoring. In this paper, we present a technology for real-time heat flux quantification during two-phase cooling by coupling acoustic sensing using hydrophones and condenser microphones and regression-based machine learning frameworks. These frameworks integrate a fast Fourier transform feature extraction algorithm with regressors, i.e., Gaussian process regressor and multilayer perceptron regressor for heat flux predictions. The acoustic signals and heat fluxes are collected from pool boiling tests under transient heat loads. It is shown that both hydrophone and condenser microphone signals are successful in predicting heat flux. Multiple models are trained and compared some using only one form of acoustic data while others combine both acoustic types (i.e., hydrophone and microphone) in fusion ML models (i.e., early, joint, late). The models using only hydrophone data are shown to perform better than the models using only microphone data. Also, some forms of fusion are shown to have better performance than either of the single input data type models. This AE-ML technology is demonstrated for accurate heatflux quantification. As such, this work will not only lead to a light, low-cost, and non-contact thermal measurement technology but also a new perspective for the physical explanation of bubble dynamics during boiling.
This content will become publicly available on September 26, 2024
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- Award ID(s):
- 2212002
- NSF-PAR ID:
- 10470891
- Publisher / Repository:
- American Society of Mechanical Engineers
- Date Published:
- Format(s):
- Medium: X
- Location:
- Washington, DC, USA
- Sponsoring Org:
- National Science Foundation
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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.
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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
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null (Ed.)This study addresses the combination of customized surface modification with the use of nanofluids, to infer on its potential to enhance pool-boiling heat transfer. Hydrophilic surfaces patterned with superhydrophobic regions were developed and used as surface interfaces with different nanofluids (water with gold, silver, aluminum and alumina nanoparticles), in order to evaluate the effect of the nature and concentration of the nanoparticles in bubble dynamics and consequently in heat transfer processes. The main qualitative and quantitative analysis was based on extensive post-processing of synchronized high-speed and thermographic images. To study the nucleation of a single bubble in pool boiling condition, a numerical model was also implemented. The results show an evident benefit of using biphilic patterns with well-established distances between the superhydrophobic regions. This can be observed in the resulting plot of the dissipated heat flux for a biphilic pattern with seven superhydrophobic spots, δ = 1/d and an imposed heat flux of 2132 w/m2. In this case, the dissipated heat flux is almost constant (except in the instant t* ≈ 0.9 when it reaches a peak of 2400 W/m2), whilst when using only a single superhydrophobic spot, where the heat flux dissipation reaches the maximum shortly after the detachment of the bubble, dropping continuously until a new necking phase starts. The biphilic patterns also allow a controlled bubble coalescence, which promotes fluid convection at the hydrophilic spacing between the superhydrophobic regions, which clearly contributes to cool down the surface. This effect is noticeable in the case of employing the Ag 1 wt% nanofluid, with an imposed heat flux of 2132 W/m2, where the coalescence of the drops promotes a surface cooling, identified by a temperature drop of 0.7 °C in the hydrophilic areas. Those areas have an average temperature of 101.8 °C, whilst the average temperature of the superhydrophobic spots at coalescence time is of 102.9 °C. For low concentrations as the ones used in this work, the effect of the nanofluids was observed to play a minor role. This can be observed on the slight discrepancy of the heat dissipation decay that occurred in the necking stage of the bubbles for nanofluids with the same kind of nanoparticles and different concentration. For the Au 0.1 wt% nanofluid, a heat dissipation decay of 350 W/m2 was reported, whilst for the Au 0.5 wt% nanofluid, the same decay was only of 280 W/m2. The results of the numerical model concerning velocity fields indicated a sudden acceleration at the bubble detachment, as can be qualitatively analyzed in the thermographic images obtained in this work. Additionally, the temperature fields of the analyzed region present the same tendency as the experimental results.more » « less
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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
