The rapid growth and scaling of electronics are causing more severe thermal management challenges. For example, the high-performance computing processors are driving the data center power density to unprecedented levels, approaching the limit of conventional air cooling. In electric vehicles (EVs) and hybrid EVs, the power conversion electronics are integrated into a compact space, leading to ultra-high heat fluxes to dissipate. Among the available thermal management mechanisms, two-phase cooling that involves the phase-change process of the working fluid can maintain electronic devices at safe operating temperatures by taking advantage of the high latent heat of the fluid. Particularly, pool boiling plays a critical role in the two-phase immersion cooling of servers and other IT hardware, integrated cooling for three-dimensional electronic packaging, cooling of the core, and used fuel in nuclear reactors. Two-phase coolers are limited by instabilities such as the critical heat flux (CHF). At the critical heat flux, the temperature increases. It is important to be able to identify the CHF in order to prevent overheating. We aim to develop and compare boiling image classification models to distinguish between 2 boiling regimes. We will leverage principal component analysis (PCA) and K-means clustering to investigate the key differences between bubbles during nucleate boiling (pre-CHF) and transition boiling (post-CHF). We will also compare the results of the unsupervised learning model against popular supervised learning models that have been used for boiling regime classification in existing studies, such as convolutional neural networks, multiplayer perceptrons, and transformers. We successfully created 4 supervised and 1 unsupervised learning models to distinguish between the two types of boiling images.
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- Award ID(s):
- 1946391
- PAR ID:
- 10497395
- Publisher / Repository:
- American Society of Mechanical Engineers
- Date Published:
- Journal Name:
- ASME 2022 Heat Transfer Summer Conference
- ISBN:
- 978-0-7918-8579-6
- Format(s):
- Medium: X
- Location:
- Philadelphia, Pennsylvania, USA
- Sponsoring Org:
- National Science Foundation
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Abstract Development of smaller, faster, and more powerful electronic devices requires effective cooling strategies to efficiently remove ever‐greater heat. Phase‐change heat transfer such as boiling and evaporation has been widely exploited in various water‐energy industries owing to its efficient heat transfer mode. Despite extensive progress, it remains challenging to achieve the physical limit of flow boiling due to highly transitional and chaotic nature of multiphase flows as well as unfavorable boundary layer structures. Herein, a new strategy that promises to approach the physical limit of flow boiling heat transfer is reported. The flow boiling device with multiple channels is characterized with the design of micropinfin fences, which fundamentally transforms the boundary layer structures and imparts significantly higher heat transfer coefficient even at high heat flux conditions, in which boiling heat transfer is usually deteriorated due to the development of dryout starting from outlet regions and severe two‐phase flow instabilities. Moreover, the approaching of physical limit is achieved without elevating pressure drop.
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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.
