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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.

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.

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.