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• Bubble dynamics in one single confined microchannel was experimentally investigated. • The effects of hydraulic diameter (D h) of channel, flow rate and heat flux on bubble dynamics are discussed. • The velocity and acceleration of L- V interface were estimated through the analysis of recorded videos using vision-based technique. • The interfacial heat transfer (Nu c) changes with re b during bubble growth and bubble shrinking. • An analysis of energy distributions by bubble dynamics is conducted, heat flux through interface counts over 96% of total input power. Bubble dynamics are fundamental to understand boiling heat transfer mechanism at microscale. In this study, bubble dynamics in one single confined microchannel was experimentally investigated through vision-assisted technique. Experimental studies were performed to understand the effects of hydraulic diameter (D h) of channel, flow rate and heat flux on bubble dynamics, which highly affect the boiling heat transfer performance. First of all, experimental results show that D h has a vital effect on the volume change rate of confined bubble, which can be used to compute the heat flux transported by single bubble. The liquid-vapor (L- V) interfaces were tracked by non-rigid image registration and the velocity and acceleration of L- V interface were estimated through the analysis of recorded videos using vision-based technique. To quantitatively evaluate the impact of bubble dynamics, transient heat transfer rates were presented at different flow rates. Furthermore, the interfacial heat transfer (Nu c) influenced by bubble dynamics was evaluated. Nu c changes with Re b during bubble growth and bubble shrinking. Finally, an analysis of energy distributions by bubble dynamics is conducted. Compared to energy transported by bubble and kinetic energy of bubble, heat flux through interface is significant, dissipating over 96% of total input power. [ABSTRACT FROM AUTHOR]

Identifying Photovoltaic (PV) array faults is crucial for improving the service life and consolidating system performance overall. The strategies based on the supervised Machine Learning (ML) approach represent an attractive solution to identify the PV array faults. However, attainable labeled data to train supervised ML algorithms present challenges in practice. Therefore, this work introduces a novel strategy that employs an ensemble learning concept in conjunction with a semi-supervised learning approach based on a self-training philosophy to realize the faults diagnosis of an arc, line-to-line, power tracker unit, open-circuit, and partial shading, under different of aspects which can directly be impacting faults behavior. The developed ensemble learning paradigm comprises multiple merged ML models, which enhances the overall diagnostics performance. Moreover, it works to alleviate the resource-intensive process, which, in turn, contributes to overcoming standard supervised ML algorithms limitations. To ensure high fault diagnostic capabilities through the proposed fault identification strategy, the principal component analysis is introduced to mitigate the correlation between variables. Moreover, the Bayesian optimization method is adopted to control the behaviors of training ML algorithms, providing models with better characterization results. The merits of the proposed strategy are corroborated through simulation and experimental case studies. [Display omitted] • A novel strategy for effective identification of faults in PV arrays. • The strategy takes into consideration of the minimum number of sensors. • The strategy works to alleviate the resource-intensive process. • The strategy can identify the faults under harsh environmental scenarios. • Strategy effectiveness has been validated by simulation and experimental results. [ABSTRACT FROM AUTHOR]