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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]

Because of its potential benefits in lowering process energy consumption, freezing desalination as a seawater desalination method has attracted a lot of attention. However, significant mass loss caused by extensive washing is a fundamental problem preventing the development of freezing desalination technologies. For freezing desalination technology to be widely adopted, the washing process must be eliminated. In this study, a fluid-layer-based method for separating ice is introduced. The density differences between the generated ice and the silicon oil intermediate fluid layer facilitate the separation of the product water without the need of conventional crushing or washing processes. The aim of this work is to study the feasibility and applicability of using silicon oil as an intermediary fluid layer in ice separation during the freezing desalination process. The outcomes show promise for a feasible freezing desalination technique utilizing silicon oil as an intermediate separation layer. It was also observed that as feed salinity decreases, the desalination rate increases. For instance, the distillation rate is 50.89 % and 67.3 % at an initial feed salinity equal 45,000 and 10,000 ppm, respectively. Overall, the initial feed salinity and COP are found to have an impact on specific energy usage. • Intermediate fluid layer for ice separation • Silicon oil for ice production in freezing desalination • Specific energy consumption is affected by initial salinity and COP. • Specific energy consumption 57.412 kWh/m3 [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]

Freezing desalination has gained interest due to its potential to reduce energy use. The freezing process faces challenges that limit its wide use. Washing product ice causes a large mass loss. Most freezing processes involve crushing to generate the appropriate ice form, limiting large-scale production. A vacuum pump and pressing piston are experimentally utilized to collect brine during melting to avoid washing and crushing ice. Five brine separation processes are needed to produce 2 %-salinity water. The studies used salinity-varying feed water. From the results, utilizing a vacuum pump and piston for brine separation during melting and a relatively small number of separation steps has considerably enhanced the quality of the produced water without causing significant mass loss. It is observed that 1000 ml of 15,000 ppm feed water produces freshwater with salinity and volume equal to 114 ppm and 185 ml, respectively. Results indicate that when the COP varied from 4 to 6, the system performance ratio increased from 31.74 to 47.62 and 7.94 to 11.90, with a recovery ratio equal to 0.9 and 0.6, respectively. Compared to previously described freeze desalination, MSF, MED, and MD desalination systems, the specific energy consumption increases by 71.5 %, 66.4 %, 54.5 %, and 36.1 %. • Crushing and washing free, freezing desalination technology • A technique that involves brine extraction by vacuum • Experimental investigation at different feed salinities • System performance ratio ranged from 31.74 to 47.62. [ABSTRACT FROM AUTHOR]