An experimental investigation of subcooled pool boiling on downward-facing surfaces with microchannels

Gao, Zhijie; Hong, Sihui; Dang, Chaobin

doi:10.1016/j.applthermaleng.2023.120283

Cited by 25 publications

(2 citation statements)

References 34 publications

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“…Apart from that, steam generator tube rupture (SGTR) leads to the inevitable release of radiation into the environment [2]. For the safety and reliable operation of the plant, it is necessary to go for online monitoring and periodic inspection [3,4]. Non-destructive testing (NDT) is a mode of testing that has been adopted by industries for more than a decade to test mass-manufactured products for anomalies.…”

Section: Introductionmentioning

confidence: 99%

DPGWO Based Feature Selection Machine Learning Model for Prediction of Crack Dimensions in Steam Generator Tubes

et al. 2023

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The selection of an appropriate number of features and their combinations will play a major role in improving the learning accuracy, computation cost, and understanding of machine learning models. In this present work, 22 gray-level co-occurrence matrix features extracted from magnetic flux leakage images captured in steam generator tubes’ cracks are considered for developing a machine learning model to predict and analyze crack dimensions in terms of their length, depth, and width. The performance of the models is examined by considering R2 and RMSE values calculated using both training and testing data sets. The F Score and Mutual Information Score methods have been applied to prioritize the features. To analyze the effect of different machine learning models, their number of features, and their selection methods, a Taguchi experimental design has been implemented and an analysis of variance test has been conducted. The dynamic population gray wolf algorithm (DPGWO) has been adopted to select the best features and their combinations. Due to the two contradictory natures of performance metrics, Pareto optimal solutions are considered, and the best one is obtained using Deng’s method. The effectiveness of DPGWO is proved by comparing its performance with Grey Wolf Optimization and Moth Flame Optimization algorithms using the Friedman test and performance indicators, namely inverted generational distance and spacing.

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Section: Introductionmentioning

confidence: 99%

DPGWO Based Feature Selection Machine Learning Model for Prediction of Crack Dimensions in Steam Generator Tubes

et al. 2023

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“…Understanding microfluidic flow patterns is important for designing and optimizing microfluidic devices for specific applications. By controlling the flow pattern, researchers can manipulate the behavior of fluids in microscale channels and develop devices that can perform precise chemical reactions, separations, and detections [22][23][24] .…”

mentioning

confidence: 99%

Computational fluid dynamics simulation of two-phase flow patterns in a serpentine microfluidic device

Amini

Ghazanfari

Heydari

et al. 2023

Sci Rep

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In the current research work, the flow behavior of a liquid–liquid extraction (LLE) process in a serpentine microchannel was analyzed. The simulation was performed using a 3D model and the results were found to be consistent with experimental data. The impact of the flow of chloroform and water on the flow model was also examined. The data indicate that once the aqua and organic phases flow rates are low and similar, a slug flow pattern is observed. However, as the overall flow rate raises, the slug flow transforms into parallel plug flow or droplet flow. An increment in the aqua flows while maintaining a constant organic phase flow rate results in a transition from slug flow to either droplet flow or plug flow. Finally, the patterns of flow rate in the serpentine micro-channel were characterized and depicted. The results of this study will provide valuable insights into the behavior of two-phase flow patterns in serpentine microfluidic devices. This information can be used to optimize the design of microfluidic devices for various applications. Furthermore, the study will demonstrate the applicability of CFD simulation in investigating the behavior of fluids in microfluidic devices, which can be a cost-effective and efficient alternative to experimental studies.

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Flexible hexagonal boron nitride@liquid metal/polydimethylsiloxane composites with excellent thermal conductivity and superior mechanical properties

Chen,

Yao,

Niu

et al. 2024

Polymer Composites

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Hexagonal boron nitride (h‐BN) has a layered lattice structure, high intrinsic thermal conductivity, and good chemical stability, and it has a promising applications in thermal management materials. However, poor interfacial compatibility and dispersion of BN flakes in the polymer matrix cause thermal contact resistance and gaps, directly impairing the performance of the composites and limiting their thermal conduction applications. Herein, the P‐BN@LM/PDMS thermally conductive composites with superior mechanical properties were fabricated by incorporating dopamine and liquid metal (LM) into BN flakes. The dopamine self‐polymerizes to form polydopamine (PDA) on the BN surface, denoted as P‐BN, which effectively improves interface compatibility and dispersion. The LM attaches to the functionalized P‐BN surface by mechanical grinding, acting as a bridge between adjacent BN flakes to enhance the integrity of the thermal conductive network within the composites. The coordination between P‐BN and LM increases interface strength, effectively improving mechanical performance. Hence, the P‐BN@LM/PDMS composites exhibit excellent thermal conductivity (4.0 W/mK) and an enhancement factor of 1373%, which is 2.22 times that of BN/PDMS composites with the same 30 wt% loading. Additionally, the composite shows superior tensile strength (2.11 MPa) and elongation at break (121%), representing 441% and 203% improvements compared with pure polydimethylsiloxane (PDMS). The P‐BN@LM/PDMS composites, with significant advantages in thermal conductivity and mechanical properties, are promising for future applications as flexible thermal interface materials in thermal management.Highlights The P‐BN@LM/PDMS composites exhibit excellent thermal conductivity (4.0 W/mK) and an enhancement factor of 1373%, making them promising for future applications as flexible thermal interface materials in thermal management. The P‐BN@LM/PDMS composites present a superior tensile strength (2.11 MPa) and elongation at break (121%). PDA formed by the self‐polymerization of dopamine on the BN surface effectively enhances interfacial compatibility and dispersion. LM attaches to the functionalized BN surface through mechanical grinding, acting as a bridge between adjacent BN flakes, thus enhancing the integrity of the thermal conductive network within the composites.

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An experimental investigation of subcooled pool boiling on downward-facing surfaces with microchannels

Cited by 25 publications

References 34 publications

DPGWO Based Feature Selection Machine Learning Model for Prediction of Crack Dimensions in Steam Generator Tubes

DPGWO Based Feature Selection Machine Learning Model for Prediction of Crack Dimensions in Steam Generator Tubes

Computational fluid dynamics simulation of two-phase flow patterns in a serpentine microfluidic device

Flexible hexagonal boron nitride@liquid metal/polydimethylsiloxane composites with excellent thermal conductivity and superior mechanical properties

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