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This paper mainly studied the thermocapillary migration of deformable droplets induced by periodic temperature boundary under microgravity conditions. The finite-difference/front-tracking (FD/FT) method was used to solve the Navier-Stokes equation coupled with the energy equation, and the continuum surface force (CSF) model was used to simplify the surface tension of the phase interface. The results showed that the maximum droplet migration velocity increased with the increase of temperature amplitude, and the droplet cycle period became shorter with the increase of temperature angular frequency. In the 1/4 cycle, the initial movement time of droplet decreased with the increase of temperature phase. If the phase was reversed, the initial movement direction of the droplet changed. With the increase of Reynolds number (Re), the droplet tended to maintain its motion inertia. Key words: periodic thermal boundary, front tracking method, Marangoni, microgravity. Nous etudions surtout la migration thermo-capillaire des gouttelettes deformables, induite par des frontieres de temperature periodiques dans des conditions de microgravite. Nous utilisons la methode de suivi d'interface (front tracking) aux differences finies (SI-DF/FD-FT) pour solutionner l'equation de Navier-Stokes couplee avec l'equation d'energie, alors que le modele de la force de surface dans le continuum (FSC/CSF) est utilise pour simplifier la tension de surface de l'interface de phase. Les resultats montrent que la vitesse maximale de migration des gouttelettes augmente avec la temperature. Aussi, la periode du cycle des gouttelettes devient plus courte avec l'augmentation de la frequence angulaire de la temperature. Dans le quart (1/4) de cycle, le temps de mouvement initial de la gouttelette diminue avec l'augmentation de la phase de la temperature. Si la phase est inversee, la direction du mouvement initial de la gouttelette change. Avec l'augmentation du nombre de Reynolds, la gouttelette tend a maintenir son mouvement inertiel. Mots-cles : frontiere thermique periodique, methode de suivi d'interface, Marangoni, microgravite., 1. Introduction In the study of heat transfer in multiphase flow, the phenomenon of fluid movement, which is caused by a temperature gradient or composition change on the liquid surface, [...]

Global surface snowfall rate estimation is crucial for hydrological and meteorological applications but is still a challenging task. A novel approach is developed to comprehensively use passive microwave, infrared data and physical constraints in deep‐learning neural networks with an attention module for retrieving surface snowfall rate (PCSSR‐DNNWA). The PCSSR‐DNNWA consistently outperforms traditional approaches in predicting surface snowfall rates with a correlation coefficient of ∼0.76, mean error of ∼−0.02 mm/hr, and root mean squared error of ∼0.21 mm/hr. It is found that graupel water path is of vital importance with largest contributions in retrieving surface snowfall rate. By integrating the physical constraints, the algorithm of PCSSR‐DNNWA opens a new avenue for retrieving the surface snowfall rate from satellites since some predictors are intelligently considered, resulting in an increased accuracy, interpretability, and computational efficiency. Plain Language Summary: Comprehensively monitoring surface snowfall on Earth can effectively be achieved through space‐borne instruments. However, estimating surface snowfall from space is a challenging task as the signals measured by space sensors are indirectly related to surface snowfall rate. In this study, a novel deep learning algorithm is developed based on deep neural networks, which is more accurate, interpretable and computationally efficient, compared with traditional approaches, in estimating surface snowfall rate using observations from various space‐borne sensors and physically relevant parameters. Key Points: Physical constraints greatly improve the ability of surface snowfall rate retrievalAttention module in deep neural networks could intelligently adjust the weights of predictors [ABSTRACT FROM AUTHOR]

Precipitation in different phases has varying effects on runoff. However, monitoring surface snowfall poses a significant challenge, highlighting the importance of developing a snowfall detection algorithm. The objective of this study is develop a snowfall detection algorithm for the Microwave Temperature Sounder-2 (MWTS-II) and the Microwave Humidity Sounder-2 (MWHS-II) onboard the FY-3D satellite while considering the differentiated atmosphere temperature conditions. The results show that: (1) The brightness temperature (TB) of MWTS Channel 3 is well-suited for pre-classifying atmospheric temperatures, and significant differences in TB distribution exist between the two pre-classification subsets. (2) Among six machine classifiers examined, the random forest classifier exhibits favorable classification performance on both the validation set (accuracy: 0.76, recall: 0.76, F1 score: 0.75) and test set (accuracy: 0.80, recall: 0.44, F1 score: 0.44). (3) The application of the snowfall detection algorithm showcases a reasonable spatial distribution and outperforms the IMERG and ERA5 snowfall data. [ABSTRACT FROM AUTHOR]

In order to address the issues of low initial Coulombic efficiency of SiOx-C composite anode due to the formation of solid electrolyte interphase, irreversible conversion reaction, and large volume change, the prelithiation method using metal lithium has been employed as one of effective solutions. However, violent side reactions with liquid electrolyte for prelithiation lead to low prelithiation efficiency and induce poor interface between the SiOx-C electrode and liquid electrolyte. Here, a new prelithiation method with so called solid-state corrosion of lithium is developed. By replacing liquid electrolyte with solid-state electrolyte of carbon-incorporated lithium phosphorus oxynitride (LiCPON), not only various side reactions associated with metal lithium are avoided, but also the perfect interface is achieved from the decomposition products of LiCPON. The successful implementation of solid-state corrosion prelithiation can be confirmed by changes in optical image, scanning electron microscopy, and X-ray diffraction. Compared with pristine electrode, the initial Coulombic efficiency of the prelithiated electrode using solid electrolyte can be increased by about 10%, reaching 98.6% in half cell and 88.9% in full cell. Compared with prelithiated electrode using liquid electrolyte, the prelithiation efficiency of the prelithiated anode with solid-state corrosion can be increased from 25.7% to 82.8%. Solid-state corrosion of lithium is expected to become a useful method for prelithiation of SiOx-C composite electrode with high initial Coulombic efficiency and large prelithiation efficiency. [ABSTRACT FROM AUTHOR]

Accurate knowledge on spatiotemporal characteristics of historical precipitation extremes could provide great potential guidance for preventing hydrological‐related disasters caused by precipitation extremes in the future. On the basis of the fifth generation of atmospheric reanalysis precipitation data by the European Centre for Medium Range Weather Forecasts (ERA5, 0.25°, 1 hourly, 1950–2020) with high spatiotemporal resolutions, continuity and quality, this study analyzed the spatiotemporal characteristics of precipitation extremes over the Ganjiang River Basin and its surroundings during 1950–2020. The main conclusions include, but are not limited to, the following: (1) In general, precipitation extremes present increasing trends over most areas of the basin and its surroundings. For instance, areas showing upward trends of R10, SDII and PRCPTOT account for ~93.45%, ~66.36%, and ~88.18%, respectively. (2) The spatiotemporal variations of precipitation extremes over the Ganjiang River Basin and its surroundings show obvious northwest–southeast differences. For instance, precipitation extremes are increasing in the southeastern parts, but they are decreasing in the northwestern parts. (3) High‐value clusters are also identified in the southeast (e.g., R10, SDII, R95P and PRCPTOT, accounting for ~20.71%, ~20.72%, ~25.88%, and ~22.56%, respectively) and low‐value clusters in the northwest (e.g., Rx5day, SDII and R95P, accounting for ~18.05%, ~27.03%, and ~21.18%, respectively). (4) The spatiotemporal variations of precipitation extremes in both the southeast and northwest are quite stable. For example, regions with less than five abrupt change points of R10, SDII, and PRCPTOT account for 77.49%, 54.84%, and 81.74%, respectively. [ABSTRACT FROM AUTHOR]