Pearl River Hydraulic Research Institute

ABSTRACT The poor tribological performance of Ti6Al4V titanium alloy severely limits its application in moving components. Although hydrogenated diamond‐like carbon (a‐C:H) coatings can effectively improve their wear resistance, the mechanism underlying the load‐driven evolution of the friction interface remains unclear. This study systematically investigates the tribological behavior and interfacial dynamic evolution of a‐C:H coatings under different loads via multiscale characterization. Results show that the a‐C:H coatings significantly reduced the coefficient of friction (COF) and wear rate by 61.08% and four orders of magnitude, respectively. With increasing load, the COF decreased monotonically, while the wear rate first decreased and then increased, reaching an optimum at 10 N. Raman spectroscopy and transmission electron microscopy (TEM) analyses revealed that the load drove the evolution from a state dominated by iron oxides to a mixed phase and finally to the formation of continuous carbon transfer films. Critically, higher loads accelerate the formation rates of the carbon transfer film while promoting its graphitization, enabling rapid surface coverage that suppresses oxidative wear. This kinetic control facilitates the transition in the friction mechanism from oxidative wear to efficient shear lubrication by the carbon transfer film. This work establishes that the kinetic regulation of the interfacial pathway is the central mechanism through which load optimizes tribological performance, providing a foundational principle for the load‐adaptive design of surface coatings on titanium alloys.

Precipitation is a key input for hydrological modeling, and high-resolution, accurate data are essential for flood forecasting and water resource management. This study presents a Hybrid Downscaling and Multi-source Precipitation Fusion (HDMPF) framework to improve the spatial resolution and accuracy of precipitation estimates and enhance simulations of extreme precipitation and hydrological responses. HDMPF combines a Radial Basis Function network and Random Forest for downscaling, and applies Bayesian Model Averaging to fuse multiple satellite precipitation products. The fused dataset was used to drive the Grid-Xin’anjiang model for extreme flood simulations. The results show that HDMPF significantly improves spatiotemporal precipitation accuracy, increasing the KGE to 0.90–0.95 and reducing the RMSE to below 0.3 mm/h. The framework accurately reproduces precipitation cores, peak intensities, flood peaks, timing, and multi-peak hydrographs, demonstrating strong potential for improving basin-scale modeling and flood early warning.

This study developed a zero-value detection device for live porcelain insulators based on high-voltage impulse method. The device generates peak impulse voltages of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{6 0 k V}$</tex> and identifies insulator status by comparing peak induced voltages between normal and zero-value conditions through an induction ring placed in the detection circuit. Experimental results show that under uncharged conditions, the induced voltage peaks for normal insulators are approximately 1150 mV, while those for zero-value insulators reach 1800 mV, demonstrating significant distinction. Further live detection tests were conducted on an insulator string composed of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{1 6}$</tex> normal insulators under power frequency voltages of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$128 \text{kV}, 190 \text{kV}, 220 \text{kV}$</tex>, and 289 kV. The grounding terminal reliably identified insulator status at all voltage levels. However, at high-voltage terminals, accurate discrimination remained achievable under 128 kV and 190 kV conditions, but signal interference severely increased at 220 kV and 289 kV due to enhanced electromagnetic interference, leading to reduced detection reliability. This research validates the effectiveness of the detection method under low and medium power frequency voltages while revealing insufficient electromagnetic interference resistance in high-voltage environments. The findings provide clear direction for optimizing detection systems and improving reliability in strong electric field environments.