Biotextured Nanocrystalline Materials with Superhydrophobic Surfaces and Controlled Friction and Wear

This study aimed to develop new wear-resistant materials with superhydrophobic surfaces and low friction by combining the high strength of nanocrystalline (NC) materials with the biological surface textures. This goal was accomplished in three stages. First, the tribological properties of electrodeposited NC Ni and NC Co were studied, and the role of the oxide-rich tribolayers in reducing the friction and wear rates was delineated. For the NC Ni, micromechanisms of wear in different testing environments were characterized, and the sliding-speed sensitivity of friction and wear rate was investigated. A modified Archard equation was proposed to predict the wear rates of NC Ni as a function of grain size and sliding speed. Additionally, it was found that the high-temperature wear resistance of NC Ni could be improved by using SiC nanoparticles as reinforcements. In the second stage, NC Ni replicas of the surface textures of a lotus leaf and a snake skin were fabricated through replication and electrodeposition. The NC Ni snake skin replica displayed anisotropic frictional properties, due to the asymmetric shape of the protrusions at the scales\u27 ridges. The NC Ni lotus leaf replica featured a high density of microscale conical protuberances that prompted a 30% lower peak coefficient of friction (COF) compared to a smooth surface, due to a smaller real area of contact. In the third stage, the surface texture of the NC Ni lotus leaf replica was modified using a short-duration electrodeposition process that increased the radius of the protuberance tips, followed by a perfluoropolyether (PFPE) solution treatment that reduced the surface energy and resulted in a multi-level surface roughness consisting of a nanoscale surface texture superimposed on microscale protuberances. The produced surfaces had a high water contact angle of 156░, similar to that of the natural lotus leaf, and had a 60% lower steady-state COF

Solar Enablement Initiative in Australia: Report on Efficiently Identifying Critical Cases for Evaluating the Voltage Impact of Large PV Investment

The increasing quantity of PV generation connected to distribution networks is creating challenges in maintaining and controlling voltages in those distribution networks. Determining the maximum hosting capacity for new PV installations based on the historical data is an essential task for distribution networks. Analyzing all historical data in large distribution networks is impractical. Therefore, this paper focuses on how to time efficiently identify the critical cases for evaluating the voltage impacts of the new large PV applications in medium voltage (MV) distribution networks. A systematic approach is proposed to cluster medium voltage nodes based on electrical adjacency and time blocks. MV nodes are clustered along with the voltage magnitudes and time blocks. Critical cases of each cluster can be used for further power flow study. This method is scalable and can time efficiently identify cases for evaluating PV investment on medium voltage networks

Systems and Methods for Enhanced Resistance Spot Welding with Textured Sheet Metal

Systems and methods for joining a first metal sheet with a second metal sheet are provided. The method includes applying a texture to a portion of a surface of the first metal sheet and positioning the first metal sheet relative to the second metal sheet such that the portion of the surface of the first metal sheet including the texture faces the second metal sheet. The method includes joining the first metal sheet to the second metal sheet through a joining technique at an interface of the portion of the surface of the first metal sheet comprising the texture with the second metal sheet

Single Iteration Conditional Based DSE Considering Spatial and Temporal Correlation

The increasing complexity of distribution network calls for advancement in distribution system state estimation (DSSE) to monitor the operating conditions more accurately. Sufficient number of measurements is imperative for a reliable and accurate state estimation. The limitation on the measurement devices is generally tackled with using the so-called pseudo measured data. However, the errors in pseudo data by cur-rent techniques are quite high leading to a poor DSSE. As customer loads in distribution networks show high cross-correlation in various locations and over successive time steps, it is plausible that deploying the spatial-temporal dependencies can improve the pseudo data accuracy and estimation. Although, the role of spatial dependency in DSSE has been addressed in the literature, one can hardly find an efficient DSSE framework capable of incorporating temporal dependencies present in customer loads. Consequently, to obtain a more efficient and accurate state estimation, we propose a new non-iterative DSSE framework to involve spatial-temporal dependencies together. The spatial-temporal dependencies are modeled by conditional multivariate complex Gaussian distributions and are studied for both static and real-time state estimations, where information at preceding time steps are employed to increase the accuracy of DSSE. The efficiency of the proposed approach is verified based on quality and accuracy indices, standard deviation and computational time. Two balanced medium voltage (MV) and one unbalanced low voltage (LV) distribution case studies are used for evaluations