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Supersonic impact of metallic microparticles onto metallic substrates generates extreme interfacial deformation and high contact pressures, enabling solid-state metallic bonding. Although higher impact velocities are generally believed to improve bond quality and mechanical properties in materials formed by supersonic impact deposition, here we report a peak in bond strength for single microparticle impact bonding, followed by a decline at higher impact velocities. Our in situ micromechanical measurements of interfacial strength for Al microparticles bonded to Al substrates reveal a three-fold increase from the critical bonding velocity (800 m/s) to a peak strength around 1,060 m/s. Interestingly, further increase in impact velocity results in a rapid decline in local interfacial strength. The decline continues up to the highest velocity studied, 1,337 m/s, which is well below the threshold required to induce melting or erosion. We show that a mechanistic transition from material strengthening to intensified elastic recovery is responsible for the peak strength in impact-induced bonding, with evidence linking the intensified elastic recovery to adiabatic softening at high impact velocities. Beyond 1,000 m/s for Al, interfacial damage induced by the intensified elastic recovery offsets the strength gain from higher impact velocities, resulting in a net decline in interfacial strength. This mechanistic understanding shall offer insights into the optimal design of processes that rely on impact bonding. 

Surface oxide layer fracture and the subsequent exposure of clean metallic surfaces are critical in various solid-state processes for powder consolidation and additive manufacturing. We resolve this process in-situ by deforming individual spherical powder particles inside a scanning electron microscope. We reveal three fracture modalities, i.e., meridian, radial, and circumferential cracking that sequentially activate with particle flattening. Real time measurements of load and displacement upon particle flattening also reveal a significant strengthening effect by surface oxide. We attribute the strengthening to two mechanisms: the composite strengthening and the strain gradient strengthening. 

The deformation behavior of particles plays a significant role in achieving adhesion during cold spray. The deformation behavior of the particles is associated with the fracture of the oxide layer and recrystallization, which are the key elements of the quality of cold spray. Studies of particle compression have been made to understand the deformation behavior of a particle. However, the deformation behavior of particle under controlled load and precise and high strain rate is yet to be studied. Here, we show the oxide layer fracture pattern and recrystallization regime under controlled load with a precise and high strain rate. We found that the cracks in the oxide layer initially appeared on the equator of the particle and propagated towards the edge of the top surface. Meanwhile, on the top surface, the circumferential crack was developed. On the other hand, the nanoindentation result showed that the compressed particle under a high strain rate has an unusual load-displacement behavior. Our results demonstrate that the oxide layer fracture behavior corresponds to the adhesion mechanism suggested by previous studies. Our study also revealed that recrystallization takes place within the particle under a high strain rate. We anticipate this finding to give a general insight into the deformation behavior of particles during cold spray. For instance, since the recrystallization behavior at a given strain rate can be predicted through this study, the resultant grain size and shape, which is associated with mechanical properties, can also be predicted. Furthermore, the amount of strain and strain rate to form optimal adhesion can be evaluated. 

Abstract Reliable subseasonal-to-seasonal (S2S) precipitation prediction is highly desired due to the great socioeconomical implications, yet it remains one of the most challenging topics in the weather/climate prediction research area. As part of the Impact of Initialized Land Temperature and Snowpack on Sub-seasonal to Seasonal Prediction (LS4P) project of the Global Energy and Water Exchanges (GEWEX) program, twenty-one climate models follow the LS4P protocol to quantify the impact of the Tibetan Plateau (TP) land surface temperature/subsurface temperature (LST/SUBT) springtime anomalies on the global summertime precipitation. We find that nudging towards reanalysis winds is crucial for climate models to generate atmosphere and land surface initial conditions close to observations, which is necessary for meaningful S2S applications. Simulations with nudged initial conditions can better capture the summer precipitation responses to the imposed TP LST/SUBT spring anomalies at hotspot regions all over the world. Further analyses show that the enhanced S2S prediction skill is largely attributable to the substantially improved initialization of the Tibetan Plateau-Rocky Mountain Circumglobal (TRC) wave train pattern in the atmosphere. This study highlights the important role that initial condition plays in the S2S prediction and suggests that data assimilation technique (e.g., nudging) should be adopted to initialize climate models to improve their S2S prediction. 

In regions of the world where topography varies significantly with distance, most global climate models (GCMs) have spatial resolutions that are too coarse to accurately simulate key meteorological variables that are influenced by topography, such as clouds, precipitation, and surface temperatures. One approach to tackle this challenge is to run climate models of sufficiently high resolution in those topographically complex regions such as the North American Regionally Refined Model (NARRM) subset of the Department of Energy’s (DOE) Energy Exascale Earth System Model version 2 (E3SM v2). Although high-resolution simulations are expected to provide unprecedented details of atmospheric processes, running models at such high resolutions remains computationally expensive compared to lower-resolution models such as the E3SM Low Resolution (LR). Moreover, because regionally refined and high-resolution GCMs are relatively new, there are a limited number of observational datasets and frameworks available for evaluating climate models with regionally varying spatial resolutions. As such, we developed a new framework to quantify the added value of high spatial resolution in simulating precipitation over the contiguous United States (CONUS). To determine its viability, we applied the framework to two model simulations and an observational dataset. We first remapped all the data into Hierarchical Equal-Area Iso-Latitude Pixelization (HEALPix) pixels. HEALPix offers several mathematical properties that enable seamless evaluation of climate models across different spatial resolutions including its equal-area and partitioning properties. The remapped HEALPix-based data are used to show how the spatial variability of both observed and simulated precipitation changes with resolution increases. This study provides valuable insights into the requirements for achieving accurate simulations of precipitation patterns over the CONUS. It highlights the importance of allocating sufficient computational resources to run climate models at higher temporal and spatial resolutions to capture spatial patterns effectively. Furthermore, the study demonstrates the effectiveness of the HEALPix framework in evaluating precipitation simulations across different spatial resolutions. This framework offers a viable approach for comparing observed and simulated data when dealing with datasets of varying spatial resolutions. By employing this framework, researchers can extend its usage to other climate variables, datasets, and disciplines that require comparing datasets with different spatial resolutions.