arXiv subjects

Hua Shu

Publications and source records attributed to Hua Shu.

3 recordsLinked to original sources

arXiv2022

Amorphous alloys surpass E/10 strength limit at extreme strain rates

Theoretical predictions of the ideal strength of materials range from E/30 to E/10 (E is Young's modulus). However, despite intense interest over the last decade, the value of the ideal strength that can be attained experimentally for metals remains a mystery (1-5). In this study, we demonstrated the unprecedented strength of an amorphous Cu-Zr alloy that surpassed the E/10 limit. Laser-induced shock experiments were conducted on Cu50Zr50 to explore its strength and failure mechanisms at ultrahigh strain rates. The material demonstrated a high spall strength of 9.8 GPa, approximately 1/13 of its P-wave modulus (~ E/6), at strain rates greater than 10^7 s^-1, which sets a new record for the elastic limit of metallic materials. Electron microscopy and large-scale molecular dynamics simulations revealed that void nucleation and growth, not shear-banding, comprised the major failure mechanism for metallic glasses at extremely fast strain rates. A new model for void formation under the control of surface energy explained the rate dependence of the material strength. The results of this study reveal new possible ways to use the amorphous phase in nanostructured metals in future applications under demanding mechanical conditions.

cond-mat.mtrl-sci↗

arXiv2022

Length L-function for Network-Constrained Point Data

Network constrained points are referred to as points restricted to road networks, such as taxi pick up and drop off locations. A significant pattern of network constrained points is referred to as an aggregation; e.g., the aggregation of pick up points may indicate a high taxi demand in a particular area. Although the network K function using the shortest path network distance has been proposed to detect point aggregation, its statistical unit is still radius based. R neighborhood, in particular, has inconsistent network length owing to the complex configuration of road networks which cause unfair counts and identification errors in networks (e.g., the length of the r neighborhood located at an intersection is longer than that on straight roads, which may include more points). In this study, we derived the length L function for network constrained points to identify the aggregation by designing a novel neighborhood as the statistical unit; the total length of this is consistent throughout the network. Compared to the network K function, our method can detect a true to life aggregation scale, identify the aggregation with higher network density, as well as identify the aggregations that the network K function cannot. We validated our method using taxi trips pick up location data within Zhongguancun Area in Beijing, analyzing differences in maximal aggregation between workdays and weekends to understand taxi demand in the morning and evening peak.

stat.OT↗

arXiv2021

Identifying Aggregation Artery Architecture of constrained Origin-Destination flows using Manhattan L-function

The movement of humans and goods in cities can be represented by constrained flow, which is defined as the movement of objects between origin and destination in road networks. Flow aggregation, namely origins and destinations aggregated simultaneously, is one of the most common patterns, say the aggregated origin-to-destination flows between two transport hubs may indicate the great traffic demand between two sites. Developing a clustering method for constrained flows is crucial for determining urban flow aggregation. Among existing methods about identifying flow aggregation, L-function of flows is the major one. Nevertheless, this method depends on the aggregation scale, the key parameter detected by Euclidean L-function, it does not adapt to road network. The extracted aggregation may be overestimated and dispersed. Therefore, we propose a clustering method based on L-function of Manhattan space, which consists of three major steps. The first is to detect aggregation scales by Manhattan L-function. The second is to determine core flows possessing highest local L-function values at different scales. The final step is to take the intersection of core flows neighbourhoods, the extent of which depends on corresponding scale. By setting the number of core flows, we could concentrate the aggregation and thus highlight Aggregation Artery Architecture (AAA), which depicts road sections that contain the projection of key flow cluster on the road networks. Experiment using taxi flows showed that AAA could clarify resident movement type of identified aggregated flows. Our method also helps selecting locations for distribution sites, thereby supporting accurate analysis of urban interactions.

cs.CG↗