Disruptive packing of binary mixtures

2016 Summer.Includes bibliographical references.Granular materials are common in many areas such as civil engineering, food industry,and chemistry. The discrete element method has been demonstrated to be an eectivemethod to study the particle dynamics of such materials over the past several decades. Thepacking of monosized spherical particles has been well studied from both numerical andexperimental perspectives. However, the study of packings of a binary mixture that containsparticles of two dierent sizes has been limited because of the numerous variables that aectthe packing structure.The potential variables for packing of binary mixtures of spherical particles blended bygeometric disruptors in a gravity loaded ramp are evaluated in this thesis. The complexity ofthe disruptor geometry was used as the primary variable to study the resulting packing of twodierent-sized particles. The nal packing structure was quantied by coordination number,radial distribution function, packing density, and vertical position of the smaller-diameterparticles. Based on the analysis conducted in this thesis, the mean coordination number ofall particles, larger particles and smaller particles, generally increases with the complexity ofdisruptor geometry. The mean vertical position of smaller particles decrease with an increasein the complexity of the disruptor geometry. The radial distribution function of each type ofparticle in a binary mixture has the same characteristics of the radial distribution functionof mono-size particle packing. The methodology presented in this thesis can be eective toanalyze binary mixtures of spherical particles

Increasing stability of the first order linearized inverse Schr\"{o}dinger potential problem with integer power type nonlinearities

We investigate the increasing stability of the inverse Schr\"{o}dinger potential problem with integer power type nonlinearities at a large wavenumber. By considering the first order linearized system with respect to the unknown potential function, a combination formula of the first order linearization is proposed, which provides a Lipschitz type stability for the recovery of the Fourier coefficients of the unknown potential function in low frequency mode. These stability results highlight the advantage of nonlinearity in solving this inverse potential problem by explicitly quantifying the dependence to the wavenumber and the nonlinearities index. A reconstruction algorithm for general power type nonlinearities is also provided. Several numerical examples illuminate the efficiency of our proposed algorithm.Comment: 37 pages, 8 figure

Maintenance Management Research of a Large-span Continuous Rigid Frame Bridge Based on Reliability Assessment by Using Strain Monitored Data

When the bridge components needing maintenance are the world problem at present, and the health monitoring system is considered to be a very helpful tool for solving this problem. In this paper, a large number of strain data acquired from the structural health monitoring system (SHMS) installed on a continuous rigid frame bridge are adopted to do reliability assessment. Firstly, a calculation method of punctiform time-dependent reliability is proposed based on the basic reliability theory, and introduced how to calculate reliability of the bridge by using the stress data transformed from the strain data. Secondly, combined with “Three Sigma” principle and the basic pressure safety reserve requirement, the critical load effects distribution function of the bridge is defined, and then the maintenance reliability threshold for controlling the unfavorable load state which appears in the early operation stage of this type bridge is suggested, and then the combination of bridge maintenance management and health monitoring system is realized. Finally, the transformed stress distribution certifies that the load effects of concrete bridges practically have a normal distribution; as for the concrete continuous rigid frame bridge with C50 strength grade concrete, the retrofit reliability threshold should be valued at 6.13. The methodology suggested in this article can help bridge engineers do effective maintenance of bridges, which can effectively extend the service life of the bridge and bring better economic and social benefits

Revealing the mystery of the double charm tetraquark in pppp collision

A novel approach is proposed to probe the nature of the double charm tetraquark through the measurement of production asymmetry between $T_{\bar{c}\bar{c}}^-$ and $T_{cc}^+$ in $pp$ collisions. When comparing two theoretical pictures, the compact tetraquark and molecular pictures, we find that the compact tetraquark picture exhibits a significantly larger production asymmetry and an order of magnitude lower total cross section compared to the molecular picture, enabling the unambiguous determination of the tetraquark's internal structure. Additionally, distinctive differences in the transverse momentum ($p_\mathrm{T}$) and rapidity ($y$) distributions of $T_{\bar{c}\bar{c}}^-$ and $T_{cc}^+$ cross sections emerge, particularly at $p_\mathrm{T}\approx 2~\mathrm{GeV}$ and $y\approx \pm 6$. The difference between particles and antiparticles in a large rapidity region is attributed to the behavior of the residual $ud$ diquarks within the proton. Our work extends to the exploration of other double heavy tetraquark candidates, offering a versatile approach to advance our understanding of exotic hadronic states in particle physics.Comment: 6 pages, 3 figure

Unsupervised 3D Learning for Shape Analysis via Multiresolution Instance Discrimination

Although unsupervised feature learning has demonstrated its advantages to reducing the workload of data labeling and network design in many fields, existing unsupervised 3D learning methods still cannot offer a generic network for various shape analysis tasks with competitive performance to supervised methods. In this paper, we propose an unsupervised method for learning a generic and efficient shape encoding network for different shape analysis tasks. The key idea of our method is to jointly encode and learn shape and point features from unlabeled 3D point clouds. For this purpose, we adapt HR-Net to octree-based convolutional neural networks for jointly encoding shape and point features with fused multiresolution subnetworks and design a simple-yet-efficient Multiresolution Instance Discrimination (MID) loss for jointly learning the shape and point features. Our network takes a 3D point cloud as input and output both shape and point features. After training, the network is concatenated with simple task-specific back-end layers and fine-tuned for different shape analysis tasks. We evaluate the efficacy and generality of our method and validate our network and loss design with a set of shape analysis tasks, including shape classification, semantic shape segmentation, as well as shape registration tasks. With simple back-ends, our network demonstrates the best performance among all unsupervised methods and achieves competitive performance to supervised methods, especially in tasks with a small labeled dataset. For fine-grained shape segmentation, our method even surpasses existing supervised methods by a large margin.Comment: Accepted by AAAI 2021. Code: https://github.com/microsoft/O-CNN/blob/master/docs/unsupervised.m