Numerical Algorithms for Simulation of a Fluid-Filed Fracture Evolution in a Poroelastic Medium

The paper deals with the computational framework for the numerical simulation of the three dimensional fluid-filled fracture evolution in a poroelastic medium. The model consists of several groups of equations including the Biot poroelastic model to describe a bulk medium behavior, Reynold’s lubrication equations to describe a flow inside fracture and corresponding bulk/fracture interface conditions. The geometric model of the fracture assumes that it is described as an arbitrary sufficiently smooth surface with a boundary. Main attention is paid to describing numerical algorithms for particular problems (poroelasticity, fracture fluid flow, fracture evolution) as well as an algorithm for the coupled problem solution. An implicit fracture mid-surface representation approach based on the closest point projection operator is a particular feature of the proposed algorithms. Such a representation is used to describe the fracture mid-surface in the poroelastic solver, Reynold’s lubrication equation solver and for simulation of fracture evolutions. The poroelastic solver is based on a special variant of X-FEM algorithms, which uses the closest point representation of the fracture. To solve Reynold’s lubrication equations, which model the fluid flow in fracture, a finite element version of the closet point projection method for PDEs surface is used. As a result, the algorithm for the coupled problem is purely Eulerian and uses the same finite element mesh to solve equations defined in the bulk and on the fracture mid-surface. Finally, we present results of the numerical simulations which demonstrate possibilities of the proposed numerical techniques, in particular, a problem in a media with a heterogeneous distribution of transport, elastic and toughness properties.

Point Projection Network: A Multi-View-Based Point Completion Network with Encoder-Decoder Architecture

Recently, unstructured 3D point clouds have been widely used in remote sensing application. However, inevitable is the appearance of an incomplete point cloud, primarily due to the angle of view and blocking limitations. Therefore, point cloud completion is an urgent problem in point cloud data applications. Most existing deep learning methods first generate rough frameworks through the global characteristics of incomplete point clouds, and then generate complete point clouds by refining the framework. However, such point clouds are undesirably biased toward average existing objects, meaning that the completion results lack local details. Thus, we propose a multi-view-based shape-preserving point completion network with an encoder–decoder architecture, termed a point projection network (PP-Net). PP-Net completes and optimizes the defective point cloud in a projection-to-shape manner in two stages. First, a new feature point extraction method is applied to the projection of a point cloud, to extract feature points in multiple directions. Second, more realistic complete point clouds with finer profiles are yielded by encoding and decoding the feature points from the first stage. Meanwhile, the projection loss in multiple directions and adversarial loss are combined to optimize the model parameters. Qualitative and quantitative experiments on the ShapeNet dataset indicate that our method achieves good results in learning-based point cloud shape completion methods in terms of chamfer distance (CD) error. Furthermore, PP-Net is robust to the deletion of multiple parts and different levels of incomplete data.

Spherically Stratified Point Projection: Feature Image Generation for Object Classification Using 3D LiDAR Data

Three-dimensional point clouds have been utilized and studied for the classification of objects at the environmental level. While most existing studies, such as those in the field of computer vision, have detected object type from the perspective of sensors, this study developed a specialized strategy for object classification using LiDAR data points on the surface of the object. We propose a method for generating a spherically stratified point projection (sP2) feature image that can be applied to existing image-classification networks by performing pointwise classification based on a 3D point cloud using only LiDAR sensors data. The sP2’s main engine performs image generation through spherical stratification, evidence collection, and channel integration. Spherical stratification categorizes neighboring points into three layers according to distance ranges. Evidence collection calculates the occupancy probability based on Bayes’ rule to project 3D points onto a two-dimensional surface corresponding to each stratified layer. Channel integration generates sP2 RGB images with three evidence values representing short, medium, and long distances. Finally, the sP2 images are used as a trainable source for classifying the points into predefined semantic labels. Experimental results indicated the effectiveness of the proposed sP2 in classifying feature images generated using the LeNet architecture.

The Role of Ultrasonography in the Diagnosis and Decision Algorithm for the Management of Pneumothorax after Transbronchial Lung Cryobiopsy

<b><i>Background:</i></b> Pneumothorax is one of the main complications of transbronchial lung cryobiopsy (TBLC). Chest ultrasound (CUS) is a radiation-free alternative method for pneumothorax detection. <b><i>Objective:</i></b> We tested CUS diagnostic accuracy for pneumothorax and assessed its role in the decision algorithm for pneumothorax management. Secondary objectives were to evaluate the post-procedure pneumothorax occurrence and risk factors. <b><i>Methods:</i></b> Eligible patients underwent TBLC, followed by chest X-ray (CXR) evaluation 2 h after the procedure, as our standard protocol. Bedside CUS was performed within 30 min and 2 h after TBLC. Pneumothorax by CUS was defined by the absence of lung sliding and comet-tail artefacts and confirmed with the stratosphere sign on M-mode. Pneumothorax size was determined through lung point projection on CUS and interpleural distance on CXR and properly managed according to clinical status. <b><i>Results:</i></b> Sixty-seven patients were included. Nineteen pneumothoraces were detected at 2 h after the procedure, of which 8 (42.1%) were already present at the first CUS evaluation. All CXR-detected pneumothoraces had a positive CUS detection. There were 3 discordant cases (κ = 0.88, 95% CI: 0.76–1.00, <i>p</i> &#x3c; 0.001), which were detected by CUS but not by inspiration CXR. We calculated a specificity of 97.5% (95% CI: 86.8–99.9) and a sensitivity of 100% (95% CI: 87.2–100) for CUS. Pneumothorax rate was higher when biopsies were taken in 2 lobes and if histology had pleural representation. Final diagnosis was achieved in 79.1% of patients, with the most frequent diagnosis being hypersensitivity pneumonitis. Regarding patients with large-volume pneumothorax needing drainage, the rate of detection was similar between CUS and CRX. <b><i>Conclusion:</i></b> CUS can replace CXR in detecting the presence of pneumothorax after TBLC, and the lung point site can reliably indicate its size. This useful method optimizes time spent at the bronchology unit and allows immediate response in symptomatic patients, helping to choose optimal treatment strategies, while preventing ionizing radiation exposure.

Characterization of Energetic Protons Generated in the ShenGuang-II UP Petawatt Laser Interactions with Foil Targets

The characterization of energetic protons generated in the ShenGuang-II UP petawatt laser interactions with foil targets has been systematically studied. The proton energy spectra and angular distributions are measured with a radiochromic film stack. It shows that the proton energy spectra have a Boltzmann distribution with temperature of about 2.8 MeV and cutoff energy of about 20 MeV. The divergence angles of protons vary from 10° to 60°, dependent on the proton energy. The proton source size and location are investigated via the proton point-projection mesh imaging. The proton virtual sources are found to locate tens to hundreds of microns in front of the foil target, depending on the proton energies. A Monte Carlo simulation estimates the diameter of the virtual proton source to be about 12 μm for the protons with energy of 16.8 MeV, which is much smaller than the laser focus size of about 50 μm. The spatial resolution of the 16.8 MeV proton imaging is quantified with the point spread function to be about 15 μm, which is consistent with the proton virtual source size. These results will be important for the users conducting experiments with the protons as a backlighting source on the ShenGuang-II UP petawatt laser.

Solving Systems of Linear Equations Based on Approximation Solution Projection Analysis

The paper considers an iterative method for solving systems of linear equations (SLE), which applies multiple displacement of the approximation solution point in the direction of the final solution, simultaneously reducing the entire residual of the system of equations. The method reduces the requirements for the matrix of SLE. The following SLE property is used: the point is located farther from the system solution result compared to the point projection onto the equation. Developing the approach, the main emphasis is made on reduction of requirements towards the matrix of the system of equations, allowing for higher volume of calculations.