Slag eye formation in single and dual bottom purged industrial steelmaking ladles

ABSTRACT

Argon gas is often injected from the bottom of the ladle during steel refining operations. The injected gas interacts with the liquid (metal and slag) bath and enhances the momentum, heat, and mass transfer rate in the melt. However, during these gas–liquid interactions, an opening of the slag layer called slag eye is formed, which exposes the molten metal surface to the atmosphere, which is generally undesirable. In the current work, a transient, three-dimensional mathematical model is used to study the turbulent gas–liquid interactions in single as well as dual bottom blown industrial steelmaking ladles. A Coupled Level Set Volume of Fluid (CLSVOF) model is used for tracking the steel-argon, steel-slag, and argon-slag interfaces, from which the slag-eye area has been predicted. It is found that the inlet gas purging rate, melt height, slag layer thickness, angular and radial positions of the gas inlets affect the slag opening area. Non-dimensional empirical correlations are proposed to predict the slag opening area in both single as well as dual purged ladles, using non-linear regression analysis.     

基于CFD-DEM方法的净化器流场模拟与结构优化

针对空气净化器能耗高的问题，使用离散元方法(DEM)在吸附滤网中建立随机堆积柱形活性炭模型，采用计算流体力学(CFD)方法对空气净化器内部流场进行数值模拟，在模拟与实验验证的基础上，考察了压降最小、流场最均匀的吸附滤网结构。结果表明，空气净化器压降主要发生在轴向，活性炭吸附滤网中回流、沟流现象严重，流体阻力是其他两种滤网的3倍。边数对多边形填充孔结构吸附滤网内压降与流场均匀性无影响，当孔结构改为圆形时，压降减小约52 Pa，节能18.4%(49 W)；当孔直径由8 mm增至12 mm，压降减小约48 Pa，节能19.4%(45 W)；滤网间距对空气净化器压降无影响，圆形、小孔径的吸附滤网内流场最均匀。     

宇宙线缪子散射成像模拟与算法研究

本文验证了基于Micromegas探测器的宇宙线缪子散射成像系统进行快速核材料检测的可行性，并对实验室宇宙线缪子成像系统原型进行参数估算。基于Geant4程序开发了用于模拟宇宙线缪子物理过程、传输径迹及Micromegas探测器响应的模拟程序。在模拟数据的基础上，实现并改进了两种主要的宇宙线缪子散射成像算法。根据模拟和成像结果，1 m×1 m成像系统可在10 min内检测到被重元素屏蔽的核材料。10 cm×10 cm成像系统的缪子事例触发率为0.16 s^-1，要获得较为清晰的成像结果，要求探测器位置分辨率达到300 μm，探测器增益为1 000时实际测量事例至少需要20 h。     

R2D2: A scalable deep learning toolkit for medical imaging segmentation

Deep learning has gained a significant popularity in recent years thanks to its tremendous success across a wide range of relevant fields of applications, including medical image analysis domain in particular. Although convolutional neural networks (CNNs) based medical applications have been providing powerful solutions and revolutionizing medicine, efficiently training of CNNs models is a tedious and challenging task. It is a computationally intensive process taking long time and rare system resources, which represents a significant hindrance to scientific research progress. In order to address this challenge, we propose in this article, R2D2, a scalable intuitive deep learning toolkit for medical imaging semantic segmentation. To the best of our knowledge, the present work is the first that aims to tackle this issue by offering a novel distributed versions of two well-known and widely used CNN segmentation architectures [ie, fully convolutional network (FCN) and U-Net]. We introduce the design and the core building blocks of R2D2. We further present and analyze its experimental evaluation results on two different concrete medical imaging segmentation use cases. R2D2 achieves up to 17.5× and 10.4× speedup than single-node based training of U-Net and FCN, respectively, with a negligible, though still unexpected segmentation accuracy loss. R2D2 offers not only an empirical evidence and investigates in-depth the latest published works but also it facilitates and significantly reduces the effort required by researchers to quickly prototype and easily discover cutting-edge CNN configurations and architectures.     

Cable tray FIRE tests simulations in open atmosphere and in confined and mechanically ventilated compartments with the CALIF3S/ISIS CFD software

Fire hazard in nuclear power plants (NPPs) is particularly often investigated as potential cause of safety equipment failure and confinement loss. Many fire events recorded in NPPs involve electric cables, widely used throughout facilities. IRSN is developing the CALIF3S/ISIS computational fluid dynamics software devoted to fire simulation in large‐scale confined and mechanically ventilated compartments. This paper presents two aspects of the CALIF3S/ISIS code ability to simulate fires. The first one concerns vertical and horizontal spreading of a cable tray fire in open atmosphere using an approach based on the FLASH‐CAT cable fire spread model. Resorting to the suitable parameters of the FLASH‐CAT model based on video fire analyses of tests enables to properly compute the heat release rate of the fire. The second aspect concerns the ability to simulate the evolution and consequences of fires in confined and mechanically ventilated compartments. For these cases, the heat release rate measured during the corresponding experiment is used as input data for the calculations. The predicted evolutions of pressure or gas temperatures are in relatively good accordance with the experiments. The major discrepancy concerns gas concentrations in the fire room which is attributed to a lack of information about the properties of the fuel material.     

The application of photography in investigating fraud

The impact of fraud on the world economy is significant. As fraud has evolved it has become more organized and increasingly cyber-enabled and committed across borders of law enforcement jurisdictions. The technological demands facing fraud investigators are increasing. Yet, little attention has been given to how forensic photography can help tackle emerging crime types, like fraud. This study reviews literature surrounding forensic photography and provides an up to date and collective narrative. This article focuses on the advancements in photography and how imaging-science techniques can apply to a fraud investigation. Photographic practice plays a key role in many crime investigations but must embrace continual change to ensure that it remains relevant to modern policing. Fresh approaches are essential and practitioners must fully utilize new technologies and adapt to tackle the increasingly demanding scope of fraud types.