The XDEM Multi-physics and Multi-scale Simulation Technology: Review on DEM-CFD Coupling, Methodology and Engineering Applications

The XDEM multi-physics and multi-scale simulation platform roots in the Ex- tended Discrete Element Method (XDEM) and is being developed at the In- stitute of Computational Engineering at the University of Luxembourg. The platform is an advanced multi- physics simulation technology that combines flexibility and versatility to establish the next generation of multi-physics and multi-scale simulation tools. For this purpose the simulation framework relies on coupling various predictive tools based on both an Eulerian and Lagrangian approach. Eulerian approaches represent the wide field of continuum models while the Lagrange approach is perfectly suited to characterise discrete phases. Thus, continuum models include classical simulation tools such as Computa- tional Fluid Dynamics (CFD) or Finite Element Analysis (FEA) while an ex- tended configuration of the classical Discrete Element Method (DEM) addresses the discrete e.g. particulate phase. Apart from predicting the trajectories of individual particles, XDEM extends the application to estimating the thermo- dynamic state of each particle by advanced and optimised algorithms. The thermodynamic state may include temperature and species distributions due to chemical reaction and external heat sources. Hence, coupling these extended features with either CFD or FEA opens up a wide range of applications as diverse as pharmaceutical industry e.g. drug production, agriculture food and processing industry, mining, construction and agricultural machinery, metals manufacturing, energy production and systems biology

Degree of hydration-based creep modeling of concrete with blended binders : from concept to real applications

The mechanical behavior of hardening concrete is to a large extent determined by the evolving microstructure as a result of the hydration process. For traditional binder systems, consisting of Portland cement or blast furnace slag cement, the degree of hydration is known to be a fundamental parameter in this respect, enabling a detailed study and accurate prediction of the early-age mechanical behavior, including basic creep. Nowadays, in view of improved sustainability of cementitious materials, binder systems tend to become more complex, consisting of a blend of different powders. As the hydration process and microstructure development are influenced by the inclusion of powders into the binder, the question is raised whether the degree of hydration concept is still applicable to concrete based on complex blended binder systems. In this paper, some experimental results are summarized and the application to real structures is illustrated. Basic creep of hardening concrete with complex blended binders can still be modeled following the degree of hydration concept

Depth estimation of inner wall defects by means of infrared thermography

There two common methods dealing with interpreting data from infrared thermography: qualitatively and quantitatively. On a certain condition, the first method would be sufficient, but for an accurate interpretation, one should undergo the second one. This report proposes a method to estimate the defect depth quantitatively at an inner wall of petrochemical furnace wall. Finite element method (FEM) is used to model multilayer walls and to simulate temperature distribution due to the existence of the defect. Five informative parameters are proposed for depth estimation purpose. These parameters are the maximum temperature over the defect area (Tmax-def), the average temperature at the right edge of the defect (Tavg-right), the average temperature at the left edge of the defect (Tavg-left), the average temperature at the top edge of the defect (Tavg-top), and the average temperature over the sound area (Tavg-so). Artificial Neural Network (ANN) was trained with these parameters for estimating the defect depth. Two ANN architectures, Multi Layer Perceptron (MLP) and Radial Basis Function (RBF) network were trained for various defect depths. ANNs were used to estimate the controlled and testing data. The result shows that 100% accuracy of depth estimation was achieved for the controlled data. For the testing data, the accuracy was above 90% for the MLP network and above 80% for the RBF network. The results showed that the proposed informative parameters are useful for the estimation of defect depth and it is also clear that ANN can be used for quantitative interpretation of thermography data

Metallurgical Process Simulation and Optimization

Metallurgy involves the art and science of extracting metals from their ores and modifying the metals for use. With thousands of years of development, many interdisciplinary technologies have been introduced into this traditional and large-scale industry. In modern metallurgical practices, modelling and simulation are widely used to provide solutions in the areas of design, control, optimization, and visualization, and are becoming increasingly significant in the progress of digital transformation and intelligent metallurgy. This Special Issue (SI), entitled “Metallurgical Process Simulation and Optimization”, has been organized as a platform to present the recent advances in the field of modelling and optimization of metallurgical processes, which covers the processes of electric/oxygen steel-making, secondary metallurgy, (continuous) casting, and processing. Eighteen articles have been included that concern various aspects of the topic

The effect of turbulence on the conversion of coal under blast furnace raceway conditions

dynamics (CFD) can be used to analyze the process virtually and thus improve its performance. Different reducing agents can be used to (partially) substitute the coke and consequently reduce overall emissions. To analyze different reducing agents effectively using CFD, their conversion process has to be modeled accurately. Under certain conditions, coal particles can cluster as the result of turbulence effects, which further reduces the mass transfer to the coal surface and consequently the conversion rate. We analyze the effect of turbulence under blast furnace raceway conditions on the conversion of coal particles and on the overall burnout. The model is applied in RANS to polydisperse particle systems and this is then compared to the simplified monodisperse assumption. Additionally, the model is extended by adding gasification reactions. Overall, we find that the turbulent effects on coal conversion are significant under blast furnace raceway conditions and should be considered in further simulations. Furthermore, we show that an a-priori assessment is difficult because the analysis via averaged quantities is impractical due to a strong variation of conditions in the furnace. Therefore, the effects of turbulence need to be correlated to the regions of conversion. © 2022 The Author(s)The effect of turbulence on the conversion of coal under blast furnace raceway conditionspublishedVersio