Determination of Kinetic Parameters for Heterogeneous Reaction System Employing Discrete Element Methods under HPC Platforms

Poster (2023, June 06)

The complex processes of heterogeneous reactions of granular materials such as occurring during metals-ore reduction or biomass gasification involve numerous physical phenomena. The combination of ... [more ▼]

The complex processes of heterogeneous reactions of granular materials such as occurring during metals-ore reduction or biomass gasification involve numerous physical phenomena. The combination of elevated temperature, complex flow, aggressive atmosphere and heterogeneous chemistry make it difficult to study these industrial processes. One of the most important aspects f heterogeneous reactions is to understand and quantify the evolution of the different transformations. For instance, during metal-oxides reduction processes, it is of high importance to quantify the rate at which the pure metal is formed. Nevertheless, it is almost impossible, by experimental means only, to separately observe, accurately quantify and gain insight into these mingled nonlinear physical and chemical processes. In the last decade, numerical simulation tools for particulate processes, such as the eXtended Discrete Element Method (XDEM), have become indispensable to study complex systems without the need of costly experimental practices. In the past, the XDEM has been employed to predict the reduction of tungsten trioxide (WO 3) in dry hydrogen (H2) atmospheres [1] and reduction of iron ores [2]. In the before-mentioned research works, it was employed kinetic data extracted from literature. On one hand, in these processes the kinetic data differ from each other. This is due to the fact that the experimental data in the literature is interpreted with lumped models and empirical models bonded to the specific experimental conditions. On the other hand, advanced simulation tools, such as XDEM, account for all the influencing phenomena (e.g. species and energy distribution, flow conditions, particles shape, rheological properties) constantly interacting in time and space. In these advanced simulation tools, each particle is treated and solved as individual entities and an accurate prediction of the species formation and transport in time and space is provided. Thus, in such advanced numerical tools, the reaction rate parameters representative of the kinetics alone of the involved chemical reactions must be employed. In this contribution, two XDEM simulation case studies accounting for the industrial reduction of WO 3 are presented. The first case study is employed to determine the reaction rate parameters of the four prevalent reduction steps (WO 3↔WO2.9↔WO2.72↔WO2↔W) upon the H 2 reduction of O3. Where the reaction rates are modeled following an Arrhenius law with two parameters per step i.e. pre-exponential factor and activation energy). The constituted optimization problem of minimization of error of the XDEM simulations vs experimental data, implemented and solved in a High Performance Computing (HPC) cluster, is presented and discussed. The determined parameters are later assessed by comparison to a secondly presented case study. [less ▲]

Developing a DEM-Coupled OpenFOAM solver for multiphysics simulation of additive manufacturing process

in Scipedia.com (2022, December)

Powder-based additive manufacturing technologies, specifically selective laser melting, are challenging to model due to the complex, interrelated physical phenomena that are present on multiple spatial ... [more ▼]

Powder-based additive manufacturing technologies, specifically selective laser melting, are challenging to model due to the complex, interrelated physical phenomena that are present on multiple spatial scales, during the process. A key element of such models will be the detailed simulation of flow and heat transfer throughout the melt pool that is formed when the powder particles melt. Due to the high temperature gradients that are rised inside the melt pool, Marangoni force plays a key role in governing the flows inside the melt pool and deciding its shape and dimensions[1]. On the other hand the mass and heat transfer between the melt and the powder also has a signifacnt role in shaping the melt pool at the edges. In this study we modified an OpenFOAM solver(icoReactingMultiphaseInterFoam) cou- pled with an in-house developed DEM code known as eXtended Discrete Element Method or XDEM which models the dynamics and thermodynamics of the particles[2]. By adding the Marangoni force to the momentum equation and also defining a laser model as a boundary Condition for Liquid-Gas Interface, the solver is capable of modeling selective laser melting process from the moment of particle melting to the completion of the so- solidified track. The coupled solver was validated with an ice-packed bed melting case and was used to simulate a multi-track selective laser melting process. [less ▲]

HEAT AND MASS TRANSFER BETWEEN XDEM & OPENFOAM USING PRECICE COUPLING LIBRARY

Scientific Conference (2022, June 09)

This work demonstrates the rapid development of a simulation environment to achieve Heat and Mass Transfer (HMT) between Discrete Element Methods (DEM) and Computa- tional Fluid Dynamics (CFD). The HMT ... [more ▼]

This work demonstrates the rapid development of a simulation environment to achieve Heat and Mass Transfer (HMT) between Discrete Element Methods (DEM) and Computa- tional Fluid Dynamics (CFD). The HMT coupling can be employed to simulate processes such as drying, pyrolysis, combustion, melting, solid-fluid reactions etc and have indus- trial applications such as biomass furnaces, boilers, heat exchangers, and flow through packed beds. This shows that diverse CFD features and solvers need to be coupled with DEM in order to achieve various applications mentioned above. The proposed DEM-CFD Eulerian-Lagrangian coupling for heat and mass transfer is achieved by employing the preCICE coupling library[1] on volumetric meshes. In our prototype, we use the eXtended Discrete Element Method (XDEM)[2] for handling DEM calculations and OpenFOAM for the CFD. The XDEM solver receives various CFD data fields such as fluid properties, and flow conditions exchanged through preCICE, which are used to set boundary conditions for particles. Various heat transfer and mass transfer laws have been implemented in XDEM to steer HMT source term computations. The heat and mass source terms computed by XDEM are transferred to CFD solver and added as source. These source terms represent particles in CFD. The generic coupling interface of preCICE, XDEM and its adapter allows to tackle a di- verse range of applications. We demonstrate the heat, mass & momentum coupling capa- bilities through various test cases and then compared with our legacy XDEM-OpenFOAM coupling and experimental results. [less ▲]

On the Reduction of Computational Costs for Tungsten Powder Bed Processes

Scientific Conference (2022, May 31)

During the Discrete Element Method (DEM) representation of powder bed processes (e.g. tungsten oxide reduction, tungsten carbide synthesis, selective laser sintering) a numerical solution for each single ... [more ▼]

During the Discrete Element Method (DEM) representation of powder bed processes (e.g. tungsten oxide reduction, tungsten carbide synthesis, selective laser sintering) a numerical solution for each single particle is impractical due to the extremely high number of particles (e.g. 10^12). However, in such processes, particles in the vicinity of each other observe low gradients concerning their thermodynamic state. This characteristic can be exploited to avoid solving repeatedly numerically equivalent equation systems. This contribution presents two numerical methods aiming to reduce the computational costs of DEM approaches for the thermochemical conversion of powder beds. In the two methods after an appropriated numerical treatment, a group of particles under similar boundary conditions is substituted by a single-effective-entity. Consequently, the entire powder space is divided into sub-domains to be solved. The methods result in considerable lower number of equations that increase computational efficiency and enable feasible time simulations. The applications of the industrial synthesis of tungsten powders and the selective laser sintering (SLS) of powder metals are presented and discussed. [less ▲]

Mathematical modeling of thermal behavior of single iron ore pellet during heat hardening oxidation

in Journal of Thermal Analysis and Calorimetry (2021)

In this study, a one-dimensional generic model capable of being integrated with reactor scale models is proposed for a single pellet through solving the transient diferential conservation equations ... [more ▼]

In this study, a one-dimensional generic model capable of being integrated with reactor scale models is proposed for a single pellet through solving the transient diferential conservation equations. Predicted results comparison with the experimental data showed close agreement. In addition, the model was used to investigate the relevance of physical characteristics of pellet, reacting gas composition, difusion factors, and prevailing regime. It was found that the pure magnetite pellet could achieve a temperature rise of about 245 K at oxygen concentration of 40 vol.%, whereas the maximum temperature diference inside the pellet was approximately 24 K. Moreover, increasing pellet size, the maximum attainable temperature reached a peak and then leveled out. Furthermore, by decreasing the pore diameter, the pellet size with peak temperature shifted to the smaller pellet sizes. Analyzing the numerical results also showed that for the small pellet sizes, shortening the difusion path leads to the spreading of the reaction interface. The modeling methodology herein can be applied to any particulate processes and is not limited to the aforementioned case. [less ▲]

Computational Study of the Industrial Synthesis of Tungsten Powders

in Powder Technology (2019)

Discrete Element Method (DEM) is a highly employed Lagrangian technique to represent particulate systems. When DEM techniques are extended by adding thermochemical conversion of solid particles as well as ... [more ▼]

Discrete Element Method (DEM) is a highly employed Lagrangian technique to represent particulate systems. When DEM techniques are extended by adding thermochemical conversion of solid particles as well as their interaction with the surrounding fluid, numerous challenging applications can be numerically studied. Nevertheless, industrial applications with large number of particles, such as powder synthesis or blast furnaces, are often time or size limited due to the high computational efforts that these simulations demand. This contribution introduces the Agglomerated Particle Method (APM) as a numerical technique aiming to reduce the computational costs of coupled Discrete Element Method and Computational Fluid Dynamics (DEM-CFD) approaches for the thermochemical conversion of powder beds. From experimental and numerical investigation on thermochemical conversion of packed beds has been observed that the temperature or composition of particles in a small spatial domain do not vary significantly. Consequently, one single numerical solution may be representative for all the particles on such a domain. Thus, a collection of neighbor particles are represented by one single agglomerated particle solved by eXtended Discrete Element Method (XDEM) techniques. The proposed model is firstly assessed with classic benchmark problems for heating and drying of packed beds. Later, the model approach is employed for predicting the industrial synthesis of metallic tungsten powder. The comparison of APM predictions with resolved XDEM predictions and experimental data shows the proposed model as a viable technique to solve large scale powder applications, such as tungsten powder production, at feasible time. [less ▲]

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

in Particuology (2019), 44

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 ... [more ▼]

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. [less ▲]

Humidity Measurement Analysis for Flow Simulations

Presentation (2018, August 30)

Exploring a Multiphysics Resolution Approach for Additive Manufacturing

in JOM (2018)

Metal additive manufacturing (AM) is a fast-evolving technology aiming to efficiently produce complex parts while saving resources. Worldwide, active research is being performed to solve the existing ... [more ▼]

Metal additive manufacturing (AM) is a fast-evolving technology aiming to efficiently produce complex parts while saving resources. Worldwide, active research is being performed to solve the existing challenges of this growing technique. Constant computational advances have enabled multiscale and multiphysics numerical tools that complement the traditional physical experimentation. In this contribution, an advanced discrete--continuous concept is proposed to address the physical phenomena involved during laser powder bed fusion. The concept treats powder as discrete by the extended discrete element method, which predicts the thermodynamic state and phase change for each particle. The fluid surrounding is solved with multiphase computational fluid dynamics techniques to determine momentum, heat, gas and liquid transfer. Thus, results track the positions and thermochemical history of individual particles in conjunction with the prevailing fluid phases' temperature and composition. It is believed that this methodology can be employed to complement experimental research by analysis of the comprehensive results, which can be extracted from it to enable AM processes optimization for parts qualification. [less ▲]

A DISCRETE-CONTINUOUS APPROACH TO MODEL POWDER METALLURGY PROCESSES

Doctoral thesis (2016)