Investigation of OpenFOAM-XDEM momentum coupling results for AWJC Nozzle using preCICE

Scientific Conference (2023, February 14)

The high-speed water jet is the momentum source in an Abrasive Water Jet Cutting Nozzle. This momentum is transferred to the abrasive particles & the air within the nozzle. This leads to turbulent ... [more ▼]

The high-speed water jet is the momentum source in an Abrasive Water Jet Cutting Nozzle. This momentum is transferred to the abrasive particles & the air within the nozzle. This leads to turbulent & complex particle-laden flow in the nozzle. These flow conditions can influence particle impacts on the nozzle, thus influencing erosion. Hence it is imperative that this complex particle-laden flow is captured correctly. The momentum exchange can be directly from the water jet to the particles or indirectly through the airflow. In this work, we investigate these fluid-particle momentum exchanges. Our prototype uses preCICE for volumetric coupling of XDEM (for the particle motion), & OpenFOAM (for the fluid). XDEM uses fluid flow conditions to compute the forces acting on particles. XDEM computes the particle momentum source that is injected into the fluid solver. The results of the coupled simulation align with literature & can be extended to include the FEM component for erosion predictions. [less ▲]

Reconstruct the biomass particles fields in the particle-fluid problem using continuum methods by applying the physics-informed neural network

in Results in Engineering (2023), 17

The motion of particles in the moving grate combustion chamber is used as the case study. These problems are categorized as particle-fluid problems. They are typically solved using Lagrangian-Eulerian ... [more ▼]

The motion of particles in the moving grate combustion chamber is used as the case study. These problems are categorized as particle-fluid problems. They are typically solved using Lagrangian-Eulerian methods, one of which is the coupling between the discrete element method (DEM, which is applied to the particles phase) and the computational fluid dynamics method (CFD, which is applied to the fluid phase). The current study's objective is to avoid coupling and instead, focusing on using the CFD method only. There are dense piles of particles moving on the grates in the biomass combustion chamber. We assumed the dense particles' behaviors similar to the fluid, and then, applied the fluid governing equations to the particles phase. The virtual fields of the velocities, pressure and density are specified for the particles' phase. Afterward, the physics-informed neural network (PINN) is used to reconstruct particles' fields and additionally to investigate the capability of the predicted fields to satisfy the fluid governing equations. This model has the benefit of reconstructing the particles' fields without the need for boundaries and initial conditions. The precision of the model is assessed by comparing the test data set with the exact data obtained from the eXtended discrete element method (XDEM is an in-house software). It is demonstrated that the trained neural network delivered high accuracy and is capable of predicting all outputs with an error value of less than 2 percent. Additionally, to choose the optimum architecture for the neural network, the effect of the number of hidden layers and neurons is studied. [less ▲]

Implementing a hybrid immersed boundary/fictitious domain (HFD-IB) method coupled with the Discrete Element Method (DEM) to consider lubrication effects between the particles in the fluid domain

Scientific Conference (2022, August 30)

Suspensions of particles in a fluid domain could be seen in different natural and industrial applications, ranging from food production to blood flow. For this reason, many researchers studied this topic ... [more ▼]

Suspensions of particles in a fluid domain could be seen in different natural and industrial applications, ranging from food production to blood flow. For this reason, many researchers studied this topic to get a better insight into the physics of the problem. One of the main topics in this field is to understand the inter-particle forces. In which, particles in the fluid domain face three main forces, which are hydrodynamic long-range interaction, a collision between particles, and lubrication forces. [1] Treatment of first and second forces is straightforward because they could be modeled accurately with the computational fluid dynamics (CFD) method coupled with the Discrete Element Method (CFD-DEM). However, this strategy becomes less accurate for calculating lubrication force when two particles approach each other in a gap distance smaller than the grid size. This is because the grid resolution is not fine enough to capture the correct hydrodynamic interaction. Among different CFD methods that could be implemented to consider the physics of the suspensions of particles, Immersed Boundary (IB) method has provided a better description of the nature of the topic since it could be used to provide fully resolved CFD simulation. However, the results of the previous researchers also have shown the IB methods also face difficulty in correctly capturing the lubrication effects. Although some researchers have proposed to add a corrective force term in the IB method, this strategy faces a stability problem when there are many particles inside the simulation domain. For this reason, Naoki Hori et al [2] proposed a new strategy to use IB without considering any correction term. In their work, the C dt parameter is defined as a function of time step size, fluid viscosity, and mesh resolution. By keeping this parameter in a specific range, IB could simulate lubrication force with high accuracy, meaning that the grid resolution and time step size are the key parameters in determining the lubrication force. [2] In the present work, a variant of the IB method named the hybrid immersed-boundary/fictitious domain (HFD-IB) method was selected as the CFD solver. [3] Then, it was coupled with the XDEM code to consider the collision forces between the particles. After successful validation of this CFD-DEM solver, the problem of falling two inline spherical particles in the fluid domain is considered. Our solver could get the interaction forces between two-particle correctly by keeping the C dt in a specific range mentioned by the reference articles. As seen in Figure 1, drafting, kissing, and tumbling of particles are illustrated. References [1] Kroupa, M., Vonka, M., Soos, M. and Kosek, J., 2016. Utilizing the discrete element method for the modeling of viscosity in concentrated suspensions. Langmuir, 32(33), pp.8451-8460. [2]Hori, N., Rosti, M.E. and Takagi, S., 2022. An Eulerian-based immersed boundary method for particle suspensions with implicit lubrica [3] Municchi, F. and Radl, S., 2017. Consistent closures for Euler-Lagrange models of bi-disperse gas-particle suspensions derived from particle-resolved direct numerical simulations. International Journal of Heat and Mass Transfer, 111, pp.171-190. [less ▲]

Parallel Multi-Physics Simulation of Biomass Furnace and Cloud-based Workflow for SMEs

in Practice and Experience in Advanced Research Computing (PEARC '22) (2022, July)

Biomass combustion is a well-established process to produce energy that offers a credible alternative to reduce the consumption of fossil fuel. To optimize the process of biomass combustion, numerical ... [more ▼]

Biomass combustion is a well-established process to produce energy that offers a credible alternative to reduce the consumption of fossil fuel. To optimize the process of biomass combustion, numerical simulation is a less expensive and time-effective approach than the experimental method. However, biomass combustion involves intricate physical phenomena that must be modeled (and validated) carefully, in the fuel bed and in the surrounding gas. With this level of complexity, these simulations require the use of High-Performance Computing (HPC) platforms and expertise, which are usually not affordable for manufacturing SMEs. In this work, we developed a parallel simulation tool for the simulation of biomass furnaces that relies on a parallel coupling between Computation Fluid Dynamics (CFD) and Discrete Element Method (DEM). This approach is computation-intensive but provides accurate and detailed results for biomass combustion with a moving fuel bed. Our implementation combines FOAM-extend (for the gas phase) parallelized with MPI, and XDEM (for the solid particles) parallelized with OpenMP, to take advantage of HPC hardware. We also carry out a thorough performance evaluation of our implementation using an industrial biomass furnace setup. Additionally, we present a fully automated workflow that handles all steps from the user input to the analysis of the results. Hundreds of parameters can be modified, including the furnace geometry and fuel settings. The workflow prepares the simulation input, delegates the computing-intensive simulation to an HPC platform, and collects the results. Our solution is integrated into the Digital Marketplace of the CloudiFacturing EU project and is directly available to SMEs via a Cloud portal. As a result, we provide a cutting-edge simulation of a biomass furnace running on HPC. With this tool, we demonstrate how HPC can benefit engineering and manufacturing SMEs, and empower them to compute and solve problems that cannot be tackled without. [less ▲]

An Innovative Partitioning Technology for Coupled Software Modules

in Proceedings of the 12th International Conference on Simulation and Modeling Methodologies, Technologies and Applications - SIMULTECH (2022, July)

Multi-physics simulation approaches by coupling various software modules is paramount to unveil the underlying physics and thus leads to an improved design of equipment and a more efficient operation ... [more ▼]

Multi-physics simulation approaches by coupling various software modules is paramount to unveil the underlying physics and thus leads to an improved design of equipment and a more efficient operation. These simulations are in general to be carried out on small to massively parallelised computers for which highly efficient partitioning techniques are required. An innovative partitioning technology is presented that relies on a co-located partitioning of overlapping simulation domains meaning that the overlapping areas of each simulation domain are located at one node. Thus, communication between modules is significantly reduced as compared to an allocation of overlapping simulation domains on different nodes. A co-located partitioning reduces both memory and inter-process communication. [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 ▲]

CFD-DEM simulation of melt pool formation and evolution in powder bed fusion process

Poster (2022, May 31)

Computational models can be used to optimize metal additive manufacturing parts, and can also play a role in the evaluation of component quality. Among the most important components of such models will be ... [more ▼]

Computational models can be used to optimize metal additive manufacturing parts, and can also play a role in the evaluation of component quality. Among the most important components of such models will be the detailed simulation of flow and heat transfer in and around the melt pool that is formed when the powder bed is melted. In the present work, A Powder Bed Fusion process is studied numerically by using a coupled Computational Fluid Dynamics (CFD) model and eXtended Discrete Element Method (XDEM) model to predict the physical behavior of discrete particles and the melt pool. In XDEM, a randomly packed powder bed of spherical particles is generated and heat and momentum exchange of each particle with other particles and the melt pool are calculated. The CFD model will predict the effects of laser-melt and powder-melt interactions on the melt pool dynamics. Using the developed numerical framework, it will be possible to determine how powder size distribution, the velocity of a laser beam, and the power, among other factors, will affect the characteristics of melt pool. [less ▲]

XDEM study of burden distribution in iron ore pellet firing

in Ironmaking and Steelmaking (2022), 49(6), 615-625

In the current study, a pseudo-2D XDEM packed bed reactor model is used to assess burden distribution effects in the firing of magnetite iron ore pellets. The model couples heat, mass, and momentum ... [more ▼]

In the current study, a pseudo-2D XDEM packed bed reactor model is used to assess burden distribution effects in the firing of magnetite iron ore pellets. The model couples heat, mass, and momentum balances of the gas phase in each CFD cell to the relevant transport phenomena of each pellet. It was found that the model predictions in terms of temperature and final composition conform well with experimental measurements. Moreover, numerical results show that both of the tested methods, namely, physical (size-separated charge) and chemical (local addition of carbon) burden distributions can improve the thermal state of the firing bed. Furthermore, the results highlight that using size separated feed leads to homogeneity enhancement in final product quality; however, the local addition of carbon can severely deteriorate the quality. [less ▲]

Prediction of the biomass particles through the physics informed neural network.

in ECCOMAS Congress 2022 - 8th European Congress on Computational Methods in Applied Sciences and Engineering (2022)

Woody biomass energy is a kind of renewable energy that contributes to the reduction of greenhouse gas emissions, the creation of healthier forests, and the reduction of wildfire danger. Simulations of ... [more ▼]

Woody biomass energy is a kind of renewable energy that contributes to the reduction of greenhouse gas emissions, the creation of healthier forests, and the reduction of wildfire danger. Simulations of biomass combustion, in general, are time-consuming simulations with a large number of input particles. We use a deep hidden physics-based neural network model to predict the behavior of particles throughout the simulation based on the equations of motion to achieve an efficient simulation and reduce the processing effort. We replace discrete element methods with inverse methods, which have the advantage of simulating velocity fields without knowing the simulation's boundary and initial conditions. Reconstruction of the velocity fields is done using a recurrent neural network in conjunction with a physics-based loss function. The proposed model is suitable for modeling problems that involve moving particles in a fixed bed. The number of neurons and activation functions in the artificial neural network are optimized, and the effect of the sampling method and the number of outputs are studied. [less ▲]

Analysis of a blast furnace behaviour through a resolving euler-lagrange approach

Scientific Conference (2021, October)