Strategies for simulation software quality assurance applied to open source DEM

We present a strategy to improve the software quality for scientific simulation software, applied to the open source DEM code LIGGGHTS [1] [2]. We aim to improve the quality of the LIGGGHTS DEM code by two measures: Firstly, making the simulation code open source gives the whole user community the possibility to detect bugs in the source code and make suggestions to improve the code quality. Secondly, we apply a test harness, which is an important part of the work-flow for quality assurance in software engineering [5]. In the case of scientific simulation software, it consists of a set of simulation examples that should span the range of applicability of the software as good as possible. Technically, in our case it consists of a set of 10-50 LIGGGHTS simulations and is being run automatically on our cluster, where the number of processors, the code features and the numerical models are varied. Qualitative results are automatically extracted and are plotted for comparison, so thus a huge parameter space of flow regimes, numerical models, code features and parallelization situations can be governed. A test harness can aid in (a) finding bugs in the software, (b) checking parallel efficiency and consistency, (c) comparing different numerical models, and, most importantly, (d) experimental validation. Parallel consistency means that within a parallel framework, we need to have the possibility to compare the answers that a run with a different number of processors gives and the time that it takes to compute them. Experimental validation is especially important for scientific simulations. If experimental data is available for a test case, the experimental data is automatically compared to the numerical results, by means of global quantities such number of particles in the simulation, translational and rotational kinetic energy, thermal energy etc. The LIGGGHTS test harness aims to be a transparent and open community effort that everybody can contribute to in order to improve the quality of the LIGGGHTS code. We illustrate the usefulness of the test harness with several examples, where we especially focus on experimental validation

DEM particle characterization by artificial neural networks and macroscopic experiments

The macroscopic simulation results in Discrete Element Method (DEM) simulations are determined by particle-particle contact laws. These usually depend on semi-empirical parameters, difficult to obtain by direct microscopic measurements. Sub- sequently, macroscopic experiments are performed, and their results need to be linked to the microscopic DEM simulation parameters. Here, a methodology for the identifica- tion of DEM simulation parameters by means of macroscopic experiments and dedicated artificial neural networks is presented. We first trained a feed forward artificial neural network by backward propagation reinforcement through the macroscopic results of a se- ries of DEM simulations, each with a set of particle based simulation parameters. Then, we utilized this artificial neural network to forecast the macroscopic ensemble behaviour in dependence of additional sets of particle based simulation parameters. We finally re- alized a comprehensive database, to connect particle based simulation parameters with a specific macroscopic ensemble output. The trained artificial neural network can predict the behaviour of additional sets of input parameters fast and precisely. Further, the nu- merical macroscopic behaviour obtained with the neural network is compared with the experimental macroscopic behaviour obtained with calibration experiments. We hence determined the DEM simulation parameters of a specific granular material

Parscale - an open-source library for the simulation of intra-particle heat and mass transport processes in coupled simulations

We introduce the open-source library ParScale for the modeling of intra-particle transport processes in non-isothermal reactive fluid-particle flows. The underlying equations, the code architecture, as well as the coupling strategy to the widely-used DEM solver LIGGGHTS is presented. A set of verification cases, embedded into an automated test harness, is presented that proofs the functionality of ParScale. To demonstrate the capabilities of ParScale, we perform simulations of a non-isothermal granular shear flow including heat transfer to the surrounding fluid. We present results for the conductive heat flux through the particle bed for a wide range of dimensionless cooling rates and particle volume fractions. Our data suggests that intra-particle temperature gradients need to be considered for an accurate prediction of the conductive flux in case of (i) a dense particle bed and (ii) for large cooling rates characterized by a critical Biot number of ca Bicrit ≈ 0.1

Determining the coefficient of friction by shear tester simulation

The flow behaviour of very dense particle regimes such as in a moving or fluidized bed is highly dependent on the inter-particle friction, which can be characterized by the coefficient of friction. Since only rough guide values for common material pairs are available in the literature, we determine the exact parameters by fitting numerical simulations to experimental measurements of a simplified Jenike shear tester [1, 2]. The open-source discrete-element-method code LIGGGHTS [3] is used to model the shear cell, which is built of triangulated meshes. In order to preload the bulk solid in the shear cell with a constant principal stress, the movement of these walls is controlled by a prescribed load. A comprehensive sensitivity study shows that the results are nearly insensitive to the spatial dimensions of the shear tester as well as all other material properties. Therefore, this set-up is applicable to determine the coefficient of friction. Furthermore, we calculate the coefficient of friction of glass beads showing very good agreement with literature data and in-house experiments. Hence, this procedure can be used to deduce material parameters for the numerical simulation of dense granular flows

A 3D-1D model for the simulation of plant-scale chemical reactors

A 3D-1D model has been developed to simulate the methane dehydroaromatization (MDA) process in plant-scale catalytic reactors. The 3D part of the model consists of CFD-DEM coupled simulations of some relevant volume elements (RVEs), while the 1D part is a low-order model bridging the solution between the RVEs. The CFD-DEM model, implemented in the CFDEM®coupling software, uses an immerse boundary method to resolve: 1) the flow around the catalytic structures, 2) the heat exchange between solid and fluid, 3) the MDA reaction at the fluid-catalyst interface. The CFD-DEM solution is scaled-up by the 1D model to allow the simulation of industrial-scale processes at acceptable computational cost. The effect of design parameters (e.g., catalyst geometry) and operating conditions (e.g., reactor operating temperature) on the methane conversion rate and pressure drop can be investigated using the proposed model and the main results will be presented

A 3D-1D model for the simulation of plant-scale chemical reactors

A 3D-1D model has been developed to simulate the methane dehydroaromatization (MDA) process in plant-scale catalytic reactors. The 3D part of the model consists of CFD-DEM coupled simulations of some relevant volume elements (RVEs), while the 1D part is a low-order model bridging the solution between the RVEs. The CFD-DEM model, implemented in the CFDEM®coupling software, uses an immerse boundary method to resolve: 1) the flow around the catalytic structures, 2) the heat exchange between solid and fluid, 3) the MDA reaction at the fluid-catalyst interface. The CFD-DEM solution is scaled-up by the 1D model to allow the simulation of industrial-scale processes at acceptable computational cost. The effect of design parameters (e.g., catalyst geometry) and operating conditions (e.g., reactor operating temperature) on the methane conversion rate and pressure drop can be investigated using the proposed model and the main results will be presented

Boundary Graph Neural Networks for 3D Simulations

The abundance of data has given machine learning considerable momentum in natural sciences and engineering. However, the modeling of simulated physical processes remains difficult. A key problem is the correct handling of geometric boundaries. While triangularized geometric boundaries are very common in engineering applications, they are notoriously difficult to model by machine learning approaches due to their heterogeneity with respect to size and orientation. In this work, we introduce Boundary Graph Neural Networks (BGNNs), which dynamically modify graph structures to address boundary conditions. Boundary graph structures are constructed via modifying edges, augmenting node features, and dynamically inserting virtual nodes. The new BGNNs are tested on complex 3D granular flow processes of hoppers and rotating drums which are standard components of industrial machinery. Using precise simulations that are obtained by an expensive and complex discrete element method, BGNNs are evaluated in terms of computational efficiency as well as prediction accuracy of particle flows and mixing entropies. Even if complex boundaries are present, BGNNs are able to accurately reproduce 3D granular flows within simulation uncertainties over hundreds of thousands of simulation timesteps, and most notably particles completely stay within the geometric objects without using handcrafted conditions or restrictions

Phase slip lines in superconducting few-layer NbSe2 crystals

We show the results of two-terminal and four-terminal transport measurements on few-layer NbSe2 devices at large current bias. In all the samples measured, transport characteristics at high bias are dominated by a series of resistance jumps due to nucleation of phase slip lines, the two dimensional analogue of phase slip centers. In point contact devices the relatively simple and homogeneous geometry enables a quantitative comparison with the model of Skocpol, Beasley and Tinkham. In extended crystals the nucleation of a single phase slip line can be induced by mechanical stress of a region whose width is comparable to the charge imbalance equilibration length