RRL: A Rich Representation Language for the Description of Agent Behaviour in NECA

In this paper, we describe the Rich Representation Language (RRL) which is used in the NECA system. The NECA system generates interactions between two or more animated characters. The RRL is a formal framework for representing the information that is exchanged at the interfaces between the various NECA system modules

Identification of block-like movement in cohesive and non-cohesive spouted bed operations

Spouted beds are widely used in the chemical and process industries for a large variety of processes. Good understanding of the transport phenomena in these systems is of great importance to improve the design and scale-up procedure [1]. Gas- solid flow heterogeneities, such as particle clustering can have a significant impact on interphase transport properties. The focus of the research is on the interaction between solid particles and interstitial gas. The investigation is realized by means of non-resolved CFD-DEM simulation and the experimental measurement of the spouted bed. Experimental measurements are conducted in a lab-scale pseudo-2D spouted bed test facility. The cohesive material is substituted by non-cohesive particles with an added moister. With wet particles, it is expected to observe the effects related to the particle cohesion, such as channeling and formation of particle clusters. The main purpose of the experiment is to obtain the information on the overall dynamics of the spouted bed in cohesive and non-cohesive flow regimes. The recorded images were processed with DaVis Particle Image Velocimetry (PIV) post-processing tool. Numerical CFD-DEM model is developed to investigate the behavior of non-cohesive particles in a spouted bed. Particles are modeled with the Discrete Element Method (DEM), where one integrates Newton’s law of motion for each particle under the forces due to the surrounding particles. This method is based on the use of an explicit numerical scheme in which the interaction of the particles is monitored contact by contact [2, 3]. Coupled CFD-DEM simulations allow the incorporation of single-particle properties and modifications of their interaction and as such are suitable for this study. Numerical simulation can be validated by the experimental measurement. Future work will deal with cohesive forces between the particles

Pattern formation in laminar flow of suspensions through square channels

The study of particulate suspensions flowing through narrow channels has received much attention in the last decades, especially with advance in the field of microfluidics devices. In square channels, it is known that particles in dilute suspensions migrate to equilibrium positions at the channel faces, and also at the channel corners for a channel Reynolds number above 260. However, most studies focused on very dilute systems, excluding particle-particle interactions. In this paper, we present simulations of suspensions with solid fractions up to 3%. In these simulations, we find two new patterns: at Rech 260, the fraction of particles moving to the corner equilibrium positions increases with solid fraction. We present a characterization of these two effects and speculate about possible mechanisms leading to the formation of both patterns

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

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

Modeling Cycle-to-Cycle Variations of a Spark-Ignited Gas Engine Using Artificial Flow Fields Generated by a Variational Autoencoder

A deeper understanding of the physical nature of cycle-to-cycle variations (CCV) in internal combustion engines (ICE) as well as reliable simulation strategies to predict these CCV are indispensable for the development of modern highly efficient combustion engines. Since the combustion process in ICE strongly depends on the turbulent flow field in the cylinder and, for spark-ignited engines, especially around the spark plug, the prediction of CCV using computational fluid dynamics (CFD) is limited to the modeling of turbulent flows. One possible way to determine CCV is by applying large eddy simulation (LES), whose potential in this field has already been shown despite its drawback of requiring considerable computational time and resources. This paper presents a novel strategy based on unsteady Reynolds-averaged Navier–Stokes (uRANS) CFD in combination with variational autoencoders (VAEs). A VAE is trained with flow field data from presimulated cycles at a specific crank angle. Then, the VAE can be used to generate artificial flow fields that serve to initialize new CFD simulations of the combustion process. With this novel approach, a high number of individual cycles can be simulated in a fraction of the time that LES needs for the same amount of cycles. Since the VAE is trained on data from presimulated cycles, the physical information of the cycles is transferred to the generated artificial cycles