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Varga M.,Ac2t Research Gmbh | Goniva C.,Johannes Kepler University | Goniva C.,DCS Computing GmbH | Adam K.,Voestalpine AG | Badisch E.,Ac2t Research Gmbh
Tribology International

Within this work, a combined experimental and numerical approach to fundamentally understand erosive wear in feed pipes was initiated. By experimental lab-scale testing, it was shown that erosion rates strongly depend on the material's properties and testing conditions. Steel wear was more pronounced at higher impact angle, whereas low impact angle was more critical for rubber. Lab-tests results distinguish from empirical erosion models because material dependent critical impact energies and fatigue phenomena cannot be considered there. A CFD-DEM approach was conducted for simulation of particulate flow in pipes. In addition, long term wear measurements were done to gain data of the wear progress. Although further validation and testing are necessary, very promising results on erosion prediction could be achieved. © 2013 Elsevier Ltd. All rights reserved. Source

Agency: Cordis | Branch: FP7 | Program: MC-ITN | Phase: FP7-PEOPLE-2013-ITN | Award Amount: 4.05M | Year: 2014

Dry, wet and multiphase particulate materials constitute over 75% of raw material feedstock to industry. Despite their significance, many industrial particulate processes display unpredictable behaviour due to both their multiscale nature and the coexistence of different phases: this leads to undesirable losses in resources, energy, money and time. Considerable progress can be achieved using multiscale analysis and modelling to provide both visual and quantitative details of the dynamics of multiphase particulate systems. However, immature predictive capabilities, together with a lack of expertise and education in this developing field, hinder the adoption of these technologies. To address this skills gap and to initiate further advances in the field, it is crucial that a coordinated and intersectoral approach (combining different industrial sectors and fields of science) is taken, broadening the portfolio of skills currently retained within the EU research community. The T-MAPPP network brings together 15 leading European organizations in their respective fields, including 10 industrial companies (4 of which are SMEs) and stakeholders ranging from agriculture/food processing, consumer/personal care, chemicals/pharmaceuticals to software and equipment manufacture, to foster and develop a pool of ESRs and ERs who can transform multiscale analysis and modelling from an exciting scientific tool into a widely adopted industrial method. Through the delivery of sound scientific training and exposure to both Academic and Industrial environments, each of the 15 fellows recruited will be equipped with the multidisciplinary and transferable skills needed not only to initiate further advances in the field, but to become future leaders in Multiscale Analysis (MA) of multiphase Particulate Processes (PPP) and systems. Such skills are Europe-wide in demand, making each fellow a highly desirable candidate for employment and very mobile across the different career domains.

Blais B.,Ecole Polytechnique de Montreal | Lassaigne M.,Ecole Polytechnique de Montreal | Goniva C.,DCS Computing GmbH | Fradette L.,Ecole Polytechnique de Montreal | Bertrand F.,Ecole Polytechnique de Montreal
Journal of Computational Physics

Although viscous solid-liquid mixing plays a key role in the industry, the vast majority of the literature on the mixing of suspensions is centered around the turbulent regime of operation. However, the laminar and transitional regimes face considerable challenges. In particular, it is important to know the minimum impeller speed (Njs) that guarantees the suspension of all particles. In addition, local information on the flow patterns is necessary to evaluate the quality of mixing and identify the presence of dead zones. Multiphase computational fluid dynamics (CFD) is a powerful tool that can be used to gain insight into local and macroscopic properties of mixing processes. Among the variety of numerical models available in the literature, which are reviewed in this work, unresolved CFD-DEM, which combines CFD for the fluid phase with the discrete element method (DEM) for the solid particles, is an interesting approach due to its accurate prediction of the granular dynamics and its capability to simulate large amounts of particles. In this work, the unresolved CFD-DEM method is extended to viscous solid-liquid flows. Different solid-liquid momentum coupling strategies, along with their stability criteria, are investigated and their accuracies are compared. Furthermore, it is shown that an additional sub-grid viscosity model is necessary to ensure the correct rheology of the suspensions. The proposed model is used to study solid-liquid mixing in a stirred tank equipped with a pitched blade turbine. It is validated qualitatively by comparing the particle distribution against experimental observations, and quantitatively by compairing the fraction of suspended solids with results obtained via the pressure gauge technique. © 2016 Elsevier Inc. Source

Benvenuti L.,Johannes Kepler University | Kloss C.,DCS Computing GmbH | Pirker S.,Johannes Kepler University
Powder Technology

In Discrete Element Method (DEM) simulations, particle-particle contact laws determine the macroscopic simulation results. Particle-based contact laws, in turn, commonly rely on semi-empirical parameters which are difficult to obtain by direct microscopic measurements. In this study, we present a method for the identification of DEM simulation parameters that uses artificial neural networks to link macroscopic experimental results to microscopic numerical parameters. In the first step, a series of DEM simulations with varying simulation parameters is used to train a feed-forward artificial neural network by backward-propagation reinforcement. In the second step, this artificial neural network is used to predict the macroscopic ensemble behaviour in relation to additional sets of particle-based simulation parameters. Thus, a comprehensive database is obtained which links particle-based simulation parameters to specific macroscopic bulk behaviours of the ensemble. The trained artificial neural network is able to predict the behaviours of additional sets of input parameters accurately and highly efficiently. Furthermore, this method can be used generically to identify DEM material parameters. For each set of calibration experiments, the neural network needs to be trained only once. After the training, the neural network provides a generic link between the macroscopic experimental results and the microscopic DEM simulation parameters. Based on these experiments, the DEM simulation parameters of any given non-cohesive granular material can be identified. © 2016 Elsevier B.V. Source

Municchi F.,University of Graz | Goniva C.,DCS Computing GmbH | Radl S.,University of Graz
Computer Physics Communications

CPPPO is a compilation of parallel data processing routines developed with the aim to create a library for "scale bridging" (i.e. connecting different scales by mean of closure models) in a multi-scale approach. CPPPO features a number of parallel filtering algorithms designed for use with structured and unstructured Eulerian meshes, as well as Lagrangian data sets. In addition, data can be processed on the fly, allowing the collection of relevant statistics without saving individual snapshots of the simulation state. Our library is provided with an interface to the widely-used CFD solver OpenFOAM®, and can be easily connected to any other software package via interface modules. Also, we introduce a novel, extremely efficient approach to parallel data filtering, and show that our algorithms scale super-linearly on multi-core clusters. Furthermore, we provide a guideline for choosing the optimal Eulerian cell selection algorithm depending on the number of CPU cores used. Finally, we demonstrate the accuracy and the parallel scalability of CPPPO in a showcase focusing on heat and mass transfer from a dense bed of particles. Program summary: Program title: CPPPO. Catalogue identifier: AFAQ_v1_0. Program summary URL: http://cpc.cs.qub.ac.uk/summaries/AFAQ_v1_0.html Program obtainable from: CPC Program Library, Queen's University, Belfast, N. Ireland. Licensing provisions: GNU Lesser General Public License, version 3. No. of lines in distributed program, including test data, etc.: 1043965. No. of bytes in distributed program, including test data, etc.: 11053655. Distribution format: tar.gz. Programming language: C++, MPI, octave. Computer: Linux based clusters for HPC or workstations. Operating system: Linux based. Classification: 4.14, 6.5, 12. External routines: Qt5, hdf5-1.8.15, jsonlab, OpenFOAM/CFDEM, Octave/Matlab. Nature of problem: Development of closure models for momentum, species transport and heat transfer in fluid and fluid-particle systems using purely Eulerian or Euler-Lagrange simulators. Solution method: The CPPPO library contains routines to perform on-line (i.e., runtime) filtering and compute statistics on large parallel data sets. Running time: Performing a Favre averaging on a structured mesh of 1283 cells with a filter size of 643 cells using one Intel Xeon(R) E5-2650, requires approximately 4 h of computation. © 2016 Elsevier B.V. Source

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