Simulation-based investigation of tar formation in after-treatment systems for biomass gasification

Even-though biomass-gasification remains a promising technology regarding de-centralized sustainable energy supply, its main limitations, namely the issues of unsteady operation, tar-formation in after-treatment systems and consequential high maintenance requirements, have never been fully overcome. In order to tackle the latter two deficiencies and to increase the understanding of thermodynamic and thermokinetic producer gas phase phenomena within the after-treatment zones, a numerical system-dynamic model has been created. Thereby naphthalene has been chosen to represent the behaviour of tars. The model has been validated against a wide variety of measured and simulated producer-gas compositions. This work particularly focuses on the investigation and minimization of tar-formation within after-treatment systems at low-pressures and decreasing temperatures. Model-based analysis has led to a range of recommended measures, which could reduce the formation tendency and thus the condensation of tars in those zones. These recommendations are i) to decrease gas residence time within pipes and producer gas purification devices; ii) to increase temperatures in low-pressure zones; iii) to increase hydrogen to carbon ratio as well as iv) to increase oxygen to carbon ratio in the producer gas. Furthermore the numerical model has been included into the cloud-computing platform KaleidoSim. Thus a wider range of process parameter combinations could be investigated in reasonable time. Consequentially a simulation-based sensitivity analysis of producer-gas composition with respect to process parameter changes was conducted and the validity-basis of above recommendations was enlarged

A massive simultaneous cloud computing platform for OpenFOAM

Today the field of numerical simulation in is faced with increasing demands for data-intensive investigations. On the one hand Engineering tasks call for parameter-studies, sensitivity analysis and optimization runs of ever-increasing size and magnitude. In addition the field of Artificial Intelligence (AI) with its notorious hunger for data, urges to provide ever more extensive, numerically derived learning-, testing- and validation input for training e.g. Artificial Neural Networks (ANN). On the other hand the current ‘age of cloud computing’ has set the stage such that nowadays any user of simulation software has access to potentially limitless hardware resources. In the light of these challenges and opportunities, Zurich University of Applied Sciences (ZHAW) and Kaleidosim Technologies AG (Kaleidosim) have developed a publically available Massive Simultaneous Cloud Computing (MSCC) platform for OpenFOAM. The platform is specifically tailored to yield vast amounts of simulation data in minimal Wall Clock Time (WCT). Spanning approximately nine-man-years of development effort the platform now features: • An instructive web-browser-based user interface (Web Interface); • An Application Programming Interface (API); • A Self-Compile option enabling users to run self-composed OpenFOAM applications directly in the cloud; • The Massive Simultaneous Cloud Computing (MSCC) feature which allows the orchestration of up to 500 cloud-based OpenFOAM simulation runs simultaneously; • The option to run Paraview in Batch Mode such that (semi-) automated cloud-based post-processing can be performed; • The Katana File Downloader (KFD) allowing the selective download of specific output dat

Evidence that dicot-infecting mastreviruses are particularly prone to inter-species recombination and have likely been circulating in Australia for longer than in Africa and the Middle East

Viruses of the genus Mastrevirus (family Geminiviridae) are transmitted by leafhoppers and infect either mono- or dicotyledonous plants. Here we have determined the full length sequences of 49 dicot-infecting mastrevirus isolates sampled in Australia, Eritrea, India, Iran, Pakistan, Syria, Turkey and Yemen. Comprehensive analysis of all available dicot-infecting mastrevirus sequences showed the diversity of these viruses in Australia to be greater than in the rest of their known range, consistent with earlier studies, and that, in contrast with the situation in monocot-infecting mastreviruses, detected inter-species recombination events outnumbered intra-species recombination events. Consistent with Australia having the greatest diversity of known dicot-infecting mastreviruses phylogeographic analyses indicating the most plausible scheme for the spread of these viruses to their present locations, suggest that most recent common ancestor of these viruses is likely nearer Australia than it is to the other regions investigated.Department of HE and Training approved lis

Fluorescent Microangiography Is a Novel and Widely Applicable Technique for Delineating the Renal Microvasculature

Rarefaction of the renal microvasculature correlates with declining kidney function. However, current technologies commonly used for its evaluation are limited by their reliance on endothelial cell antigen expression and assessment in two dimensions. We set out to establish a widely applicable and unbiased optical sectioning method to enable three dimensional imaging and reconstruction of the renal microvessels based on their luminal filling. The kidneys of subtotally nephrectomized (SNx) rats and their sham-operated counterparts were subjected to either routine two-dimensional immunohistochemistry or the novel technique of fluorescent microangiography (FMA). The latter was achieved by perfusion of the kidney with an agarose suspension of fluorescent polystyrene microspheres followed by optical sectioning of 200 µm thick cross-sections using a confocal microscope. The fluorescent microangiography method enabled the three-dimensional reconstruction of virtual microvascular casts and confirmed a reduction in both glomerular and peritubular capillary density in the kidneys of SNx rats, despite an overall increase in glomerular volume. FMA is an uncomplicated technique for evaluating the renal microvasculature that circumvents many of the limitations imposed by conventional analysis of two-dimensional tissue sections

KaleidoSim : massive simultaneous cloud computing for multiphysics simulations

The cloud-computing software KaleidoSim has been developed. While the current web-platform focuses on compatibility with the open source CFD toolbox OpenFOAM, KaleidoSim enables MSCC Massive Simultaneous Cloud Computing functionality for any Multiphysics simulation software. Thereby the idea is to provide an easy-to-use, web-browser-based software, which allows the user to run any Multiphysics simulation in the cloud within minutes. Rather than just replacing local hardware, KaleidoSim has been specifically designed to tackle the issue of horizontal scaling in the cloud. While vertical scaling (parallelization) can well be conducted according to a standard selection of cloud-computers with up to 224 cores each, KaleidoSim’s key functionality is about the orchestration of multiple cloud-machines running simultaneously. KaleidoSim has been tested for workflows involving the seamless coordination of up to 500 individual, simultaneous simulation runs. Features include: i) The Self-Compile Option where OpenFOAM-based, self-composed software can be directly uploaded, compiled and run in the cloud; ii) The Katana File Downloader, which enables the selective download of any computed cloud data; iii) The In-Cloud Post-Processing Option for Paraview-based, automated post-processing via Python-trace utilities; iv) The Kaleidoscope Feature enabling the automated creation of large parameter studies; v) The API Application Programming Interface, for command-line-based access of the entire spectrum of KaleidoSim-functionality and for easy integration into third-party software. These capabilities allow the conduction of parameter studies, optimization runs and generally the creation of vast amounts of simulation data with unprecedented speed. This means that Kaleidosim effectively democratized vast computational resources and enables the integration of numerical studies of significantly higher data-intensity, investigative depth and thus quality, into every-day Multiphysics investigations. Application examples of several thermo-fluiddynamic scenarios can be demonstrated in which work-flow-speed-up-factors of no less than 50-100 can be achieved

Tutorial on OpenFOAM & kaleidosim : live demo of speeding up OpenFOAM parameter study by factor 50

YouTubeUsing a couple of relatively simple python utilities, OpenFoam simpleFoam solver and Kaleidosim cloud platform, Prof. G. Boiger of ZHAW_ICP does a 20min live-demo of a work-flow that would have taken one-two full working days only six months ago: The famous OpenFoam 'Motorbike' tutorial case is modified such that relative onset flow velocity is rotated in one-hundred, 3.6 degree steps (360° in total) around the motorbiker. One-hundred individual turbulent steady-state OpenFoam simulation cases are then created automatically, uploaded and run simultaneously in the cloud using Kaleidosim (MSCC Massive Simultaneous Cloud Computing). Drag- and lift- coefficients are being calculated and evaluated for each case from parsing terminal output. One paraview image is being automatically created per simulation run in the cloud using paraview-batch-mode. Results are selectively downloaded from the cloud and paraview images are forged into one movie, rotating the view around the motorbiker along with resulting turbulent flow-field calculation

A multiphysics-simulation-based study of process-parameter-impact on key-performance-attributes of the powder coating of u-profiles

The abilities of Kaleidosim, a Massive Simultaneous Cloud Computing (MSCC) platform and of a Eulerian-Lagrangian finite-volume OpenFOAM-based solver were combined within the powder coating simulation software CoatSim. While the OpenFOAM solver can model Multiphysics corona-formation effects as well as Lagrangian particle-based spray-cloud evolution between coating pistols and electrically grounded metallic substrates, the MSCC capability allows for the simultaneous cloud-based computation of hundreds of process-parameter-scenarios. CoatSim simulates the fluid dynamics of process airflow, coating-particle-dynamics, fluid-particle, particle-particle and highly detailed particle-substrate interaction including blow-off effects, corona formation around the high voltage electrode as well as particle charging mechanisms within the corona. Previous works have demonstrated the practical relevance of the software in terms of qualitative and quantitative validation against hundreds of coating experiments. In addition a comprehensive graphical user-interface was recently added, further increasing its practical applicability. In the current study, CoatSim is applied to compare actually thousands of process-parameter scenarios for the powder coating of U-profiles, which, due to Faraday-cage effects, have always posed a certain challenge for powder coating. More specifically the focus of this study lies on investigating the impact of varying process parameters on key-performance-attributes of the process. The process parameters to-be-varied are: i) applied voltage, ii) primary- and secondary- process airflow rate and iii) particle-charging-function-factors. The key-performance-attributes to be evaluated are: i) visualised coating-patterns, ii) coating-efficiencies, and iii) the relative standard batch-based coating deviation. Limiting investigations to static setups (= pistol not moving), single-pistol-, single-burst- scenarios and simplifying certain parameter-cross-dependencies, optimal U-profile coating-process-settings can be proposed

Massive simultaneous cloud computing (MSCC) for multiphysics-simulation applications

The Massive Simultaneous Cloud Computing concept allows appliers and developers of Multiphysics simulation software to utilize any number of cloud-based computers simultaneously. Thus novel workflows in simulation based research and development can be introduced, focusing on simultaneous rather than on sequential computation of individual cases. On the basis of this capability the simulation-researcher/-engineer can (i) dramatically increase the parameter space of any computational parameter study, (ii) devise novel concepts of conducting optimization procedures and (iii) can ultimately even proceed to produce sufficient simulation data in order to train e.g. artificial neuronal networks. The cloud-based software platform Kaleidosim has been devised to effectively enable the handling of MSCC simulation run series of up to 500 simultaneous cloud runs. Case studies will be presented where a speed up factor of 100 has been achieved in terms of comparing MSCC based parameter studies to the standard workflow on in-house hardware

Tutorial on OpenFOAM & kaleidosim : introducing the kaleidoscope feature

YouTubeThe Kaleidoscope Feature is introduced within the Kaleidosim cloud platform: Using a couple of relatively simple Python utilities, OpenFoam v1912, simpleFoam solver and the Motorbike tutorial case, Prof. G. Boiger of ZHAW_ICP does a 19min live-demo of a work-flow that would have taken four full working days only six months ago: The famous OpenFoam 'Motorbike' tutorial case is modified such that relative onset flow velocity as well as the entire wind channel are being rotated in 360 steps (one step per degree and 360° in total) around the 'Motorbike'. One single base-case is prepared introducing variable parameters within 'initialConditions' and 'blockMeshDict' dictionaries such that: @Variable-Parameter-Name@. Here the variable parameter is the #Angle-of-Attack. The 'eval' function of OpenFoam v1912 is used as well as since wind-channel coordinates are modified with respect to variable angles and using 'sin' and 'cos' functions. The thus prepared base-case is uploaded to Kaleidosim cloud platform. Then the 'Kaleidoscope Feature' comes into play: 360 individual turbulent steady-state OpenFoam simulation cases are created automatically and run simultaneously in the cloud using Kaleidosim (MSCC Massive Simultaneous Cloud Computing). Drag- and lift- coefficients are being calculated and evaluated for each case from parsing terminal output. One Paraview image is being automatically created per simulation run in the cloud using Paraview-Batch-Mode via a prepared Python script that was uploaded along with the OpenFoam case. Results are selectively downloaded from the cloud using the #Katana File Downloader function and Paraview images are automatically forged into one movie, rotating the view around the 'Motorbike' along with resulting turbulent flow-field calculation. It is shown that the whole immense workflow, comprising 360 individual simulation runs on a 300k cell mesh, is completed in just 28min