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The structural integrity of Offshore Wind Turbines (OWT) is of prime significance, due to the significant dynamic stresses generated by the extreme blast and impact loads. The detrimental damage of such loads emanates from either large inelastic localized deformations or the global rotations which to the collapse of the wind turbine over the ship. In some circumstances, however, the combination of the two is imminent. In this work, we examine two scenarios of impact and blast phenomena on offshore wind turbines. In the first, the influence of gravity loads, wind velocity, vessel angle of attack, and its initial momentum, on the localized and global deformations is discussed. A numerical FE model is developed to further investigate the damage of the OWT struck by a commercial ship. In the second scenario, we develop a mathematical model to capture the blast response of cylindrical shells utilized in the OWT. By decomposing the load into a spatial part with constant magnitude and a temporal part characterized by a piecewise function, the analytical solution is sought for two distinguishable phases. The validation of the analytical model with the FE one shows the capability of the former to capture the dynamic plastic collapse of the shell with a good degree of accuracy.

In this extended study, electrostatic precipitation, a cornerstone technology in industrial coating applications, is examined with enhanced depth and breadth, targeting its applicability in coating standard automotive rims. Utilizing an advanced Eulerian- Lagrangian, Extended Discrete Element Method, Finite Volume solver constructed within the OpenFOAM CFD-framework, we present a holistic computational model that incorporates various facets such as airflow dynamics, coating-particle interactions, and intricate particle-substrate phenomena like blow-off and corona formation. Enabled by Massive Simultaneous Cloud Computing technology, our solver permits concurrent exploration of a wide array of industrially relevant conditions. This research goes beyond earlier studies by encompassing not only variations in "Mean Powder Particle Diameters" and "Powder Particle Density," but also conducting a more expansive simulation sweep that incorporates changes in "Particle Diameter Deviation" and "Applied Voltage." This allows for a nuanced understanding of sensitivities and uncertainties linked to these parameters. We apply this comprehensive modeling approach to scrutinize single-burst powder coating on a typical metallic, automotive rim substrate. The study delivers intricate predictions and visualizations of coating patterns, efficiencies, and homogeneity across a range of conditions. Our findings offer valuable insights for optimizing powder properties, which hold considerable implications for material suppliers in the coating industry. Despite these advances, certain limitations remain, underscoring the need for further research in this vital domain.

Many industrial applications involve particles transported by a carrier fluid flow with additional multi-physical effects such as electromagnetics. The simulation of such processes is computationally expensive especially because of the diverse dimensional and time scales involved. In this study, the time scale for the fluid flow to be stably simulated is shown to be up to 2 orders of magnitude higher than the time scale for a spherical particle to assume carrier flow velocity. A semi-transient solution methodology has been devised, utilizing a dual time stepping approach for the flow and particle simulations. In this methodology, first the flow field is simulated with the larger time step, saving the resultant fields at regular intervals serving as snap shots of the flow. Then between each snap shot, the flow is treated as steady state, facilitating the calculation of the particle trajectory based on the resultant forces. This approach is especially suitable for applications where the particle cloud density is low enough not to have a significant effect on the flow field warranting a one way coupling. The accuracy of the method is established by comparing key performance parameters such as coating transfer efficiency and the homogeneity of the coating obtained from a fully transient simulation. The saving potential in terms of computational resources is also quantified

Peristaltic pumps play an indispensable role in transporting sensitive fluids, especially at low flow rates down to the milliliter per hour range. Considering complex and variable tube setups including various fluid dynamically active components, the calibration of these pumps poses significant challenges due to the intricate contributions of the components to pressure loss. This study addresses the challenge posed by the potentially innumerable setup combinations that could emerge in practical applications. Recognizing the impracticality of empirical calibration measurements for each possible setup and process condition, our team pursued the development and validation of a numerical modelling approach. Given the importance of efficiency in practical applications, we diligently assessed the computational demand and efficiency of the numerical strategy. Initially, a 3D fluid-structure interaction (FSI) approach for multiphysics modelling was adopted. However, recognizing the excessive complexity and computational load, the strategy was streamlined to a more simplified yet effective methodology, relying on geometric displacement of the liquid due to the tube deformation within the peristalsis of the pump. Besides the viscoelastic behavior of the tube material, the characterization of the pressure loss behavior of each individual fluid dynamic component within the tube system was crucial. We undertook a series of rigorous fluid dynamic experiments to conduct these characteristics. The findings from these experiments were subsequently integrated into the solver development phase. The final solver showcased reliable performance. We validated its efficacy through a series of tests, and it demonstrated consistent and accurate predictions. We are pleased to announce that this novel solver offers an innovative approach for calibrating peristaltic pump systems encompassing complex setups and varying process conditions.

Solid oxide fuel cell (SOFC) technology is a promising solution for the on-demand supply of electrical energy using synthetic gas or biogas (or natural gas) as input. To significantly improve on the unavoidable degradation of state-of-the-art anodes like Ni-YSZ, we elaborate on fully ceramic composite electrodes, which are based on mixed ionic and electronic conductors (MIEC) like doped ceria and perovskite (e.g., titanate) materials. To accelerate the development of these novel electrodes, a Digital Materials Design (DMD) framework for the systematic and model-based optimization of MIEC SOFC-electrodes is elaborated. In our DMD approach we combine stochastic microstructure modeling, virtual testing of 3D microstructures and a multiscale-multiphysics electrode model to explore the available design space by performing parametric studies. The multiphysics electrode model is thereby used to predict the impact of a virtual microstructure variation on the electrode performance. The model captures all the relevant physico-chemical processes involved like the transport of charge carriers in the two MIEC solid phases, transport of the gas species in the pore-phase (described by the dusty-gas model) and the reaction kinetics (calibrated to the experimental performance characterization of the cells). A special emphasize is laid to the appropriate description of the microstructure effects. Thereby, a 1D FEM continuum model implemented in Comsol Multiphysics is used to describe the electrode on a button cell level. The microstructure effects are then captured using effective transport and interface properties, determined from 3D microstructure data. The model results are validated on experimental performance characterizations of the button cells (e.g., EIS results). This model-based performance prediction enables to establish the relationship between materials choices and compositions, fabrication parameters, microstructure properties and cell-performance. This approach is thus

In contemporary control engineering, the nuanced amalgamation of multiphysics models into efficacious control frameworks remains a sophisticated challenge. This research endeavor was orchestrated to devise methodologies that adeptly transpose the stationary solutions inherent in 1D and 2D multiphysics models into dynamic control system models, with a specific inclination towards platforms such as Simulink. Concurrently, we embarked on the formulation of multiphysics models designed to seamlessly dovetail with advanced observer systems. These models allow to continuously estimate system states that enables the control engineer to increase reaction time based on model prediction. A paradigmatic and quintessential project will be elucidated during the presentation, serving as a testament to the enhanced capabilities engendered by this novel integration of multiphysics-modelling within control algorithm schematics.

This research presents Computational Fluid Dynamics (CFD) simulations in ANSYS® illustrating emissions of to the air. The CFD simulations is employed to study plume transport in urban environment, i.e., Breivika port in the city of Tromsø. The case study presents a two-phase model considering specific wind strength and direction in the city of Tromsø. Geographical coordinates, temperature, and wind data were obtained from the open sources, such as Google Maps, and Norwegian Meteorological Institute. The results from the simulations indicates a potential outcome with respect to various weather conditions. It was revealed for vessels less than 30 meter chimney height, the higher the wind strength, the lower the plume dispersion, causing the plume to stay closer to the terrain. This brings in a concentrated amount of pollutants closer to the public areas. The terrain in the model is recognizable for the Tromsø port’s location. From the CFD results, it is illustrated that onshore wind with high wind strength could affect the environment. The results simulated in OpenFOAM are qualitatively showing the same as visible in ANSYS®.

Zugehörige Dateien: https://zenodo.org/records/7744110 https://doi.org/10.1039/D3YA00132F https://doi.org/10.21256/zhaw-28430, Digital Materials Design (DMD) offers new possibilities for data-driven microstructure optimization of solid oxide cells (SOC). Despite the progress in 3D-imaging, experimental microstructure investigations are typically limited to only a few tomography analyses. In this publication, a DMD workflow is presented for extensive virtual microstructure variation, which is based on a limited number of real tomography analyses. Real 3D microstructures, which are captured with FIB-tomography from LSTN-CGO anodes, are used as a basis for stochastic modeling. Thereby, digital twins are constructed for each of the three real microstructures. The virtual structure generation is based on the pluri-Gaussian method (PGM). In order to match the properties of selected virtual microstructures (i.e., digital twins) with real structures, the construction parameters for the PGM-model are determined by interpolation of a database of virtual structures. Moreover, the relative conductivities of the phases are optimized with morphological operations. The digital twins are then used as anchor points for virtual microstructure variation of LSTN-CGO anodes, covering a wide range of compositions and porosities. All relevant microstructure properties are determined using our standardized and automated microstructure characterization procedure, which was recently published. The microstructure properties can then e.g., be used as input for a multiphysics electrode model to predict the corresponding anode performances. This set of microstructure properties with corresponding performances is then the basis to provide design guidelines for improved electrodes. The PGM-based structure generation is available as a new Python app for the GeoDict software package.

Ion-flow-stimulated roughening transition is a phenomenon that may prove useful in the hierarchical structuring of nanostructures. In this work, we have investigated theoretically and experimentally the surface texturing of single-crystal and multi-crystalline silicon wafers irradiated using ion-beam flows. In contrast to previous studies, ions had relatively low energies, whereas flow densities were high enough to induce a quasi-liquid state in the upper silicon layers. The resulting surface modifications reduced the wafer light reflectance to values characteristic of black silicon, widely used in solar energetics. Features of nanostructures on different faces of silicon single crystals were studied numerically based on the mesoscopic Monte Carlo model. We established that the formation of nano-pyramids, ridges, and twisting dune-like structures is due to the stimulated roughening transition effect. The aforementioned variety of modified surface morphologies arises due to the fact that the effects of stimulated surface diffusion of atoms and re-deposition of free atoms on the wafer surface from the near-surface region are manifested to different degrees on different Si faces. It is these two factors that determine the selection of the allowable "trajectories" (evolution paths) of the thermodynamic system along which its Helmholtz free energy, F, decreases, concomitant with an increase in the surface area of the wafer and the corresponding changes in its internal energy, U (dU>0), and entropy, S (dS>0), so that dF=dU - TdS<0, where T is the absolute temperature. The basic theoretical concepts developed were confirmed in experimental studies, the results of which showed that our method could produce, abundantly, black silicon wafers in an environmentally friendly manner compared to traditional chemical etching.

Peristaltic pump technology is widely used wherever relatively low, highly accurately dosed volumetric flow rates are required and where fluid contamination must be excluded. Thus, typical fields of application include food, pharmaceuticals, medical technology, and analytics. In certain cases, when applied in conjunction with polymer-based tubing material, supplied peristaltic flow rates are reported to be significantly lower than the expected set flow rates. Said flow rate reductions are related to (i) the chosen tube material, (ii) tube material fatigue effects, and (iii) the applied pump frequency. This work presents a fast, dynamic, multiphysics, 1D peristaltic pump solver, which is demonstrated to capture all qualitatively relevant effects in terms of peristaltic flow rate reduction within linear peristaltic pumps. The numerical solver encompasses laminar fluid dynamics, geometric restrictions provided by peristaltic pump operation, as well as viscoelastic tube material properties and tube material fatigue effects. A variety of validation experiments were conducted within this work. The experiments point to the high degree of quantitative accuracy of the novel software and qualify it as the basis for elaborating an a priori drive correction.

Multiphysics simulation software has been developed to predict the key performance attributes of industrial powder coating applications based on applied process-parameter settings. The software is a Eulerian-Lagrangian finite-volume Multiphysics solver based on OpenFOAM, capable of modelling mass transfer effects between powder-coating pistols and electrically grounded metallic substrates. It considers various factors such as fluid dynamics of process airflow, coating-particle dynamics, particle-substrate interactions, and particle charging mechanisms within the corona. The software is fully compatible with Massive Simultaneous Cloud Computing technology, allowing hundreds of simulated coating scenarios to be computed simultaneously. Experimental validation efforts have been conducted, indicating a high degree of practical relevance of the technology. The current simulation study aims to demonstrate the potential of the simulation software for adjusting coating lines and optimizing powder coating of U-profiles. Specifically, the study focuses on optimizing the key-performance-attributes of the powder coating application with respect to varying material parameters of the applied powder, namely mean particle diameter, standard deviation of Gaussian particle size distribution, and powder particle density. The software predicts and visualizes coating patterns, coating efficiencies, and the batch-based standard deviation of coating thickness on a U-shaped metallic substrate, resulting in concrete and optimized powder settings. The presented results and the applied software are highly relevant for powder material suppliers.

Peristaltic pump technology is widely used wherever relatively low, highly-accurately dosed volumetric flow rates are required and where fluid contamination must be excluded. Thus, typical fields of application include food, pharmaceutical, medical technology and analytics. In certain cases, when applied in conjunction with polymer-based tubing material, supplied peristaltic flow rates are reported to deviate from expected set flow rates relevantly. Said deviations are related to i) chosen tube material, ii) total over all time of the pump cycles exerted on applied polymer tubes and iii) applied frequency of pump revolutions. This work presents a fast, dynamic, 2D Multiphysics simulation method, which significantly reduces the considered deviations by serving as the basis for elaborating an a priori operational setpoint correction. The numerical solver encompasses laminar fluid dynamics, geometric restrictions provided by peristaltic pump operation, as well as viscoelastic tube material properties and tube material fatigue effects. Within this work, a considerable variety of validation experiments and application examples of the novel methodology will be presented.

Performance and durability of solid oxide cell (SOC) electrodes are closely linked to their microstructure properties. Thus, the comprehensive characterization of the 3D microstructures e.g., obtained by FIB-SEM tomography is essential for SOC electrode optimization. Recent advances and trends call for a standardized and automated microstructure characterization. Advances in FIB-SEM tomography enable the acquisition of more samples. In order to make reasonable comparisons of 3D microstructures from different sources and to make reliable statistical analyses, these structures need to be analyzed with standardized 3D image processing tools. In addition, the emerging methods for Digital Materials Design (DMD) enable to create numerous virtual but realistic microstructure variations using stochastic microstructure modeling. For such a DMD workflow, many virtual microstructures need to be characterized. Thus, the availability of a standardized, efficient and automated microstructure characterization tool is a crucial prerequisite for the data-driven optimization of energy materials. This dataset provides a standardized microstructure characterization tool for SOC electrodes, which is implemented as a Python app for the GeoDict software-package. A large number of microstructure characteristics can be determined with this app, which are relevant for the performance of conventional electrodes like Ni-YSZ and for more recent MIEC-based electrodes like Ni-CGO or titanate-CGO anodes. This dataset consists of the following files: - The SOC characterization app is described in detail in the file “1_Read_Me_SOC_Characterization_App.pdf” including a description of three examples (testcases with 3D structures). - The scripts for the characterization-app are provided in the folder “2_SOC_Characterization_App”. - Three characterization examples are provided in the folder “3_Characterization_Examples”.

Performance and durability of solid oxide cell (SOC) electrodes are closely linked to their microstructure properties. Thus, the comprehensive characterization of 3D microstructures e.g., obtained by FIB-SEM tomography is essential for SOC electrode optimization. Recent advances and trends call for a standardized and automated microstructure characterization. Advances in FIB-SEM tomography enable the acquisition of more samples, which are also more frequently shared within the research community due to evolving open science concepts. In addition, the emerging methods for Digital Materials Design (DMD) enable to create numerous virtual but realistic microstructure variations using stochastic microstructure modeling. In this publication, a standardized microstructure characterization tool for SOC electrodes is presented, which is implemented as a Python app for the GeoDict software-package. A large number of microstructure characteristics can be determined with this app, which are relevant for the performance of conventional electrodes like Ni-YSZ and for more recent MIEC-based electrodes. The long list of 3D characteristics that can be determined selectively includes morphological characteristics, interface properties and effective transport properties deduced from morphological predictions and from numerical simulations. The extensive possibilities of the standardized microstructure characterization tool are illustrated for a dataset of three LSTN-CGO anode microstructures reconstructed with FIB-SEM tomography and for a dataset of three virtual sphere-packing structures. The automated microstructure characterization is a key element to exploit the full potential of open science, Digital Materials Design (DMD) and artificial intelligence (AI) for the data-driven optimization of SOC electrodes by providing standardized high quality microstructure property data.

Digital Materials Design (DMD) offers new possibilities for data-driven microstructure optimization of solid oxide cells (SOC). Despite the progress in imaging technology, 3D-imaging still represents a bottleneck for the application of DMD. Experimental microstructure variation studies are typically limited to a few 3D datasets from tomography. In contrast, stochastic microstructure modeling allows to explore a much larger design space by performing parametric studies. Therefore, the availability of an appropriate virtual structure generator is a crucial prerequisite for realistic design studies. The stochastic microstructure modeling based on the pluri-Gaussian method (PGM) has proven to be well-suited for the virtual reconstruction of SOC electrodes. This dataset provides a Python app for the stochastic microstructure modeling of SOC electrodes in GeoDict based on a pluri-Gaussian method (PGM). The PGM-app allows for an efficient construction of virtual but realistic SOC microstructures consisting of three phases (two solid-phases and one pore-phase). This dataset consists of the following files: 1. The PGM-app is decribed in detail in the file "01_Read_Me_PGM_SOC_App_Zenodo.pdf". 2. The script of the PGM-app is provided in the file "02_PGM_SOC_App.zip".

In industrial processes, powder coating is widely utilized to attain functional or aesthetic surface properties on manufactured parts. A Eulerian-Lagrangian Multiphysics solver has been developed within the OpenFOAM framework in order to simulate and optimize such processes with regards to coating efficiency and homogeneity. In the scope of this study, the powder particle-substrate and particle-particle interactions that occur on the surface of a substrate during the coating process are investigated. This is instigated by the observation that some particles glide over the substrate, rather than sticking to the substrate upon first contact. The phenomenon is governed by the balance of pressure, fluid shear stress traction, electrostatic particle-particle repulsion and gravity forces on the substrate. On the basis of experimental data previously gathered, it is demonstrated that the surface interactions are essential to predicting the coating outcome accurately enough, such as to serve as basis for later process-optimization steps. Furthermore, a dimensional analysis illustrates the weight of the individual force contributions on the overall force balance.

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

Speaker’s Bio: Gernot Boiger is a Professor of ‘Modelling Multiphysics Applications’ at the ZHAW Zurich University of Applied Sciences, Switzerland and head of the research group ‘Multiphysics Modelling and Imaging’ at the ICP Institute of Computational Physics. As such he pursues strong research-driven relationships with academia and industry in the field of simulation-based product- and process development. He concluded his PhD in ‘Simulation Technology and Process Engineering – Eulerian-Lagrangian modelling of nonspherical particle flows in the context of automotive filtration’ at the University of Leoben, Austria. He has been awarded a ‘ProScientia scholarship’ as well as the ‘Rektor Platzer Ring of Excellence’ of the University of Leoben. He holds the post of ‘Vice President Europe’ of the International Society of Multiphysics., Creating qualitative Multiphysics models of technical and natural processes is a demanding yet fascinating task. It requires careful observation, thorough analysis and the ability to break up complex phenomena into simpler sub-problems. However as scientists work ever more closely together with industry, one basic truth becomes clearer and clearer: Qualitative modelling is often just the very first step on a ‘long, hard journey’ from grasping the mere physical principles of any technological problem towards creating a fully validated, comprehensive simulation approach. One that can not just replicate, but reliably predict, one that can actually be applied in ‘real-life’, one that stands up to the task of seamless integration into cost- and time- driven, competitive industrial development efforts. This Keynote-talk will focus on one representative example of such a ‘long, hard journey’. It will focus on ‘Modelling, simulating and predicting particle cloud behaviour in the context of complex, industrial surface treatment applications’. This concrete case shall demonstrate just how time-intensive and demanding the path from creating a Multiphysics simulation model that ‘just looks about right’, towards ‘full validation’ and ‘actual industrial applicability’ can really be. The talk will span a journey form first qualitative simulation models, via thorough step-by-step validation, towards the creation of a novel, cloud-powered simulation software, capable of computing 100s of simultaneous process scenarios and ready for industrial application. We will cover a span of seven years of development-effort and approximately 16’000 manhours of work invested. Thus the talk will hopefully show that for us Multiphysics enthusiasts and experts, persistence, obsessive love for detail, accuracy and diligence are just as important as creativity and knowledge. And finally there is one essential realisation: we can never ‘know’ the actual truth, all we can ever do is ‘to model it’.

References: [1] Gernot Kurt Boiger, Darren Sharman, Bercan Siyahhan, Viktor Lienhard, Marlon Boldrini, and Dominic Drew. A massive simultaneous cloud computing platform for openfoam. In 9th OpenFOAM Conference, online, 19-20 October 2021. ZHAW Zürcher Hochschule für Angewandte Wissenschaften, 2021.2, The field of Computational Science is facing an increasing demand for data-intensive research. Engineering tasks such as parameter-, sensitivity- and optimisation studies require ensemble computing to an ever-increasing extent. At the same time, the field of artificial intelligence (AI) is pushing for ever more extensive, numerically derived learning-, testing- and validation data. With the cloud software KaleidoSim, we can run hundreds of numerical simulations simultaneously [1] and generate large amounts of data in a short time. Whilst KaleidoSim supports various simulation tools, only OpenFOAM is used in this study. In this work, we have developed tools and routines to accelerate, simplify and automate studies with hundreds of simultaneous simulations in the cloud. We performed a full 360° aerodynamics analysis of different aircraft to test our toolbox. The study included 420 OpenFOAM simulation cases. Each case was a steady-state, Reynolds Average Stress (RAS) turbulence model-based, single-phase flow simulation on a 1.5 million cell hexahedral finite volume grid. Drag and lift coefficients were calculated for each case. For the toolbox development, we used a combination of Python and KaleidoSim Application Programming Interface (API) routines. The Python-based graphical user interface (GUI) allows switching between different CAD models to compare multiple aircraft. The GUI also allows mesh sensitivity analysis to determine optimised meshes for each aerodynamic shape. Based on this, we performed a series of mesh sensitivity analyses using snappyHexMesh and CfMesh meshes. This work has shown that a combination of cloud computing via KaleidoSim-based API routines and Python scripting can speed up certain parameter study workflows by a factor of 50-100. Specifically, the exemplary representative semi-automated workflow of the 420-case aerodynamic study could be performed and post-processed in less than 45 minutes, whereas a comparable workflow had previously taken

In industrial applications involving particle-laden flows such as powder coating, often ad-hoc heuristic methods are employed to design or adjust processes in light of varying process-parameter settings. This labor-intensive approach often leads to inefficiencies and sub-optimal outcomes, as the entirety of the design space is not taken into consideration. The alternative, much more efficient approach entails knowledge-based process adjustments. However, the latter is often beyond the capabilities of industrial appliers, since it requires an extensive expertise and highly skilled labor force. The numerical Multiphysics-simulation software coatSim has been designed in order to bridge the gap between the required expertise and the need for a predictive tool for industrial knowledge-based process design. coatSim is a user friendly, cloud-based simulation software that can be customized for any industrial application involving flows laden with solid or liquid particles or indeed ionic transport mechanisms. Its core technology relies on an OpenFOAM based, Eulerian-Lagrangian solver, where particle flow-paths, particle-particle and particle-substrate interaction effects can be modeled in light of acting fluid dynamic-, and body forces as well as particle dynamic effects. In the case of powder coating applications, the body forces are the electrostatic forces generated by the coating pistol and gravity. The solver has been validated on the basis of an experimental campaign, where substrate plates were coated in 72 configurations. Thereby measured relative coated volume could be predicted up to a variation of 5%. The underlying solver can handle domains comprised of several independent mobile regions, on each of which a periodic motion in terms of any arbitrary Fourier series can be imposed. This enables realistic duplication of industrial applications, as motion is an intrinsic part of most of them. The core solver technology has been bundled with an intuitive user interfa

References: [1] Gernot Kurt Boiger, Darren Sharman, Bercan Siyahhan, Viktor Lienhard, Marlon Boldrini, and Dominic Drew. A massive simultaneous cloud computing platform for openfoam. In 9th OpenFOAM Conference, online, 19-20 October 2021. ZHAW Zürcher Hochschule für Angewandte Wissenschaften, 2021.2, The field of Computational Science is facing an increasing demand for data-intensive investigations. Engineering tasks such as parameter-, sensitivity- and optimisation studies need ensemble computing to an ever-increasing extent. At the same time, the field of artificial intelligence (AI) is pushing for ever more extensive, numerically derived learning-, testing- ,and validation data. With the cloud software KaleidoSim, we can run hundreds of numerical simulations simultaneously [1] and generate large amounts of data in a short time. Although KaleidoSim supports various simulation tools, this study uses only OpenFOAM. In this work, we developed tools and routines to speed up, simplify and automate studies containing hundreds of simultaneous simulations in the cloud. To test our toolbox, we conducted a complete 360° aerodynamic analysis of various airborne vehicles. The study included 420 OpenFOAM simulation cases. Each case was a steady-state, Reynolds Average Stress (RAS) turbulence model-based, single-phase flow simulation on a 1.5 million cell hexahedral finite volume grid. Drag and lift coefficients were calculated for each case.We used a combination of Python and KaleidoSim Application Programming Interface (API) routines to develop the toolbox. The Python based graphical user interface (GUI) allows switching between different CAD models so that multiple aircraft can be compared. The GUI also enables mesh sensitivity analysis to identify optimised meshes for each aerodynamic shape. Based on this, we performed a series of mesh sensitivity analyses using snappyHexMesh and CfMesh grids. This work proved that a combination of cloud computing via KaleidoSim-based API routines and Python scripting can speed up certain parameter study workflows by a factor of 50-100. Specifically, the exemplary, representative, semi-automated workflow of the aerodynamic study with 420 cases could be performed and post-processed in under 45 minutes, whereas a comparable workflow had b

YouTube, The 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.

YouTube, Using 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.

NAFEMS World Congress 2021 Keynote, A unified approach on teaching and modelling 1D dynamic Multiphysics systems has been investigated, re-organised, elaborated and applied. While the presented method is of the system-dynamic kind, a 1:1 equivalence to the tetrahedron-of-state-concept within the well-known bond graph modelling technique can be shown. Gibbs fundamental equation is an essential theme within the demonstrated approach. It constitutes the basis and justification for modelling any Multiphysics system. The method involves a thorough distinction between capacitive- and inductive elements. Furthermore it proposes graphical tokens to represent relevant phenomena. Among the central modelling elements are containers for conservative quantities, capacities, inductivities, potentials, equations of state as well as fluxes of information and physical quantities. While being most practical and efficient for zero- or one-dimensional problems, there is no theoretical limitation towards modelling two or three- dimensional scenarios. Thus the presented concept enables fast instructive representation of linear- and angular-, mechanical-, electrodynamic-, fluiddynamic- and, with limitations concerning inductive processes, also the representation of thermodynamic- as well as thermochemical systems. In this context, selected examples of first order capacitive-inductive, mechanical-, fluiddynamic- and electrodynamic systems will be shown in order to demonstrate the applicability, efficiency as well as validity of the modelling concept in discussion.

YouTube, This is a tutorial video in Prof. Gernot Boiger's series on OpenFOAM and the cloud computing platform Kaleidosim. Here the focus lies on the 'EVAL' functionality within OpenFOAM v1912 (and later). Using the 'EVAL' function the user can prescribe (dynamic) boundary conditions from inside any OpenFOAM dict, based on a multitude of mathematical expressions. In this movie the 'EVAL' function is demonstrated based on an example surrounding the classical simpleFoam Motorbike tutorial case with a 'twist'. The 'twist' is about conducting a full #aerodynamic #CFD study of the #Motorbike frame, involving 360 individual simpleFoam #simulation runs simultaneously conducted within Kaleidosim #cloud platform. Thereby each run differs in that onset-airflow velocity vector as well as the entire bounding box of the simulation vicinity (wind channel) are rotated degree after degree (step size 1°) around the Motorbike, yielding 360 simulation cases. The 'EVAL' function performs the math to rotate the bounding box based on a 2D rotation matrix with respect to the angle-of-attack, while Kaleidosim's Kaleidoscope Feature conducts a parameter-study varying the angle-of-attack step-by-step. Furthermore the video contains a demonstration of how, thanks to Kaleidosim's MSCC Massive Simultaneous Cloud Computing capacity, the entire study can be completed in no more than 25min. Find links to related tutorial videos here: 1.) OpenFoam & Kaleidosim: Creating Python Macro for Paraview based Post-Processing in the Cloud https://youtu.be/EzmkmrDnu0Y 2.) OpenFOAM & Kaleidosim: Introducing the Kaleidoscope Feature https://youtu.be/6T08Li7gVqE 3.) Live Demo of Speeding up OpenFoam Parameter Study by Factor 50 https://youtu.be/rWe4KfFDPKs 4.) Compile and Run Custom OpenFoam Solvers in the Cloud using Kaleidosim https://youtu.be/3QoQAlaCQxk

YouTube, This is the second video (see first one here: https://youtu.be/6T08Li7gVqE ) about introducing the Kaleidoscope Feature within the Kaleidosim cloud platform: Prof. G. Boiger of ZHAW/ICP uses OpenFOAM's Motorbike tutorial case in simpleFoam mode to demonstrate. This time a very large computational parameter study involving the RAS turbulence model parameters 'turbulent kinetic energy' and 'omega', is conducted based on just one single base case. The base case is uploaded to the cloud and multiplied within seconds to yield not less than 300 individual OpenFOAM simulation cases. All cases are then computed simultaneously in the cloud (MSCC Massive Simultaneous Cloud Computing) and the entire study is completed in only about 25min. Regarding post processing: Paraview screenshots are 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 (for 'how to' see also: https://youtu.be/EzmkmrDnu0Y ). Finally (visualised) results can be selectively downloaded from the cloud using the Katana File Downloader function.

YouTube, In this video Prof. G. Boiger of ICP Institute of Computational Physics ZHAW Zurich University of Applied Sciences shows how to compile and run custom OpenFOAM solvers in the cloud using Kaleidosim. This feature was inspired by colleagues from our aR&D team here at ICP/ZHAW like Bercan Siyahhan, Marlon Boldrini and Viktor Lienhard as well as by customers, contributors and friends of Kaleidosim Technologies AG like: Dr. Christian Witz of TU-Graz (see: christian.witz@tu-graz.at) or Dr. József Nagy to whom special thanks go out. How it works: 1.) Copy & Paste any folder of any self-composed OpenFoam application into the OpenFoam case folder you wish to upload and run in Kaleidosim #cloud platform. 2.) Zip case folder containing case set-up & source code of self-composed OpenFoam application(s). 3.) Set up an OpenFoam project within Kaleidosim cloud platform & start case creation wizard (if unsure how: press 'Show Tutorial' button top-right at Kaleidosim - Dashboard). 4.) In Kaleidosim case-creation-wizard 'Step-2 Choose OpenFoam Solver': choose any standard OpenFoam solver from the list; Even if you do not intend to use it since you are after compiling & running your own application(s). 5.) In Kaleidosim case-creation-wizard 'Step-4 Additional Parameters': enter a bash-script which will step-by-step do the following: 5.1.) Change from case-directory into directory of Library1 (if present), clean links/environment variables using wclean and compile by using wmake libso. Like so: cd ./Library1 wclean wmake libso 5.2) Change back to case-directory, change into directory of SolverA (or any application, if present), clean links/environment variables using wclean and compile by using wmake. Like so: cd .. cd ./SolverA wclean wmake 5.3) Change back to case-directory, and run case using your freshly compiled application SolverA on any number of cores Num_Cores in accordance with the hardware you chose previously in 'Step-3 Computing resources'. Like so: cd .. decomposePar mpir

YouTube, In this tutorial video Prof. G. Boiger of ZHAW_ICP demonstrates how to record and modify a Python script such that it can be used for automated Paraview - based post-processing using Kaleidosim cloud software. The demo-case is an OpenFOAM run using simpleFoam solver on the classic 'Motorbike' tutorial case. Activating Paraview's 'Python Trace' function in order to track the workflow, simulation results are visualised and screen shots are stored. After recording, the Python utility is stored and then manually modified such as to allow it to be run more generically... e.g. within Kaleidosim's cloud-based virtual machines. Here are links to other, related tutorial videos to which the speaker refers: OpenFOAM & Kaleidosim: Introducing the Kaleidoscope Feature https://youtu.be/6T08Li7gVqE Live Demo of Speeding up OpenFoam Parameter Study by Factor 50 https://youtu.be/rWe4KfFDPKs

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 investi

In this study, an existing static Eulerian-Lagrangian solver has been updated to incorporate both rotational and translational motion, which can be defined in terms of an arbitrary Fourier series function. This development is paramount to simulate real life powder coating applications. Thereby particles are transported by airflow through mobile coating pistols that contain an electrode, which charges them electrically. Upon discharge, the particles flow within a chamber until impact onto a grounded substrate, which they subsequently coat. Even though this process has a wide application area in the industry, there is a lack of simulation-based tools specifically designed for it. Hence the industry relies almost exclusively on ad hoc, heuristic methods in their process design. In the scope of this project, a software is developed which bundles together; an automated meshing procedure for typical coating configurations utilizing cfMesh, a dynamic Eulerian-Lagrangian Multiphysics OpenFOAM version 7 solver and post processing scripts to assess the effectiveness of a coating process.

We studied the striking effect of external irradiation of nanowires on the dynamics of their surface morphology at elevated temperatures that do not destroy their crystal lattice. Numerical experiments performed on the basis of the Monte Carlo model revealed new possibilities for controlled periodic modulation of the cross-section of quasi-one-dimensional nanostructures for opto- and nanoelectronic elements. These are related to the fact that external irradiation stimulates the surface diffusion of atoms. On the one hand, such stimulation should accelerate the development of the well-known spontaneous thermal instability of nanowires (Rayleigh instability), which leads to their disintegration into nanoclusters. On the other hand, this leads to the forced development of the well-known roughening transition (RT) effect. Under normal circumstances, this manifests itself on selected crystal faces at a temperature above the critical one. The artificial stimulation of this effect on the lateral surface of quasi-one-dimensional structures determines many unpredictable scenarios of their surface dynamics, which essentially depend on the orientation of the nanowire axis relative to its internal crystal structure. In particular, the long-wave Rayleigh breakup observed in absence of external irradiation transforms into strongly pronounced short-wave metastable modulations of the cross-section (a chain of unduloids). The effect of the self-consistent relationship between the Rayleigh instability and RT is dimensional and can be observed only at relatively small nanowire radii. The fact is analyzed that, for the manifestation of this effect, it is very important to prevent significant heating of the nanowire when surface diffusion is stimulated. A number of developed theoretical concepts have already found confirmation in real experiments with Au and Ag nanowires irradiated by electrons and Ag+ ions, respectively.

Localised pressure pulse loads can pose a significant threat to structural elements and critical equipment and may cause failure and damage in the target due to the concentrated energy delivered upon a localised area of the target. The impulse impinged upon the local area at the contact interface can exceed 80% of the total impulse that the charge can deliver upon the infinite target, leading to potential perforation of the structural element. When multiple charges are detonated, the advection of gaseous products depends, among other parameters such as fluid density, on the type of blast wave interference and superposition. This work examines the influence of multiple charge detonations blasted in the air on the external surface of cylindrical shells. Two types of detonations were considered, viz. simultaneous and sequential. In both cases the charges were positioned at 50mm and 75mm stand-off to the right and left of the shell. The Fluid-Structure Interaction (FSI) phenomenon was investigated in each scenario. The pressure registered with the gauge points of the rigid target was implemented in an uncoupled study on a flexible target which demonstrated different mode shapes occurring in the shell due to each blast scenario. A dimensionless impulse parameter was defined based on the Gaussian distribution function associated with the load shape, which renders the probability of the impulse as the total impulse that can potentially be imparted to the target.