Project partner: Volkswagen AG
Project Partner: Fraunhofer IESE
This project addresses the problems of sequential CFD work-flows by introducing online monitoring and computational steering concepts into existing CFD systems. The focus is on application-dependent visualization methods needed to provide sufficient information about ongoing simulations and their quality.
Intuitive Multi-Touch-Interaction provides an effective and efficient support of users so that even complex representations may be individualized and explored in an intuitive and context-specific manner. The central goal is the development of an framework, which allows the application to choose from a variety of multi-touch gestures, while abstracting from the underlying hardware-specific multi-touch framework. MULTI is funded by "Stiftung Rheinland-Pfalz für Innovation".
GeoDict generates and operates on 3D geometric material models and requires the visualization of these models as well as that of computed scalar, vectorial and tensorial fields, such as temperature distributions, flow fields, displacement fields, etc. The objective of this project is to develop a highly time- and memory-efficient pde-solver that is compatible with GeoDicts voxelized structure and solution representation. This project is realized in cooperation with Fraunhofer ITWM and AG Computergrafik and HCI.
The project deals with the augmentation of safety and reliability of technical systems. The methods and technologies being developed are both application-specific and cross-application. The objective of this integration is to transfer methodic knowledge into the application area and to generalize these methods.
Heart vessel diseases, as atherosclerosis are one of the leading cause of death in modern society. Lots of research focusses on the early and fast detection of these diseases. CT scans are a wide used diagnosis method to review the entire heart system. Unfortunately an early detection of heart vessel anomalies on CT scans is often not possible as its resolution is low and contains several image errors. In this project we aim to enhance CT scans in a way that medical researchers are able to detect diseases earlier, more accurate and trustworthy by extracting features from CT scans, simulating blood flow and visually provide our medical coworkers with tools to examine CT scans in a more efficient way. This project is proceeded as a collaboration with the Wright State University, Dayton (OH).
This Project is proceeded as a collaboration with UC Davis (CA) and is part of the IRTG 2057 - "Physical Modeling for Virtual Manufacturing Systems and Processes". In this research project, we propose to develop and evaluate human-centered visualization and interaction techniques that scale both with the level of the transaction to be considered and with the used devices with the expected result of a Human-centered virtual production environment achieved by the development of a highly scalable visualization/interaction framework, focusing on the cross product of visualization, interaction and collaboration.
In recent times, visual analysis has become increasingly important, especially in the area of software measurement, as most of the data from software measurement is multivariate. In this regard, standard software analysis tools are limited by their lack of ability to process huge collections of multidimensional data sets; current tools are designed to either support only well-known metrics, are too complicated to use for generating custom software metrics, or have limited software visualization capabilities. Furthermore, the analyst requires extensive knowledge of the underlying data schemas and the relevant querying language. To address these shortcomings, we propose an interactive visual workflow modeling approach that focuses on visual elements, their configurations, and inter-connectivity rather than a data ontology and querying language. Importantly, in terms of software comprehension we provide a tighter integration between 'software analysis' and 'software visualization' in order to provide an integrated means to specify and visualize software measurements.
In the field of technical textiles (nonwovens, glass wool, ...), fibers and filaments of different materials are produced and processed. Simulations allow trying out modifications to these processes and running experiments at reduced cost, before implementing them in the real world. During the production, the fibers are subject to forces determining their dynamics. In order to simulate such processes, the dynamics of the fibers need to be modeled. One of the challenges for the simulation is to model the interaction of fibers with machine parts. When a collision of a fiber with the machine geometry is detected, its dynamics equation has to be expanded by geometric constraints to prevent penetration. The time step is then recomputed including appropriate contact forces resulting from the constraints. The picture shows the simulation of a filament in air flow for a spunbond line of Oerlikon Neumag, computations performed by Fraunhofer ITWM.
In car manufacturing and prototyping, quality control plays an important role. While this was done mostly by tactile measurements, using robots, there is a supposed paradigm change towards optical measurements via stereoscopic cameras or similar devices. The aim of our project is to efficiently store, evaluate and visualize this new type of measurement data and integrate it into Kronion’s widely used eMMA software suite. Funding by: Zentrales Innovationsprogramm Mittelstand (ZIM). Kooperationsprojekt zwischen Steinbichler Optotechnik GmbH, Hochschule Rhein-Main, Kronion GmbH, Universität Kaiserslautern.
Collaborators: Los Alamos National Laboratories, UC Davis In today's large scale simulations we encounter limitations due to storage capacity or bandwidth. Additionally, data triage becomes more and more difficult, as the data sets grow in size. As a consequence we need to filter out relevant parts of the data. Basing on a recently published quad tree based method, we present a novel approach, where we use a Voronoi tree to store approximately the same amount of relevant data in each cell. As a result, large cells cover less interesting data and small cells contain the most important data, which will allow us efficient data reduction or highlighting.
Numerical simulation of the magnetic field generation of the Earth (the geodynamo), other planets, and the sun, presents an enormous computational and visualization challenges. Simulations of the physical system that generates the magnetic field, the geodynamo, is being developed and carried out on some of the world’s fastest computers, requiring massively parallel computing to carry out long-duration simulations at sufficiently high resolution. The geodynamo represents a major visualization challenge, as the output consists of time-varying vector and scalar fields representing turbulent convection in the Earth’s core and the coupled magnetic field generated by that flow. The number of fields to be studied, the resolution required, and the long-time series makes extraction of features very challenging; moreover, the observation used to compare against simulations is the magnetic field at and above the Earth’s surface far from the computational domain of the simulations. Effective and specialized analysis and visualization systems capable of allowing a geophysicist to truly comprehend a simulated data set in its entirety and derive the relevant, hidden scientific insight are still missing. Our research effort is motivated by this fact, and the members of interdisciplinary constituting this effort jointly specified the design objectives of the magnetic field system presented here. Specifically, the Earth’s magnetic field exhibits highly turbulent behavior in the Earth’s interior, leading to simulated magnetic fields to highly intricate and hard-to-comprehend flow behavior and patterns. Contact: Patrick Rüdiger
With the increasing digitalization of factory and manufacturing planning and engineering the data produced no longer can be summarized in a single snapshot. Having already pursued a lot of effort in analyzing 1d, 2d and lately 3d field data, the challenge now is to deal with multiple outcomes at once and therefore not only incorporating unsteady (time-dependent) data but also correlating the results from multiple sources like simulations, measurements etc. This way the task of bridging the layers from process to manufacturing to factory level is getting to a new level of complexity.
The aim of this project is to provide insights in the correlation of all levels by investigating the needs in analysis and communication of every discipline. Doing so, we set up on state-of-the-art technologies and built a framework that allows the user to seamless integrate his results in one environment.