Benedikt Stehno

In this paper we propose a novel approach to hybrid visual steering of simulation ensembles. A simulation ensemble is a collection of simulation runs of the same simulation model using different sets of control parameters. Complex engineering systems have very large parameter spaces so a naïve sampling can result in prohibitively large simulation ensembles. Interactive steering of simulation ensembles provides the means to select relevant points in a multi-dimensional parameter space (design of experiment). Interactive steering efficiently reduces the number of simulation runs needed by coupling simulation and visualization and allowing a user to request new simulations on the fly. As system complexity grows, a pure interactive solution is not always sufficient. The new approach of hybrid steering combines interactive visual steering with automatic optimization. Hybrid steering allows a domain expert to interactively (in a visualization) select data points in an iterative manner, approximate the values in a continuous region of the simulation space (by regression) and automatically find the “best” points in this continuous region based on the specified constraints and objectives (by optimization). We argue that with the full spectrum of optimization options, the steering process can be improved substantially. We describe an integrated system consisting of a simulation, a visualization, and an optimization component. We also describe typical tasks and propose an interactive analysis workflow for complex engineering systems. We demonstrate our approach on a case study from automotive industry, the optimization of a hydraulic circuit in a high pressure common rail Diesel injection system.

In this paper we propose a novel approach to hybrid visual steering of simulation ensembles. A simulation ensemble is a

collection of simulation runs of the same simulation model using different sets of control parameters. Complex engineering systems have very large parameter spaces so a na ̈ıve sampling can result in prohibitively large simulation ensembles. Interactive steering of simulation ensembles provides the means to select relevant points in a multi-dimensional parameter space (design of experiment). Interactive steering efficiently reduces the number of simulation runs needed by coupling simulation and visualization and allowing a user to request new simulations on the fly. As system complexity grows, a pure interactive solution is not always sufficient. The new approach of hybrid steering combines interactive visual steering with automatic optimization. Hybrid steering allows a domain expert to interactively (in a visualization) select data points in an iterative manner, approximate the values in a continuous region of the simulation space (by regression) and automatically find the “best” points in this continuous region based on the specified constraints and objectives (by optimization). We argue that with the full spectrum of optimization options, the steering process can be improved substantially. We describe an integrated system consisting of a simulation, a visualization, and an optimization component. We also describe typical tasks and propose an interactive analysis workflow for complex engineering systems. We demonstrate our approach

on a case study from automotive industry, the optimization of a hydraulic circuit in a high pressure common rail Diesel injection system.

(978-3-9502533-4-4)

Over the years, many visualization tools and applications were developed for specific fields of science or business. Staying in the alcove of their field, these are highly suited and optimized for visualizing specific data, with the drawback of not being flexible enough to extend or alter these visualizations for other purposes.

Often, customers of such visualization packages cannot extend them, to fit their needs, especially if the software is closed source. But even using open source software does not solve this problem efficiently, since to extend the software, costumers need to have programming skills and are often forced to reimplement algorithms or visualizations which already exist, making rapid development impossible.

The goal of this thesis is to develop a dataflow visual programming language (DFVPL) and a visual editor for the rapid development of visualizations. With this programming language, called OpenInsightExplorer, users can develop visualizations by connecting graphical representations of modules rather than writing source code. Each module represents a part of a visualization program. Modules are designed to function as an independent black box and they start to operate as soon as data is sent to them. This black box design and execution model allows to reuse modules more frequently and simplifies their development.

This programming language and its editor run platform independently to reach a high number of potential programmers, respectively users, to develop visualizations, since they are not bound to a specific platform. It is extendable, by means of self developed modules and data types to extend the language. Programming and editing a visualization is easy and fast, even for people with only little programming experience. The production cycle of the development of visualizations is reduced to a minimum. This is achieved by reusing and combining existing modules.

The usability of the programming language was evaluated by implementing two example visualizations with it. Each example originates from different areas of visualization, therefore demanding different data types, data transformation tasks and rendering.