Interactive Visual Steering - Rapid Visual Prototyping of a Common Rail Injection System

Abstract

Interactive steering with visualization has been a common goal of the visualization research community for twenty years, but it is rarely ever realized in practice. In this paper we describe a successful realization of a tightly coupled steering loop, integrating new simulation technology and interactive visual analysis in a prototyping environment for automotive industry system design. Due to increasing pressure on car manufacturers to meet new emission regulations, to improve efficiency, and to reduce noise, both simulation and visualization are pushed to their limits. Automotive system components, such as the powertrain system or the injection system have an increasing number of parameters, and new design approaches are required. It is no longer possible to optimize such a system solely based on experience or forward optimization. By coupling interactive visualization with the simulation back-end (computational steering), it is now possible to quickly prototype a new system, starting from a non-optimized initial prototype and the corresponding simulation model. The prototyping continues through the refinement of the simulation model, of the simulation parameters and through trial-and-error attempts to an optimized solution. The ability to early see the first results from a multidimensional simulation space--thousands of simulations are run for a multidimensional variety of input parameters--and to quickly go back into the simulation and request more runs in particular parameter regions of interest significantly improves the prototyping process and provides a deeper understanding of the system behavior. The excellent results which we achieved for the common rail injection system strongly suggest that our approach has a great potential of being generalized to other, similar scenarios.

Visualization System Requirements for Data Processing Pipeline Design and Optimization

“…Visualization brings benefits that include significantly improving the process of prototyping engineering designs [17] and comparing flood-control strategies [18].…”

“…In some applications visualization tools are just used to investigate output from computation steps, with examples being Vismon for multiple Monte Carlo runs of a fisheries model [19] and Orchestral for copy number calculations in genomics [1]). In other applications a user both runs computations and visualizes output with a single tool, with examples being World Lines [18] and the visual steering during design prototyping [17].…”

“…Each colored cell indicates a requirement that is important for a given case study/paper, and an 'x' indicates that the requirement remains unmet by users' current tools. [25] req req req req req Chemical process models [26] x req req x Economic modelling [27] req req xx req req x req Aircraft engine design [28] req req x req req Phylogenetic trees [29,30] x req x x req req Molecular evolution [31,32] x x req x req x * Raidou et al [75] iCoCooN req req req * Ruppert et al [46] --x req x Luboshik et al [38] --x req req req Bruckner et al [10] --x req x req * Beham et al [43] Cupid req req xx Konyha et al [39] --x req x Pretorius et al [12] Paramorama req x req Afzal et al [36] RVF x req req x Bergner et al [13] ParaGlide xx req Torsney-Weir et al [33] Tuner x req x x * Padua et al [11] --x req x * Bögl et al [42] TiMoVA req req req x x req Spence et al [73] --req req Berger et al [9] --req x xx Piringer et al [40] HyperMoVal xx req req req Matkovic et al [17] --req x Coffey et al [34] --x x req Matkovic et al [50] --x Potter et al [49] Ensemble-Vis x req Booshehrian et al [19] Vismon req x req x Brecheisen et al [52] --x req x Unger et al [54] --req req req Amirkhanov et al [74] --x Marks et al [48] Design Galleries req x Waser et al [18] World Lines req x x * Martins et al [35] --x req x * Wu [53] --xx x Guo et al [41] --req…”

“…Sometimes the pipeline is wellestablished (e.g., [38], [39]), but in other situations users need to choose between algorithms (e.g., the 3D image segmentation case study in Sec. 4.2), improve the sophistication of an existing pipeline [17], [40], make a pipeline robust to the characteristics of the input data [12], or design the pipeline from scratch [27], [41], [42]. The latter is particularly true in exploratory analysis, where users are analyzing a new form of data or looking for new patterns (e.g., [9], [10], [11], [43]).…”

On Computationally-Enhanced Visual Analysis of Heterogeneous Data and Its Application in Biomedical Informatics

“…Future challenges lie in the integration of alternate feature detection methods and their utilization in intelligent brushes. Furthermore, integrating IVA and simulations, thus supporting computational steering, offers a wide range of new possibilities for knowledge discovery [105].…”