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Visual Analytics for Complex Engineering Systems: Hybrid Visual Steering of Simulation Ensembles.

Krešimir Matković, Denis Gračanin, Rainer Splechtna

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    This study introduces hybrid visual steering, combining interactive visualization with automatic optimization for simulation ensembles. This approach efficiently navigates complex parameter spaces, improving engineering system design and analysis.

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    Area of Science:

    • Engineering Simulation
    • Computer-Aided Engineering
    • Data Visualization

    Background:

    • Simulation ensembles are crucial for complex engineering systems but face challenges with large parameter spaces.
    • Traditional interactive steering methods can be insufficient for highly complex systems.
    • Efficiently exploring multi-dimensional parameter spaces is essential for design optimization.

    Purpose of the Study:

    • To propose a novel hybrid visual steering approach for simulation ensembles.
    • To integrate interactive visual steering with automatic optimization techniques.
    • To enhance the exploration and optimization of complex engineering systems.

    Main Methods:

    • Developed a hybrid steering system coupling simulation, visualization, and optimization components.
    • Employed interactive visual steering for iterative data point selection.
    • Utilized regression for approximating continuous simulation space regions and optimization for finding optimal points.

    Main Results:

    • The hybrid approach enables domain experts to interactively select data points and identify optimal solutions.
    • Demonstrated improved steering process through a full spectrum of optimization options.
    • Successfully applied the method to optimize a hydraulic circuit in a automotive common rail Diesel injection system.

    Conclusions:

    • Hybrid visual steering offers a substantial improvement for analyzing and optimizing complex engineering systems.
    • The integrated system and proposed workflow facilitate efficient design of experiments.
    • This approach effectively reduces the number of simulations required for complex parameter space exploration.