@phdthesis{,
    author = {Jehle, Jonas Siegfried},
    title = {Uncertainty Management Framework for Automotive Crash Applications},
    editor = {},
    booktitle = {},
    series = {},
    journal = {},
    address = {},
    publisher = {},
    edition = {},
    year = {2022},
    isbn = {},
    volume = {},
    number =  {},
    pages = {},
    url = {},
    doi = {},
    keywords = {Uncertainty Analysis, Sensitivity Analysis, Metamodels, Crash, Finite Element Models },
    abstract = {This thesis presents a general framework for managing uncertainties of vehicle safety applications. The need for this framework stems from the multiple uncertainties from different sources present in crash scenarios that have a decisive effect on the results and thus on the safety of vehicles. Engineers must therefore confront these uncertainties inherent in crash models that can be computationally expensive. The framework shows different ways to enable uncertainty analysis. The appropriate route depends on the type of the object under investigation. Resource efficient models can be directly subjected to further analysis. Resource inefficient models can first be optionally simplified by technical expertise. The model under consideration is then replaced by approximated response surfaces characterized by low computational times. In terms of time, this enables subsequent analyses. When the model includes many parameters, the quality of the approximated response surfaces may suffer. Screening can therefore be used to reduce the input space to the important dimensions before creating the approximated response surfaces. By using them afterwards, sensitivity analysis can be performed rapidly. This evaluates the relevance of the individual parameters and their interactions. As a final step, uncertainty propagation measures the effects of the uncertain parameters for specified quantities of interest. Finite element simulations, the most commonly studied models in crash, are devoted special focus. Accordingly, the framework is adapted to them. At first, the simulations are defined as mathematical mappings of different levels of information. The second step consists of metamodels, representatives of approximated response surfaces, which approximate the mappings. Next, variance-based sensitivity analysis is performed quickly by using the metamodels. In the end, the Dempster-Shafer evidence theory is used for uncertainty propagation. The framework that structures these methods makes this whole procedure efficient, temporally feasible, and flexible. It also yields a reasonable visualization for engineers. The practicability of the framework is examined on the basis of a real-world project. The object of investigation is a finite element simulation of a side pole test. The individual components of the framework are applied step by step. Metamodels approximating quantities of interest at different levels of information are compared. The best metamodel is then used for further investigations, i.e. sensitivity and uncertainty analysis. Engineers can use the results of these analyses to assess the level of maturity of their systems. The framework thus successfully contributes to the evaluation and improvement of passive safety concepts.},
    note = {},
    
    school = {Universität der Bundeswehr München},
    
}