Performing an uncertainty analysis for complex measurement tasks, such as those found in engine research, presents unique challenges. Also, because of the excessive computational costs, modeling-based approaches, such as a Monte Carlo approach, may not be practical. This work provides a traditional statistical approach to uncertainty analysis that incorporates the uncertainty tree, which is a graphical tool for complex uncertainty analysis. Approaches to calculate the required sensitivities are discussed, including issues associated with numerical differentiation, numerical integration, and post-processing. Trimming of the uncertainty tree to remove insignificant contributions is discussed. The article concludes with a best practices guide in the Appendix to uncertainty propagation in experimental engine combustion post-processing, which includes suggested post-processing techniques and down-selected functional relationships for uncertainty propagation.


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    Titel :

    A Guide to Uncertainty Quantification for Experimental Engine Research and Heat Release Analysis


    Weitere Titelangaben:

    Sae Int. J. Engines


    Beteiligte:
    Lawler, Benjamin (Autor:in) / Gainey, Brian (Autor:in) / Longtin, Jon P. (Autor:in)

    Erschienen in:

    Erscheinungsdatum :

    2019-08-22


    Format / Umfang :

    15 pages




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Print


    Sprache :

    Englisch




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