Spatial Flow-Field Approximation Using Few Thermodynamic Measurements\n Part II: Uncertainty Assessments Article Swipe
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· 2019
· Open Access
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· DOI: https://doi.org/10.48550/arxiv.1908.02934
· OA: W4288265042
In this second part of our two-part paper, we provide a detailed, frequentist\nframework for propagating uncertainties within our multivariate linear least\nsquares model. This permits us to quantify the impact of uncertainties in\nthermodynamic measurements---arising from calibrations and the data acquisition\nsystem---and the correlations therein, along with uncertainties in probe\npositions. We show how the former has a much larger effect (relatively) than\nuncertainties in probe placement.\n We use this non-deterministic framework to demonstrate why the well-worn\nmetric for assessing spatial sampling uncertainty falls short of providing an\naccurate characterization of the effect of a few spatial measurements. In other\nwords, it does not accurately describe the uncertainty associated with sampling\na non-uniform pattern with a few circumferentially scattered rakes. To this\nend, we argue that our data-centric framework can offer a more rigorous\ncharacterization of this uncertainty. Our paper proposes two new uncertainty\nmetrics: one for characterizing spatial sampling uncertainty and another for\ncapturing the impact of measurement imprecision in individual probes. These\nmetrics are rigorously derived in our paper and their ease in computation\npermits them to be widely adopted by the turbomachinery community for carrying\nout uncertainty assessments.\n
