Estimation of Inflow Uncertainties in Laminar Hypersonic Double-Cone Experiments

This paper proposes a probabilistic framework for assessing the consistency of an experimental dataset, i.e., whether the stated experimental conditions are consistent with the measurements provided. In case the dataset is inconsistent, our framework allows one to hypothesize and test sources of inc...

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Bibliographic Details
Published in:AIAA journal 2020-10, Vol.58 (10), p.4461-4474
Main Authors: Ray, J, Kieweg, S, Dinzl, D, Carnes, B, Weirs, V. G, Freno, B, Howard, M, Smith, T, Nompelis, I, Candler, G. V
Format: Article
Language:English
Subjects:
Bayesian analysis
Computer simulation
Datasets
Experiments
Lenses
Research facilities
Shock tunnels
Statistical inference
Uncertainty
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Summary:This paper proposes a probabilistic framework for assessing the consistency of an experimental dataset, i.e., whether the stated experimental conditions are consistent with the measurements provided. In case the dataset is inconsistent, our framework allows one to hypothesize and test sources of inconsistencies. This is crucial in model validation efforts. The framework relies on Bayesian inference to estimate experimental settings deemed uncertain, from measurements deemed accurate. The quality of the inferred variables is gauged by its ability to reproduce held-out experimental measurements. The correctness of the framework is tested on three double-cone experiments conducted in the Calspan-University at Buffalo Research Center’s Large Energy National Shock Tunnel-I (LENS-I), which have also been numerically simulated successfully. Thereafter, the framework is used to investigate two double-cone experiments (executed in the LENS-XX), which have encountered difficulties when used in model validation exercises. An inconsistency is detected with one of the LENS-XX experiments. In addition, two causes are hypothesized for our inability to simulate LEXS-XX experiments accurately and are tested using our framework. It is found that there is no single cause that explains all the discrepancies between model predictions and experimental data, but different causes explain different discrepancies, to larger or smaller extent. This paper proposes that uncertainty quantification methods be used more widely to understand experiments and characterize facilities, and three different methods are cited to do so, the third of which is presented in this paper.
ISSN:0001-1452
1533-385X
DOI:10.2514/1.J059033