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Estimation of inflow uncertainties in laminar hypersonic double-cone experiments

Conference · · AIAA Scitech Forum
DOI:https://doi.org/10.2514/6.2019-2279· OSTI ID:1619241
 [1];  [2];  [2];  [2];  [2];  [2];  [2];  [2];  [3];  [3]
  1. Sandia National Lab. (SNL-CA), Livermore, CA (United States)
  2. Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
  3. Univ. of Minnesota, Minneapolis, MN (United States)
+ Show Author Affiliations
We propose herein 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. We test the correctness of the framework on three double-cone experiments conducted in the CUBRC Inc.'s LENS-I shock tunnel, which have also been numerically simulated successfully. Thereafter, we use the framework to investigate two double-cone experiments (executed in the LENS-XX shock tunnel) which have encountered difficulties when used in model validation exercises. We detect an inconsistency with one of the LENS-XX experiments. In addition, we hypothesize two causes for our inability to simulate LEXS-XX experiments accurately and test them using our framework. We find 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. We end by proposing that uncertainty quantification methods be used more widely to understand experiments and characterize facilities, and we cite three different methods to do so, the third of which we present in this paper.
Research Organization:
Sandia National Laboratories (SNL-CA), Livermore, CA (United States); Sandia National Laboratories (SNL-NM), Albuquerque, NM (United States)
Sponsoring Organization:
USDOE National Nuclear Security Administration (NNSA), Office of Defense Science (NA-113)
DOE Contract Number:
AC04-94AL85000; NA0003525
OSTI ID:
1619241
Report Number(s):
SAND--2020-4921J; 685986; ISBN: 978-1-62410-578-4
Conference Information:
Journal Name: AIAA Scitech Forum Journal Volume: 2019
Country of Publication:
United States
Language:
English

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Figures / Tables (12)

Fig. 1(p. 7)figure Fig. 1
Table 1(p. 11)table Table 1
Fig. 2(p. 13)figure Fig. 2
Fig. 3(p. 14)figure Fig. 3
Fig. 4(p. 21)figure Fig. 4
Fig. 5(p. 22)figure Fig. 5
Fig. 6(p. 23)figure Fig. 6
Fig. 7(p. 25)figure Fig. 7
Fig. 8(p. 26)figure Fig. 8
Fig. 9(p. 27)figure Fig. 9
Table 2(p. 28)table Table 2
Table 3(p. 28)table Table 3

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