Estimation of inflow uncertainties in laminar hypersonic double-cone experiments

J. Ray, S. Kieweg, D. Dinzl, B. Carnes, V. G. Weirs, B. Freno, M. Howard, T. Smith, I. Nompelis, G. V. Candler

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

We propose 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 statistical inference to estimate experimental settings deemed untrustworthy, from measurements deemed accurate. The quality of the inferred variables is gauged by its ability to reproduce held-out experimental measurements; if the new predictions are closer to measurements than before, the cause of the discrepancy is deemed to have been found. The framework brings together recent advances in the use of Bayesian inference and statistical emulators in fluid dynamics with similarity measures for random variables to construct the hypothesis testing approach. We test the framework on two double-cone experiments executed in the LENS-XX wind tunnel and one in the LENS-I tunnel; all three have encountered difficulties when used in model validation exercises. However, the cause behind the difficulties with the LENS-I experiment is known, and our inferential framework recovers it. We also detect an inconsistency with one of the LENS-XX experiments, and hypothesize three causes for it. We check two of the hypotheses using our framework, and we find evidence that rejects them. 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.

Original languageEnglish (US)
Title of host publicationAIAA Scitech 2019 Forum
PublisherAmerican Institute of Aeronautics and Astronautics Inc, AIAA
ISBN (Print)9781624105784
DOIs
StatePublished - Jan 1 2019
EventAIAA Scitech Forum, 2019 - San Diego, United States
Duration: Jan 7 2019Jan 11 2019

Publication series

NameAIAA Scitech 2019 Forum

Conference

ConferenceAIAA Scitech Forum, 2019
CountryUnited States
CitySan Diego
Period1/7/191/11/19

Fingerprint

Hypersonic aerodynamics
Cones
Wind tunnels
Experiments
Fluid dynamics
Random variables
Uncertainty
Testing

Cite this

Ray, J., Kieweg, S., Dinzl, D., Carnes, B., Weirs, V. G., Freno, B., ... Candler, G. V. (2019). Estimation of inflow uncertainties in laminar hypersonic double-cone experiments. In AIAA Scitech 2019 Forum (AIAA Scitech 2019 Forum). American Institute of Aeronautics and Astronautics Inc, AIAA. https://doi.org/10.2514/6.2019-2279

Estimation of inflow uncertainties in laminar hypersonic double-cone experiments. / Ray, J.; Kieweg, S.; Dinzl, D.; Carnes, B.; Weirs, V. G.; Freno, B.; Howard, M.; Smith, T.; Nompelis, I.; Candler, G. V.

AIAA Scitech 2019 Forum. American Institute of Aeronautics and Astronautics Inc, AIAA, 2019. (AIAA Scitech 2019 Forum).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Ray, J, Kieweg, S, Dinzl, D, Carnes, B, Weirs, VG, Freno, B, Howard, M, Smith, T, Nompelis, I & Candler, GV 2019, Estimation of inflow uncertainties in laminar hypersonic double-cone experiments. in AIAA Scitech 2019 Forum. AIAA Scitech 2019 Forum, American Institute of Aeronautics and Astronautics Inc, AIAA, AIAA Scitech Forum, 2019, San Diego, United States, 1/7/19. https://doi.org/10.2514/6.2019-2279
Ray J, Kieweg S, Dinzl D, Carnes B, Weirs VG, Freno B et al. Estimation of inflow uncertainties in laminar hypersonic double-cone experiments. In AIAA Scitech 2019 Forum. American Institute of Aeronautics and Astronautics Inc, AIAA. 2019. (AIAA Scitech 2019 Forum). https://doi.org/10.2514/6.2019-2279
Ray, J. ; Kieweg, S. ; Dinzl, D. ; Carnes, B. ; Weirs, V. G. ; Freno, B. ; Howard, M. ; Smith, T. ; Nompelis, I. ; Candler, G. V. / Estimation of inflow uncertainties in laminar hypersonic double-cone experiments. AIAA Scitech 2019 Forum. American Institute of Aeronautics and Astronautics Inc, AIAA, 2019. (AIAA Scitech 2019 Forum).
@inproceedings{a559b441d2e042c4b2f0b0e70759e30e,
title = "Estimation of inflow uncertainties in laminar hypersonic double-cone experiments",
abstract = "We propose 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 statistical inference to estimate experimental settings deemed untrustworthy, from measurements deemed accurate. The quality of the inferred variables is gauged by its ability to reproduce held-out experimental measurements; if the new predictions are closer to measurements than before, the cause of the discrepancy is deemed to have been found. The framework brings together recent advances in the use of Bayesian inference and statistical emulators in fluid dynamics with similarity measures for random variables to construct the hypothesis testing approach. We test the framework on two double-cone experiments executed in the LENS-XX wind tunnel and one in the LENS-I tunnel; all three have encountered difficulties when used in model validation exercises. However, the cause behind the difficulties with the LENS-I experiment is known, and our inferential framework recovers it. We also detect an inconsistency with one of the LENS-XX experiments, and hypothesize three causes for it. We check two of the hypotheses using our framework, and we find evidence that rejects them. 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.",
author = "J. Ray and S. Kieweg and D. Dinzl and B. Carnes and Weirs, {V. G.} and B. Freno and M. Howard and T. Smith and I. Nompelis and Candler, {G. V.}",
year = "2019",
month = "1",
day = "1",
doi = "10.2514/6.2019-2279",
language = "English (US)",
isbn = "9781624105784",
series = "AIAA Scitech 2019 Forum",
publisher = "American Institute of Aeronautics and Astronautics Inc, AIAA",
booktitle = "AIAA Scitech 2019 Forum",

}

TY - GEN

T1 - Estimation of inflow uncertainties in laminar hypersonic double-cone experiments

AU - Ray, J.

AU - Kieweg, S.

AU - Dinzl, D.

AU - Carnes, B.

AU - Weirs, V. G.

AU - Freno, B.

AU - Howard, M.

AU - Smith, T.

AU - Nompelis, I.

AU - Candler, G. V.

PY - 2019/1/1

Y1 - 2019/1/1

N2 - We propose 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 statistical inference to estimate experimental settings deemed untrustworthy, from measurements deemed accurate. The quality of the inferred variables is gauged by its ability to reproduce held-out experimental measurements; if the new predictions are closer to measurements than before, the cause of the discrepancy is deemed to have been found. The framework brings together recent advances in the use of Bayesian inference and statistical emulators in fluid dynamics with similarity measures for random variables to construct the hypothesis testing approach. We test the framework on two double-cone experiments executed in the LENS-XX wind tunnel and one in the LENS-I tunnel; all three have encountered difficulties when used in model validation exercises. However, the cause behind the difficulties with the LENS-I experiment is known, and our inferential framework recovers it. We also detect an inconsistency with one of the LENS-XX experiments, and hypothesize three causes for it. We check two of the hypotheses using our framework, and we find evidence that rejects them. 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.

AB - We propose 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 statistical inference to estimate experimental settings deemed untrustworthy, from measurements deemed accurate. The quality of the inferred variables is gauged by its ability to reproduce held-out experimental measurements; if the new predictions are closer to measurements than before, the cause of the discrepancy is deemed to have been found. The framework brings together recent advances in the use of Bayesian inference and statistical emulators in fluid dynamics with similarity measures for random variables to construct the hypothesis testing approach. We test the framework on two double-cone experiments executed in the LENS-XX wind tunnel and one in the LENS-I tunnel; all three have encountered difficulties when used in model validation exercises. However, the cause behind the difficulties with the LENS-I experiment is known, and our inferential framework recovers it. We also detect an inconsistency with one of the LENS-XX experiments, and hypothesize three causes for it. We check two of the hypotheses using our framework, and we find evidence that rejects them. 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.

UR - http://www.scopus.com/inward/record.url?scp=85068977224&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85068977224&partnerID=8YFLogxK

U2 - 10.2514/6.2019-2279

DO - 10.2514/6.2019-2279

M3 - Conference contribution

AN - SCOPUS:85068977224

SN - 9781624105784

T3 - AIAA Scitech 2019 Forum

BT - AIAA Scitech 2019 Forum

PB - American Institute of Aeronautics and Astronautics Inc, AIAA

ER -