Bayesian Parameter Estimation of a κ-ε Model for Accurate Jet-in-Crossflow Simulations.
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| Title: | Bayesian Parameter Estimation of a κ-ε Model for Accurate Jet-in-Crossflow Simulations. |
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| Authors: | Ray, Jaideep1, Lefantzi, Sophia1, Arunajatesan, Srinivasan2, Dechant, Lawrence2 |
| Source: | AIAA Journal. Aug2016, Vol. 54 Issue 8, p2432-2448. 17p. |
| Abstract: | Reynolds-averaged Navier-Stokes models are not very accurate for high-Reynolds-number compressible jet-in-crossflow interactions. The inaccuracy arises from the use of inappropriate model parameters and model-form errors in the Reynolds-averaged Navier-Stokes model. In this work, the hypothesis is pursued that Reynolds-averaged Navier-Stokes predictions can be significantly improved by using parameters inferred from experimental measurements of a supersonic jet interacting with a transonic crossflow. A Bayesian inverse problem is formulated to estimate three Reynolds-averaged Navier-Stokes parameters (C956,Cε2,Cε1), and a Markov chain Monte Carlo method is used to develop a probability density function for them. The cost of the Markov chain Monte Carlo is addressed by developing statistical surrogates for the Reynolds-averaged Navier-Stokes model. It is found that only a subset of the (C956,Cε2,Cε1) space RR supports realistic flow simulations. RR is used as a prior belief when formulating the inverse problem. It is enforced with a classifier in the current Markov chain Monte Carlo solution. It is found that the calibrated parameters improve predictions of the entire flowfield substantially when compared to the nominal/literature values of (C956,Cε2,Cε1); furthermore, this improvement is seen to hold for interactions at other Mach numbers and jet strengths for which the experimental data are available to provide a comparison. The residual error is quantifies, which is an approximation of the model-form error; it is most easily measured in terms of turbulent stresses. [ABSTRACT FROM AUTHOR] |
| Copyright of AIAA Journal is the property of American Institute of Aeronautics & Astronautics and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
| FullText | Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 118057485 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Bayesian Parameter Estimation of a κ-ε Model for Accurate Jet-in-Crossflow Simulations. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Ray%2C+Jaideep%22">Ray, Jaideep</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Lefantzi%2C+Sophia%22">Lefantzi, Sophia</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Arunajatesan%2C+Srinivasan%22">Arunajatesan, Srinivasan</searchLink><relatesTo>2</relatesTo><br /><searchLink fieldCode="AR" term="%22Dechant%2C+Lawrence%22">Dechant, Lawrence</searchLink><relatesTo>2</relatesTo> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22AIAA+Journal%22">AIAA Journal</searchLink>. Aug2016, Vol. 54 Issue 8, p2432-2448. 17p. – Name: Abstract Label: Abstract Group: Ab Data: Reynolds-averaged Navier-Stokes models are not very accurate for high-Reynolds-number compressible jet-in-crossflow interactions. The inaccuracy arises from the use of inappropriate model parameters and model-form errors in the Reynolds-averaged Navier-Stokes model. In this work, the hypothesis is pursued that Reynolds-averaged Navier-Stokes predictions can be significantly improved by using parameters inferred from experimental measurements of a supersonic jet interacting with a transonic crossflow. A Bayesian inverse problem is formulated to estimate three Reynolds-averaged Navier-Stokes parameters (C956,Cε2,Cε1), and a Markov chain Monte Carlo method is used to develop a probability density function for them. The cost of the Markov chain Monte Carlo is addressed by developing statistical surrogates for the Reynolds-averaged Navier-Stokes model. It is found that only a subset of the (C956,Cε2,Cε1) space RR supports realistic flow simulations. RR is used as a prior belief when formulating the inverse problem. It is enforced with a classifier in the current Markov chain Monte Carlo solution. It is found that the calibrated parameters improve predictions of the entire flowfield substantially when compared to the nominal/literature values of (C956,Cε2,Cε1); furthermore, this improvement is seen to hold for interactions at other Mach numbers and jet strengths for which the experimental data are available to provide a comparison. The residual error is quantifies, which is an approximation of the model-form error; it is most easily measured in terms of turbulent stresses. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of AIAA Journal is the property of American Institute of Aeronautics & Astronautics and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.2514/1.J054758 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 17 StartPage: 2432 Titles: – TitleFull: Bayesian Parameter Estimation of a κ-ε Model for Accurate Jet-in-Crossflow Simulations. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Ray, Jaideep – PersonEntity: Name: NameFull: Lefantzi, Sophia – PersonEntity: Name: NameFull: Arunajatesan, Srinivasan – PersonEntity: Name: NameFull: Dechant, Lawrence IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 08 Text: Aug2016 Type: published Y: 2016 Identifiers: – Type: issn-print Value: 00011452 Numbering: – Type: volume Value: 54 – Type: issue Value: 8 Titles: – TitleFull: AIAA Journal Type: main |
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