An uncertainty estimation module for turbulence model predictions in SU2 Article Swipe

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Aashwin Mishra , Jayant Mukhopadhaya , Gianluca Iaccarino , Juan J. Alonso ·

YOU? · · 2018 · Open Access · · DOI: https://doi.org/10.48550/arxiv.1803.00725

With the advent of improved computational resources, aerospace design has testing-based process to a simulation-driven procedure, wherein uncertainties in design and operating conditions are explicitly accounted for in the design under uncertainty methodology. A key source of such uncertainties in design are the closure models used to account for fluid turbulence. In spite of their importance, no reliable and extensively tested modules are available to estimate this epistemic uncertainty. In this article, we outline the EQUiPS uncertainty estimation module developed for the SU2 CFD suite that focuses on uncertainty due to turbulence models. The theoretical foundations underlying this uncertainty estimation and its computational implementation are detailed. Thence, the performance of this module is presented for a range of test cases, including both benchmark problems and flows relevant to aerospace design. Across the range of test cases, the uncertainty estimates of the module were able to account for a significant portion of the discrepancy between RANS predictions and high fidelity data.

Related Topics

Benchmark (Surveying)

Computational Fluid Dynamics

Aerospace Engineering

Simulation

Systems Engineering

Engineering

Mechanical Engineering

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Benchmark (surveying) Uncertainty quantification Computational fluid dynamics Reynolds-averaged Navier–Stokes equations Range (aeronautics) Aerospace Computer science Suite Uncertainty analysis Surrogate model Turbulence Engineering design process Estimation Process (computing) Aerospace engineering Simulation Systems engineering Engineering Mechanical engineering Machine learning Thermodynamics Physics Geodesy Operating system Geography History Archaeology

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Type: preprint
Language: en
Landing Page: http://arxiv.org/abs/1803.00725
PDF: https://arxiv.org/pdf/1803.00725
OA Status: green
Cited By: 3
References: 11
Related Works: 10
OpenAlex ID: https://openalex.org/W2793727160

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DOI: https://doi.org/10.48550/arxiv.1803.00725

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Title: An uncertainty estimation module for turbulence model predictions in SU2

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Type: preprint

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Language: en

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Publication year: 2018

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Publication date: 2018-03-02

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Authors: Aashwin Mishra, Jayant Mukhopadhaya, Gianluca Iaccarino, Juan J. Alonso

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Landing page: https://arxiv.org/abs/1803.00725

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PDF URL: https://arxiv.org/pdf/1803.00725

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Concepts: Benchmark (surveying), Uncertainty quantification, Computational fluid dynamics, Reynolds-averaged Navier–Stokes equations, Range (aeronautics), Aerospace, Computer science, Suite, Uncertainty analysis, Surrogate model, Turbulence, Engineering design process, Estimation, Process (computing), Aerospace engineering, Simulation, Systems engineering, Engineering, Mechanical engineering, Machine learning, Thermodynamics, Physics, Geodesy, Operating system, Geography, History, Archaeology

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Cited by: 3

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Citations by year (recent): 2024: 1, 2023: 1, 2021: 1

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References (count): 11

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Related works (count): 10

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abstract_inverted_index.suite	84
abstract_inverted_index.their	54
abstract_inverted_index.under	30
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abstract_inverted_index.EQUiPS	75
abstract_inverted_index.advent	2
abstract_inverted_index.cases,	119, 135
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abstract_inverted_index.models	44
abstract_inverted_index.module	78, 111, 141
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abstract_inverted_index.tested	60
abstract_inverted_index.Thence,	106
abstract_inverted_index.account	47, 145
abstract_inverted_index.between	153
abstract_inverted_index.closure	43
abstract_inverted_index.design.	129
abstract_inverted_index.focuses	86
abstract_inverted_index.models.	92
abstract_inverted_index.modules	61
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abstract_inverted_index.wherein	16
abstract_inverted_index.article,	71
abstract_inverted_index.estimate	65
abstract_inverted_index.fidelity	158
abstract_inverted_index.improved	4
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abstract_inverted_index.relevant	126
abstract_inverted_index.reliable	57
abstract_inverted_index.accounted	25
abstract_inverted_index.aerospace	7, 128
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abstract_inverted_index.benchmark	122
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abstract_inverted_index.epistemic	67
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abstract_inverted_index.estimation	77, 99
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abstract_inverted_index.methodology.	32
abstract_inverted_index.uncertainty.	68
abstract_inverted_index.computational	5, 102
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cited_by_percentile_year
countries_distinct_count	0
institutions_distinct_count	4
sustainable_development_goals[0].id	https://metadata.un.org/sdg/9
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citation_normalized_percentile