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On the influence of uncertainty in computational simulations of a high-speed jet flow from an aircraft exhaust
•Uncertainty Quantification has been applied to RANS simulations of a high-speed jet.•Generalised Polynomial Chaos and Kriging surrogates have been used and compared.•Results using the Spalart-Allmaras turbulence model are compared with other data.•The investigation outlines details of the spatial d...
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| Published in: | Computers & fluids 2019-02, Vol.180, p.139-158 |
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| creator | Granados-Ortiz, Francisco-Javier Arroyo, Carlos Pérez Puigt, Guillaume Lai, Choi-Hong Airiau, Christophe |
| description | •Uncertainty Quantification has been applied to RANS simulations of a high-speed jet.•Generalised Polynomial Chaos and Kriging surrogates have been used and compared.•Results using the Spalart-Allmaras turbulence model are compared with other data.•The investigation outlines details of the spatial distribution of uncertainty.•A global Sensitivity Analysis is carried out to show the contribution to uncertainty.
A classic approach to Computational Fluid Dynamics (CFD) is to perform simulations with a fixed set of variables in order to account for parameters and boundary conditions. However, experiments and real-life performance are subject to variability in their conditions. In recent years, the interest of performing simulations under uncertainty is increasing, but this is not yet a common rule, and simulations with lack of information are still taking place. This procedure could be missing details such as whether sources of uncertainty affect dramatic parts in the simulation of the flow. One of the reasons of avoiding to quantify uncertainties is that they usually require to run an unaffordable number of CFD simulations to develop the study.
To face this problem, Non-Intrusive Uncertainty Quantification (UQ) has been applied to 3D Reynolds-Averaged Navier-Stokes simulations of an under-expanded jet from an aircraft exhaust with the Spalart-Allmaras turbulent model, in order to assess the impact of inaccuracies and quality in the simulation. To save a large number of computations, sparse grids are used to compute the integrals and built surrogates for UQ. Results show that some regions of the jet plume can be more sensitive than others to variance in both physical and turbulence model parameters. The Spalart-Allmaras turbulent model is demonstrated to have an accurate performance with respect to other turbulent models in RANS, LES and experimental data, and the contribution of a large variance in its parameter is analysed. This investigation explicitly outlines, exhibits and proves the details of the relationship between diverse sources of input uncertainty, the sensitivity of different quantities of interest to said uncertainties and the spatial distribution arising due to their propagation in the simulation of the high-speed jet flow. This analysis represents first numerical study that provides evidence for this heuristic observation. |
| doi_str_mv | 10.1016/j.compfluid.2018.12.003 |
| format | article |
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A classic approach to Computational Fluid Dynamics (CFD) is to perform simulations with a fixed set of variables in order to account for parameters and boundary conditions. However, experiments and real-life performance are subject to variability in their conditions. In recent years, the interest of performing simulations under uncertainty is increasing, but this is not yet a common rule, and simulations with lack of information are still taking place. This procedure could be missing details such as whether sources of uncertainty affect dramatic parts in the simulation of the flow. One of the reasons of avoiding to quantify uncertainties is that they usually require to run an unaffordable number of CFD simulations to develop the study.
To face this problem, Non-Intrusive Uncertainty Quantification (UQ) has been applied to 3D Reynolds-Averaged Navier-Stokes simulations of an under-expanded jet from an aircraft exhaust with the Spalart-Allmaras turbulent model, in order to assess the impact of inaccuracies and quality in the simulation. To save a large number of computations, sparse grids are used to compute the integrals and built surrogates for UQ. Results show that some regions of the jet plume can be more sensitive than others to variance in both physical and turbulence model parameters. The Spalart-Allmaras turbulent model is demonstrated to have an accurate performance with respect to other turbulent models in RANS, LES and experimental data, and the contribution of a large variance in its parameter is analysed. This investigation explicitly outlines, exhibits and proves the details of the relationship between diverse sources of input uncertainty, the sensitivity of different quantities of interest to said uncertainties and the spatial distribution arising due to their propagation in the simulation of the high-speed jet flow. This analysis represents first numerical study that provides evidence for this heuristic observation.</description><identifier>ISSN: 0045-7930</identifier><identifier>EISSN: 1879-0747</identifier><identifier>DOI: 10.1016/j.compfluid.2018.12.003</identifier><language>eng</language><publisher>Amsterdam: Elsevier Ltd</publisher><subject>Aerodynamics ; Aircraft ; Boundary conditions ; CFD ; Computational fluid dynamics ; Computer Science ; Computer simulation ; Fluid flow ; High speed ; Jet flow ; Jets ; Kriging ; Modeling and Simulation ; Order parameters ; Polynomial chaos ; RANS ; Reynolds averaged Navier-Stokes method ; Simulation ; Spatial distribution ; Turbulence models ; Uncertainty ; Uncertainty quantification ; Variance</subject><ispartof>Computers & fluids, 2019-02, Vol.180, p.139-158</ispartof><rights>2018 Elsevier Ltd</rights><rights>Copyright Elsevier BV Feb 15, 2019</rights><rights>Distributed under a Creative Commons Attribution 4.0 International License</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c426t-22b950a8c6cd1e6816b1ad623adda29e8ecd55048ca34e66ed99dbc9acdbdc23</citedby><cites>FETCH-LOGICAL-c426t-22b950a8c6cd1e6816b1ad623adda29e8ecd55048ca34e66ed99dbc9acdbdc23</cites><orcidid>0000-0002-0617-3952 ; 0000-0003-4537-8165 ; 0000-0002-3043-7911 ; 0000-0001-7453-2964</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>230,315,786,790,891,27957,27958</link.rule.ids><backlink>$$Uhttps://hal.science/hal-01999518$$DView record in HAL$$Hfree_for_read</backlink></links><search><creatorcontrib>Granados-Ortiz, Francisco-Javier</creatorcontrib><creatorcontrib>Arroyo, Carlos Pérez</creatorcontrib><creatorcontrib>Puigt, Guillaume</creatorcontrib><creatorcontrib>Lai, Choi-Hong</creatorcontrib><creatorcontrib>Airiau, Christophe</creatorcontrib><title>On the influence of uncertainty in computational simulations of a high-speed jet flow from an aircraft exhaust</title><title>Computers & fluids</title><description>•Uncertainty Quantification has been applied to RANS simulations of a high-speed jet.•Generalised Polynomial Chaos and Kriging surrogates have been used and compared.•Results using the Spalart-Allmaras turbulence model are compared with other data.•The investigation outlines details of the spatial distribution of uncertainty.•A global Sensitivity Analysis is carried out to show the contribution to uncertainty.
A classic approach to Computational Fluid Dynamics (CFD) is to perform simulations with a fixed set of variables in order to account for parameters and boundary conditions. However, experiments and real-life performance are subject to variability in their conditions. In recent years, the interest of performing simulations under uncertainty is increasing, but this is not yet a common rule, and simulations with lack of information are still taking place. This procedure could be missing details such as whether sources of uncertainty affect dramatic parts in the simulation of the flow. One of the reasons of avoiding to quantify uncertainties is that they usually require to run an unaffordable number of CFD simulations to develop the study.
To face this problem, Non-Intrusive Uncertainty Quantification (UQ) has been applied to 3D Reynolds-Averaged Navier-Stokes simulations of an under-expanded jet from an aircraft exhaust with the Spalart-Allmaras turbulent model, in order to assess the impact of inaccuracies and quality in the simulation. To save a large number of computations, sparse grids are used to compute the integrals and built surrogates for UQ. Results show that some regions of the jet plume can be more sensitive than others to variance in both physical and turbulence model parameters. The Spalart-Allmaras turbulent model is demonstrated to have an accurate performance with respect to other turbulent models in RANS, LES and experimental data, and the contribution of a large variance in its parameter is analysed. This investigation explicitly outlines, exhibits and proves the details of the relationship between diverse sources of input uncertainty, the sensitivity of different quantities of interest to said uncertainties and the spatial distribution arising due to their propagation in the simulation of the high-speed jet flow. This analysis represents first numerical study that provides evidence for this heuristic observation.</description><subject>Aerodynamics</subject><subject>Aircraft</subject><subject>Boundary conditions</subject><subject>CFD</subject><subject>Computational fluid dynamics</subject><subject>Computer Science</subject><subject>Computer simulation</subject><subject>Fluid flow</subject><subject>High speed</subject><subject>Jet flow</subject><subject>Jets</subject><subject>Kriging</subject><subject>Modeling and Simulation</subject><subject>Order parameters</subject><subject>Polynomial chaos</subject><subject>RANS</subject><subject>Reynolds averaged Navier-Stokes method</subject><subject>Simulation</subject><subject>Spatial distribution</subject><subject>Turbulence models</subject><subject>Uncertainty</subject><subject>Uncertainty quantification</subject><subject>Variance</subject><issn>0045-7930</issn><issn>1879-0747</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2019</creationdate><recordtype>article</recordtype><recordid>eNqFkU1P3DAQhi0EEsvHb8BSTz0ktR3nw8cVKlBpJS7crVl7Qhxl48V2aPn3ON2Ka0-jsZ95pJmXkDvOSs5482MsjT8c-2lxthSMdyUXJWPVGdnwrlUFa2V7TjaMybpoVcUuyVWMI8t9JeSGzM8zTQNSN2cDzgap7-mSa0jg5vSRP-jqXxIk52eYaHSHZfrbxJUFOrjXoYhHREtHTLSf_G_aB3-gMFNwwQToE8U_Aywx3ZCLHqaIt__qNXl5-Ply_1Tsnh9_3W93hZGiSYUQe1Uz6ExjLMem482eg21EBdaCUNihsXXNZGegktg0aJWye6PA2L01orom30_aASZ9DO4A4UN7cPppu9PrG-NKqZp37zyz307sMfi3BWPSo19C3jRqIUQruZScZao9USb4GAP2X1rO9JqDHvVXDnrNQXOhcw55cnuaxLzvu8Ogo3Hrpa0LaJK23v3X8QkLyJdz</recordid><startdate>20190215</startdate><enddate>20190215</enddate><creator>Granados-Ortiz, Francisco-Javier</creator><creator>Arroyo, Carlos Pérez</creator><creator>Puigt, Guillaume</creator><creator>Lai, Choi-Hong</creator><creator>Airiau, Christophe</creator><general>Elsevier Ltd</general><general>Elsevier BV</general><general>Elsevier</general><scope>AAYXX</scope><scope>CITATION</scope><scope>7SC</scope><scope>7TB</scope><scope>7U5</scope><scope>8FD</scope><scope>FR3</scope><scope>H8D</scope><scope>JQ2</scope><scope>KR7</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope><scope>1XC</scope><scope>VOOES</scope><orcidid>https://orcid.org/0000-0002-0617-3952</orcidid><orcidid>https://orcid.org/0000-0003-4537-8165</orcidid><orcidid>https://orcid.org/0000-0002-3043-7911</orcidid><orcidid>https://orcid.org/0000-0001-7453-2964</orcidid></search><sort><creationdate>20190215</creationdate><title>On the influence of uncertainty in computational simulations of a high-speed jet flow from an aircraft exhaust</title><author>Granados-Ortiz, Francisco-Javier ; 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A classic approach to Computational Fluid Dynamics (CFD) is to perform simulations with a fixed set of variables in order to account for parameters and boundary conditions. However, experiments and real-life performance are subject to variability in their conditions. In recent years, the interest of performing simulations under uncertainty is increasing, but this is not yet a common rule, and simulations with lack of information are still taking place. This procedure could be missing details such as whether sources of uncertainty affect dramatic parts in the simulation of the flow. One of the reasons of avoiding to quantify uncertainties is that they usually require to run an unaffordable number of CFD simulations to develop the study.
To face this problem, Non-Intrusive Uncertainty Quantification (UQ) has been applied to 3D Reynolds-Averaged Navier-Stokes simulations of an under-expanded jet from an aircraft exhaust with the Spalart-Allmaras turbulent model, in order to assess the impact of inaccuracies and quality in the simulation. To save a large number of computations, sparse grids are used to compute the integrals and built surrogates for UQ. Results show that some regions of the jet plume can be more sensitive than others to variance in both physical and turbulence model parameters. The Spalart-Allmaras turbulent model is demonstrated to have an accurate performance with respect to other turbulent models in RANS, LES and experimental data, and the contribution of a large variance in its parameter is analysed. This investigation explicitly outlines, exhibits and proves the details of the relationship between diverse sources of input uncertainty, the sensitivity of different quantities of interest to said uncertainties and the spatial distribution arising due to their propagation in the simulation of the high-speed jet flow. This analysis represents first numerical study that provides evidence for this heuristic observation.</abstract><cop>Amsterdam</cop><pub>Elsevier Ltd</pub><doi>10.1016/j.compfluid.2018.12.003</doi><tpages>20</tpages><orcidid>https://orcid.org/0000-0002-0617-3952</orcidid><orcidid>https://orcid.org/0000-0003-4537-8165</orcidid><orcidid>https://orcid.org/0000-0002-3043-7911</orcidid><orcidid>https://orcid.org/0000-0001-7453-2964</orcidid><oa>free_for_read</oa></addata></record> |
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| ispartof | Computers & fluids, 2019-02, Vol.180, p.139-158 |
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| source | ScienceDirect Freedom Collection |
| subjects | Aerodynamics Aircraft Boundary conditions CFD Computational fluid dynamics Computer Science Computer simulation Fluid flow High speed Jet flow Jets Kriging Modeling and Simulation Order parameters Polynomial chaos RANS Reynolds averaged Navier-Stokes method Simulation Spatial distribution Turbulence models Uncertainty Uncertainty quantification Variance |
| title | On the influence of uncertainty in computational simulations of a high-speed jet flow from an aircraft exhaust |
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