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| Format: | Preprint |
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2024
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| Online-Zugang: | https://arxiv.org/abs/2411.16693 |
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| _version_ | 1866910907916877824 |
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| author | Georgalis, Georgios Becerra, Alejandro Budzinski, Kenneth McGurn, Matthew Faghihi, Danial DesJardin, Paul E. Patra, Abani |
| author_facet | Georgalis, Georgios Becerra, Alejandro Budzinski, Kenneth McGurn, Matthew Faghihi, Danial DesJardin, Paul E. Patra, Abani |
| contents | The goal of this paper is to demonstrate and address challenges related to all aspects of performing a complete uncertainty quantification analysis of a complicated physics-based simulation like a 2D slab burner direct numerical simulation (DNS). The UQ framework includes the development of data-driven surrogate models, propagation of parametric uncertainties to the fuel regression rate--the primary quantity of interest--and Bayesian calibration of the latent heat of sublimation and a chemical reaction temperature exponent using experimental data. Two surrogate models, a Gaussian Process (GP) and a Hierarchical Multiscale Surrogate (HMS) were constructed using an ensemble of 64 simulations generated via Latin Hypercube sampling. HMS is superior for prediction demonstrated by cross-validation and able to achieve an error < 15% when predicting multiscale boundary quantities just from a few far field inputs. Subsequent Bayesian calibration of chemical kinetics and fuel response parameters against experimental observations showed that the default values used in the DNS should be higher to better match measurements. This study highlights the importance of surrogate model selection and parameter calibration in quantifying uncertainty in predictions of fuel regression rates in complex combustion systems. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_16693 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | UQ of 2D Slab Burner DNS: Surrogates, Uncertainty Propagation, and Parameter Calibration Georgalis, Georgios Becerra, Alejandro Budzinski, Kenneth McGurn, Matthew Faghihi, Danial DesJardin, Paul E. Patra, Abani Computational Physics Machine Learning The goal of this paper is to demonstrate and address challenges related to all aspects of performing a complete uncertainty quantification analysis of a complicated physics-based simulation like a 2D slab burner direct numerical simulation (DNS). The UQ framework includes the development of data-driven surrogate models, propagation of parametric uncertainties to the fuel regression rate--the primary quantity of interest--and Bayesian calibration of the latent heat of sublimation and a chemical reaction temperature exponent using experimental data. Two surrogate models, a Gaussian Process (GP) and a Hierarchical Multiscale Surrogate (HMS) were constructed using an ensemble of 64 simulations generated via Latin Hypercube sampling. HMS is superior for prediction demonstrated by cross-validation and able to achieve an error < 15% when predicting multiscale boundary quantities just from a few far field inputs. Subsequent Bayesian calibration of chemical kinetics and fuel response parameters against experimental observations showed that the default values used in the DNS should be higher to better match measurements. This study highlights the importance of surrogate model selection and parameter calibration in quantifying uncertainty in predictions of fuel regression rates in complex combustion systems. |
| title | UQ of 2D Slab Burner DNS: Surrogates, Uncertainty Propagation, and Parameter Calibration |
| topic | Computational Physics Machine Learning |
| url | https://arxiv.org/abs/2411.16693 |