High-Order Deterministic Sensitivity Analysis and Uncertainty Quantification: Review and New Developments
This work reviews the state-of-the-art methodologies for the deterministic sensitivity analysis of nonlinear systems and deterministic quantification of uncertainties induced in model responses by uncertainties in the model parameters. The need for computing high-order sensitivities is underscored b...
| Main Author: | |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2021-10-01
|
| Series: | Energies |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1996-1073/14/20/6715 |
| _version_ | 1797514743139794944 |
|---|---|
| author | Dan Gabriel Cacuci |
| author_facet | Dan Gabriel Cacuci |
| author_sort | Dan Gabriel Cacuci |
| collection | DOAJ |
| description | This work reviews the state-of-the-art methodologies for the deterministic sensitivity analysis of nonlinear systems and deterministic quantification of uncertainties induced in model responses by uncertainties in the model parameters. The need for computing high-order sensitivities is underscored by presenting an analytically solvable model of neutron scattering in a hydrogenous medium, for which all of the response’s relative sensitivities have the same absolute value of unity. It is shown that the wider the distribution of model parameters, the higher the order of sensitivities needed to achieve a desired level of accuracy in representing the response and in computing the response’s expectation, variance, skewness and kurtosis. This work also presents new mathematical expressions that extend to the sixth-order of the current state-of-the-art fourth-order formulas for computing fourth-order correlations among computed model response and model parameters. Another novelty presented in this work is the mathematical framework of the 3rd-Order Comprehensive Adjoint Sensitivity Analysis Methodology for Nonlinear Systems (3rd-CASAM-N), which enables the most efficient computation of the exact expressions of the 1st-, 2nd- and 3rd-order functional derivatives (“sensitivities”) of a model’s response to the underlying model parameters, including imprecisely known initial, boundary and/or interface conditions. The 2nd- and 3rd-level adjoint functions are computed using the same forward and adjoint computer solvers as used for solving the original forward and adjoint systems. Comparisons between the CPU times are also presented for an OECD/NEA reactor physics benchmark, highlighting the fact that finite-difference schemes would not only provide approximate values for the respective sensitivities (in contradistinction to the 3rd-CASAM-N, which provides exact expressions for the sensitivities) but would simply be unfeasible for computing sensitivities of an order higher than first-order. Ongoing work will generalize the 3rd-CASAM-N to a higher order while aiming to overcome the curse of dimensionality. |
| first_indexed | 2024-03-10T06:36:05Z |
| format | Article |
| id | doaj.art-e3bea32c0b7f40d2844122019efa72ad |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-03-10T06:36:05Z |
| publishDate | 2021-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-e3bea32c0b7f40d2844122019efa72ad2023-11-22T18:07:38ZengMDPI AGEnergies1996-10732021-10-011420671510.3390/en14206715High-Order Deterministic Sensitivity Analysis and Uncertainty Quantification: Review and New DevelopmentsDan Gabriel Cacuci0Center for Nuclear Science and Energy, Department of Mechanical Engineering, University of South Carolina, Columbia, SC 29208, USAThis work reviews the state-of-the-art methodologies for the deterministic sensitivity analysis of nonlinear systems and deterministic quantification of uncertainties induced in model responses by uncertainties in the model parameters. The need for computing high-order sensitivities is underscored by presenting an analytically solvable model of neutron scattering in a hydrogenous medium, for which all of the response’s relative sensitivities have the same absolute value of unity. It is shown that the wider the distribution of model parameters, the higher the order of sensitivities needed to achieve a desired level of accuracy in representing the response and in computing the response’s expectation, variance, skewness and kurtosis. This work also presents new mathematical expressions that extend to the sixth-order of the current state-of-the-art fourth-order formulas for computing fourth-order correlations among computed model response and model parameters. Another novelty presented in this work is the mathematical framework of the 3rd-Order Comprehensive Adjoint Sensitivity Analysis Methodology for Nonlinear Systems (3rd-CASAM-N), which enables the most efficient computation of the exact expressions of the 1st-, 2nd- and 3rd-order functional derivatives (“sensitivities”) of a model’s response to the underlying model parameters, including imprecisely known initial, boundary and/or interface conditions. The 2nd- and 3rd-level adjoint functions are computed using the same forward and adjoint computer solvers as used for solving the original forward and adjoint systems. Comparisons between the CPU times are also presented for an OECD/NEA reactor physics benchmark, highlighting the fact that finite-difference schemes would not only provide approximate values for the respective sensitivities (in contradistinction to the 3rd-CASAM-N, which provides exact expressions for the sensitivities) but would simply be unfeasible for computing sensitivities of an order higher than first-order. Ongoing work will generalize the 3rd-CASAM-N to a higher order while aiming to overcome the curse of dimensionality.https://www.mdpi.com/1996-1073/14/20/6715six-order moments of model response distribution in parameter phase spacethird-order comprehensive adjoint sensitivity analysis methodology for nonlinear systems |
| spellingShingle | Dan Gabriel Cacuci High-Order Deterministic Sensitivity Analysis and Uncertainty Quantification: Review and New Developments Energies six-order moments of model response distribution in parameter phase space third-order comprehensive adjoint sensitivity analysis methodology for nonlinear systems |
| title | High-Order Deterministic Sensitivity Analysis and Uncertainty Quantification: Review and New Developments |
| title_full | High-Order Deterministic Sensitivity Analysis and Uncertainty Quantification: Review and New Developments |
| title_fullStr | High-Order Deterministic Sensitivity Analysis and Uncertainty Quantification: Review and New Developments |
| title_full_unstemmed | High-Order Deterministic Sensitivity Analysis and Uncertainty Quantification: Review and New Developments |
| title_short | High-Order Deterministic Sensitivity Analysis and Uncertainty Quantification: Review and New Developments |
| title_sort | high order deterministic sensitivity analysis and uncertainty quantification review and new developments |
| topic | six-order moments of model response distribution in parameter phase space third-order comprehensive adjoint sensitivity analysis methodology for nonlinear systems |
| url | https://www.mdpi.com/1996-1073/14/20/6715 |
| work_keys_str_mv | AT dangabrielcacuci highorderdeterministicsensitivityanalysisanduncertaintyquantificationreviewandnewdevelopments |