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The n th -Order Comprehensive Adjoint Sensitivity Analysis Methodology for Response-Coupled Forward/Adjoint Linear Systems (n th -CASAM-L): I. Mathematical Framework

Author

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  • Dan Gabriel Cacuci

    (Department of Mechanical Engineering, College of Engineering and Computing, University of South Carolina, Columbia, SC 29208, USA)

Abstract

This work presents the mathematical framework of the n th -Order Comprehensive Adjoint Sensitivity Analysis Methodology for Response-Coupled Forward/Adjoint Linear Systems (abbreviated as “n th -CASAM-L”), which is conceived for obtaining the exact expressions of arbitrarily-high-order (n th -order) sensitivities of a generic system response with respect to all of the parameters (including boundary and initial conditions) underlying the respective forward/adjoint systems. Since many of the most important responses for linear systems involve the solutions of both the forward and the adjoint linear models that correspond to the respective physical system, the sensitivity analysis of such responses makes it necessary to treat linear systems in their own right, rather than treating them as particular cases of nonlinear systems. This is in contradistinction to responses for nonlinear systems, which can depend only on the forward functions, since nonlinear operators do not admit bona-fide adjoint operators (only a linearized form of a nonlinear operator admits an adjoint operator). The n th -CASAM-L determines the exact expression of arbitrarily-high order sensitivities of responses to the parameters underlying both the forward and adjoint models of a nonlinear system, thus enable the most efficient and accurate computation of such sensitivities. The mathematical framework underlying the n th -CASAM is developed in linearly increasing higher-dimensional Hilbert spaces, as opposed to the exponentially increasing “parameter-dimensional” spaces in which response sensitivities are computed by other methods, thus providing the basis for overcoming the “curse of dimensionality” in sensitivity analysis and all other fields (uncertainty quantification, predictive modeling, etc.) which need such sensitivities. In particular, for a scalar-valued valued response associated with a nonlinear model comprising TP parameters, the 1 st - − CASAM-L requires 1 additional large-scale adjoint computation (as opposed to TP large-scale computations, as required by other methods) for computing exactly all of the 1 st - − order response sensitivities. All of the (mixed) 2 nd -order sensitivities are computed exactly by the 2 nd -CASAM-L in at most TP computations, as opposed to TP(TP + 1)/2 computations required by all other methods, and so on. For every lower-order sensitivity of interest, the n th -CASAM-L computes the “TP next-higher-order” sensitivities in one adjoint computation performed in a linearly increasing higher-dimensional Hilbert space. Very importantly, the n th -CASAM-L computes the higher-level adjoint functions using the same forward and adjoint solvers (i.e., computer codes) as used for solving the original forward and adjoint systems, thus requiring relatively minor additional software development for computing the various-order sensitivities.

Suggested Citation

  • Dan Gabriel Cacuci, 2021. "The n th -Order Comprehensive Adjoint Sensitivity Analysis Methodology for Response-Coupled Forward/Adjoint Linear Systems (n th -CASAM-L): I. Mathematical Framework," Energies, MDPI, vol. 14(24), pages 1-42, December.
  • Handle: RePEc:gam:jeners:v:14:y:2021:i:24:p:8314-:d:699125
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    References listed on IDEAS

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    1. Dan G. Cacuci & Ruixian Fang & Jeffrey A. Favorite, 2020. "Comprehensive Second-Order Adjoint Sensitivity Analysis Methodology (2nd-ASAM) Applied to a Subcritical Experimental Reactor Physics Benchmark. VI: Overall Impact of 1st- and 2nd-Order Sensitivities o," Energies, MDPI, vol. 13(7), pages 1-37, April.
    2. Ruixian Fang & Dan Gabriel Cacuci, 2020. "Comprehensive Second-Order Adjoint Sensitivity Analysis Methodology (2nd-ASAM) Applied to a Subcritical Experimental Reactor Physics Benchmark: IV. Effects of Imprecisely Known Source Parameters," Energies, MDPI, vol. 13(6), pages 1-49, March.
    3. Ruixian Fang & Dan G. Cacuci, 2020. "Comprehensive Second-Order Adjoint Sensitivity Analysis Methodology (2nd-ASAM) Applied to a Subcritical Experimental Reactor Physics Benchmark: V. Computation of Mixed 2nd-Order Sensitivities Involvin," Energies, MDPI, vol. 13(10), pages 1-50, May.
    4. Dan Gabriel Cacuci, 2021. "Fourth-Order Comprehensive Adjoint Sensitivity Analysis (4th-CASAM) of Response-Coupled Linear Forward/Adjoint Systems: I. Theoretical Framework," Energies, MDPI, vol. 14(11), pages 1-45, June.
    5. D. G. Cacuci & R. Fang & J. A. Favorite & M. C. Badea & F. Di Rocco, 2019. "Comprehensive Second-Order Adjoint Sensitivity Analysis Methodology (2nd-ASAM) Applied to a Subcritical Experimental Reactor Physics Benchmark: III. Effects of Imprecisely Known Microscopic Fission Cr," Energies, MDPI, vol. 12(21), pages 1-67, October.
    6. Dan G. Cacuci & Ruixian Fang & Jeffrey A. Favorite, 2019. "Comprehensive Second-Order Adjoint Sensitivity Analysis Methodology (2nd-ASAM) Applied to a Subcritical Experimental Reactor Physics Benchmark: I. Effects of Imprecisely Known Microscopic Total and Ca," Energies, MDPI, vol. 12(21), pages 1-43, November.
    7. Ruixian Fang & Dan Gabriel Cacuci, 2019. "Comprehensive Second-Order Adjoint Sensitivity Analysis Methodology (2nd-ASAM) Applied to a Subcritical Experimental Reactor Physics Benchmark: II. Effects of Imprecisely Known Microscopic Scattering ," Energies, MDPI, vol. 12(21), pages 1-33, October.
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    Cited by:

    1. Dan Gabriel Cacuci, 2023. "Computation of High-Order Sensitivities of Model Responses to Model Parameters—I: Underlying Motivation and Current Methods," Energies, MDPI, vol. 16(17), pages 1-31, September.
    2. Dan Gabriel Cacuci, 2021. "The n th -Order Comprehensive Adjoint Sensitivity Analysis Methodology for Response-Coupled Forward/Adjoint Linear Systems (n th -CASAM-L): II. Illustrative Application," Energies, MDPI, vol. 14(24), pages 1-49, December.
    3. Dan Gabriel Cacuci, 2022. "Overview of Arbitrarily High-Order Adjoint Sensitivity and Uncertainty Quantification Methodology for Large-Scale Systems," Energies, MDPI, vol. 15(18), pages 1-44, September.
    4. Dan Gabriel Cacuci, 2022. "Sensitivity Analysis, Uncertainty Quantification and Predictive Modeling of Nuclear Energy Systems," Energies, MDPI, vol. 15(17), pages 1-7, September.
    5. Dan Gabriel Cacuci, 2022. "Advances in High-Order Sensitivity Analysis for Uncertainty Quantification and Reduction in Nuclear Energy Systems," Energies, MDPI, vol. 15(17), pages 1-6, September.

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