The nth-order comprehensive adjoint sensitivity analysis methodology. Volume II, Overcoming the curse of dimensionality : large-scale application / Dan Gabriel Cacuci, Ruixian Fang.
Cacuci, Dan Gabriel, author.; Fang, Ruixian (Mechanical engineer), author.
2023
QA402.3 .C33 2023
Available Online

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Title The nth-order comprehensive adjoint sensitivity analysis methodology. Volume II, Overcoming the curse of dimensionality : large-scale application / Dan Gabriel Cacuci, Ruixian Fang.
Author Cacuci, Dan Gabriel, author.
ISBN 9783031196355 electronic book
303119635X electronic book
9783031196348
3031196341
DOI https://doi.org/10.1007/978-3-031-19635-5
Published Cham, Switzerland : Springer, [2023]
Language English
Description 1 online resource (xvii, 463 pages) : illustrations
Other Standard Identifiers 10.1007/978-3-031-19635-5 doi
Call Number QA402.3 .C33 2023
Dewey Decimal Classification 003/.5
Summary This text describes a comprehensive adjoint sensitivity analysis methodology (C-ASAM), developed by the author, enabling the efficient and exact computation of arbitrarily high-order functional derivatives of model responses to model parameters in large-scale systems. The C-ASAM framework is set in linearly increasing Hilbert spaces, each of state-function-dimensionality, as opposed to exponentially increasing parameter-dimensional spaces, thereby breaking the so-called curse of dimensionality in sensitivity and uncertainty analysis. The C-ASAM applies to any model; the larger the number of model parameters, the more efficient the C-ASAM becomes for computing arbitrarily high-order response sensitivities. The book will be helpful to those working in the fields of sensitivity analysis, uncertainty quantification, model validation, optimization, data assimilation, model calibration, sensor fusion, reduced-order modelling, inverse problems and predictive modelling. This Volume Two, the second of three, presents the large-scale application of C-ASAM to compute exactly the first-, second-, third-, and fourth-order sensitivities of the Polyethylene-Reflected Plutonium (PERP) OECD/NEO international benchmark which is modeled mathematically by the Boltzmann particle transport equation. It follows from the description of the C-ASAM framework applied to linear systems in Volume One where the PERP benchmark's response of interest is the leakage of particles through its outer boundary. The benchmark represents the largest sensitivity analysis endeavor ever carried out in the field of reactor physics and the numerical results shown in this book prove, for the first time ever, that many of the second-order sensitivities are much larger than the corresponding first-order ones. Currently, the nth-CASAM is the only known methodology which enables such large-scale computations of the exact expressions and values of the nth-order response sensitivities.
Bibliography, etc. Note Includes bibliographical references and index.
Access Note Access limited to authorized users.
Source of Description Description based on online resource; title from digital title page (viewed on May 25, 2023).
Added Author Fang, Ruixian (Mechanical engineer), author.
Available in Other Form Print version: 9783031196348
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Chapter1. 1st-Order Sensitivity Analysis of the OECD/NEA PERP Reactor Physics Benchmark
Chapter2. 2nd-Order Sensitivities of the PERP Benchmark to the Microscopic Total and Capture Cross Sections
Chapter3. 2nd-Order Sensitivities of the PERP Benchmark to the Microscopic Scattering Cross Sections
Chapter4. 2nd-Order Sensitivities of the PERP Benchmark to the Microscopic Fission Cross Sections
Chapter5. 2nd-Order Sensitivities of the PERP Benchmark to the Average Number of Neutrons per Fission
Chapter6. 2nd-Order Sensitivities of the PERP Benchmark to the Spontaneous Fission Source Parameters
Chapter7. 2nd-Order Sensitivities of the PERP Benchmark to the Isotopic Number Densities
Chapter8. 3rd-Order Sensitivities of the PERP Benchmark
Chapter9. 4th-Order Sensitivities of the PERP Benchmark
Chapter10. Overall Impact of 1st-, 2nd-, 3rd-, and 4th-Order Sensitivities on the PERP Benchmark's Response Uncertainties.

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