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The Handbook of Nuclear Engineering is an authoritative compilation of information regarding methods and data used in all phases of nuclear engineering. Addressing nuclear engineers and scientists at all academic levels, this five volume set provides the latest findings in nuclear data and experimental techniques, reactor physics, kinetics, dynamics and control. Readers will also find a detailed description of data assimilation, model validation and calibration, sensitivity and uncertainty analysis, fuel management and cycles, nuclear reactor types and radiation shielding. A...

The author has achieved the breakthrough of generalizing the First-Order Theory presented in his previous books, to the efficient computations of arbitrarily high-order sensitivities for nonlinear systems (HONASAP). This breakthrough has many applications, especially when there is a need to quantify nonlinear behavior or to quantify uncertainties in design parameter/system responses in large-scale systems. This book presents the theory of the HONASAP with applications, from simple, analytically solvable, paradigm problems to large-scale applications in thermal hydraulics, particle...

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 model’s responses can be either scalar-valued functionals of the model’s parameters and state variables (as customarily encountered, e.g., in optimization problems) or general function-valued responses, which are often of interest but are currently not amenable to efficient sensitivity analysis. The C-ASAM framework...

The computational models of physical systems comprise parameters, independent and dependent variables. Since the physical processes themselves are seldom known precisely and since most of the model parameters stem from experimental procedures which are also subject to imprecisions, the results predicted by these models are also imprecise, being affected by the uncertainties underlying the respective model. The functional derivatives (also called “sensitivities”) of results (also called “responses”) produced by mathematical/computational models are needed for many purposes,...