@article{jessee_turinsky_abdel-khalik_2011, title={Many-Group Cross-Section Adjustment Techniques for Boiling Water Reactor Adaptive Simulation}, volume={169}, ISSN={["0029-5639"]}, DOI={10.13182/nse09-67}, abstractNote={Abstract Computational capability has been developed to adjust multigroup neutron cross sections, including self-shielding correction factors, to improve the fidelity of boiling water reactor (BWR) core modeling and simulation. The method involves propagating multigroup neutron cross-section uncertainties through various BWR computational models to evaluate uncertainties in key core attributes such as core keff, nodal power distributions, thermal margins, and in-core detector readings. Uncertainty-based inverse theory methods are then employed to adjust multigroup cross sections to minimize the disagreement between BWR core modeling predictions and observed (i.e., measured) plant data. For this paper, observed plant data are virtually simulated in the form of perturbed three-dimensional nodal power distributions with the perturbations sized to represent actual discrepancies between predictions and real plant data. The major focus of this work is to efficiently propagate multigroup neutron cross-section uncertainty through BWR lattice physics and core simulator calculations. The data adjustment equations are developed using a subspace approach that exploits the ill-conditioning of the multigroup cross-section covariance matrix to minimize computation and storage burden. Tikhonov regularization is also employed to improve the conditioning of the data adjustment equations. Expressions are also provided for posterior covariance matrices of both the multigroup cross-section and core attributes uncertainties.}, number={1}, journal={NUCLEAR SCIENCE AND ENGINEERING}, author={Jessee, M. A. and Turinsky, P. J. and Abdel-Khalik, H. S.}, year={2011}, month={Sep}, pages={40–55} }
 @article{abdel-khalik_turinsky_jessee_2008, title={Efficient subspace methods-based algorithms for performing sensitivity, uncertainty, and adaptive simulation of large-scale computational models}, volume={159}, ISSN={["1943-748X"]}, DOI={10.13182/NSE159-256}, abstractNote={Abstract This paper introduces the concepts and derives the mathematical theory of efficient subspace methods (ESMs) applied to the simulation of large-scale complex models, of which nuclear reactor simulation will serve as a test basis. ESMs are intended to advance the capabilities of predictive simulation to meet the functional requirements of future energy system simulation and overcome the inadequacies of current design methods. Some of the inadequacies addressed by ESM include lack of rigorous approach to perform comprehensive validation of the multitudes of models and input data used in the design calculations and lack of robust mathematical approaches to enhance fidelity of existing and advanced computational codes. To accomplish these tasks, the computational tools must be capable of performing the following three applications with both accuracy and efficiency: (a) sensitivity analysis of key system attributes with respect to various input data; (b) uncertainty quantification for key system attributes; and (c) adaptive simulation, also known as data assimilation, for adapting existing models based on the assimilated body of experimental information to achieve the best possible prediction accuracy. These three applications, involving large-scale computational models, are now considered computationally infeasible if both the input data and key system attributes or experimental information fields are large. This paper will develop the mathematical theory of ESM-based algorithms for these three applications. The treatment in this paper is based on linearized approximation of the associated computational models. Extension to higher-order approximations represents the focus of our ongoing research.}, number={3}, journal={NUCLEAR SCIENCE AND ENGINEERING}, author={Abdel-Khalik, Hany S. and Turinsky, Paul J. and Jessee, Matthew A.}, year={2008}, month={Jul}, pages={256–272} }
 @article{abdel-khalik_turinsky_jessee_elkins_stover_iqbal_2008, title={Uncertainty Quantification, Sensitivity Analysis, and Data Assimilation for Nuclear Systems Simulation}, volume={109}, ISSN={["0090-3752"]}, DOI={10.1016/j.nds.2008.11.010}, abstractNote={Reliable evaluation of nuclear data will play a major role in reduction of nuclear systems simulation uncertainties via the use of advanced sensitivity analysis (SA), uncertainty quantification (UQ), and data assimilation (DA) methodologies. This follows since nuclear data have proven to constitute a major source of neutronics uncertainties. This paper will overview the use of the Efficient Subspace Method (ESM), developed at NCSU, to overcome one of the main deficiencies of existing methodologies for SA/UQ/DA, that is the ability to handle codes with large input and output (I/O) data streams, where neither the forward nor the adjoint approach alone are appropriate. We demonstrate the functionality of ESM for an LWR core, a boiling water reactor, and a fast reactor benchmark experiment, the ZPR6/7A assembly. This work demonstrates the capability of adjusting cross section data thereby providing guidance to cross section evaluation efforts by identification of key cross sections and associated energy ranges that contribute the most to the propagated core attributes uncertainties.}, number={12}, journal={NUCLEAR DATA SHEETS}, author={Abdel-Khalik, H. and Turinsky, P. and Jessee, M. and Elkins, J. and Stover, T. and Iqbal, M.}, year={2008}, month={Dec}, pages={2785–2790} }
 @article{jessee_kropaczek_2007, title={Coupled bundle-core design using fuel rod optimization for boiling water reactors}, volume={155}, ISSN={["1943-748X"]}, DOI={10.13182/NSE07-A2670}, abstractNote={Abstract An optimization method has been developed to determine the optimal fresh fuel rod configurations, fresh streams, and fresh bundle design placements given a known exposed fuel loading pattern and operational strategy for boiling water reactors. The optimization method is based on a first-order approximation of various core parameters, such as hot excess reactivity and critical power ratio, using fuel rod perturbations to the reference fresh bundle designs. A simulated annealing optimization algorithm is shown to produce fresh bundle designs, consisting of rods selected from a user-defined set of rod types that optimize the core design with respect to its design constraints. The method utilizes a linear superposition method based upon sensitivity coefficients to approximate core parameters. A parallel computing system was implemented to decrease wall clock time for the numerous lattice physics and core simulator calculations. A periodic update of the reference bundle design, without the computational burden of updating the sensitivity coefficients, was introduced and is shown to significantly improve the accuracy of the approximation model. Application of the method demonstrates that improved core designs are achieved when a many-fresh bundle design (i.e., stream) solution is considered as part of the design space. Six-stream (and higher) core designs that increase fuel utilization while simultaneously reducing manufacturing costs through reduction of fuel rod types fabricated, previously unattainable with existing methodologies, are now possible.}, number={3}, journal={NUCLEAR SCIENCE AND ENGINEERING}, author={Jessee, Matthew A. and Kropaczek, David J.}, year={2007}, month={Mar}, pages={378–385} }