@article{khuwaileh_turinsky_2019, title={Non-linear, time dependent target accuracy assessment algorithm for multi-physics, high dimensional nuclear reactor calculations}, volume={114}, ISSN={["0149-1970"]}, DOI={10.1016/j.pnucene.2019.01.023}, abstractNote={Safety analysis and design optimization depend on the accurate prediction of various reactor core responses. Model predictions can be enhanced by reducing the uncertainty associated with the responses of interest. Accordingly, an inverse problem analysis can be designed to provide guidance to determine the optimum experimental program to reduce the uncertainties in model parameters, e.g. cross-sections and fuel pellet-clad thermal conductivity, so as to reduce the uncertainties in constrained reactor core responses. This process is referred to as target accuracy assessment. In this work a nonlinear algorithm to determine an optimum experimental program has been developed and tested. The algorithm is based on the construction of surrogate model to replace the original model used to predict the core responses and uncertainties, therefore, enabling the target accuracy assessment to treat non-linearity within reasonable computational cost. Subspace based projection techniques are used to identify the influential degrees of freedom, which are then used to construct the surrogate model. Once constructed, the new computationally efficient surrogate model is used to propagate uncertainties via Monte Carlo sampling. Moreover, this work replaces the classical objective function used for nuclear data target accuracy assessment with another that factors in the financial gains of the target accuracy assessment results and replaces [or can supplement] differential experiments with many times more readily available integral experiments. Finally, the proposed algorithm is applied on a 3-dimensional fuel assembly depletion problem with thermal-hydraulics feedback using the VERA-CS core simulator. Specifically, CASL Progression Problem Number 6 is the illustrative problem employed which resembles a pressurized water reactor fuel assembly.}, journal={PROGRESS IN NUCLEAR ENERGY}, author={Khuwaileh, Bassam A. and Turinsky, Paul J.}, year={2019}, month={Jul}, pages={227–233} }
 @article{khuwaileh_williams_turinsky_hartanto_2019, title={Verification of Reduced Order Modeling based Uncertainty/Sensitivity Estimator (ROMUSE)}, volume={51}, ISSN={["1738-5733"]}, DOI={10.1016/j.net.2019.01.020}, abstractNote={This paper presents a number of verification case studies for a recently developed sensitivity/uncertainty code package. The code package, ROMUSE (Reduced Order Modeling based Uncertainty/Sensitivity Estimator) is an effort to provide an analysis tool to be used in conjunction with reactor core simulators, in particular the Virtual Environment for Reactor Applications (VERA) core simulator. ROMUSE has been written in C++ and is currently capable of performing various types of parameter perturbations and associated sensitivity analysis, uncertainty quantification, surrogate model construction and subspace analysis. The current version 2.0 has the capability to interface with the Design Analysis Kit for Optimization and Terascale Applications (DAKOTA) code, which gives ROMUSE access to the various algorithms implemented within DAKOTA, most importantly model calibration. The verification study is performed via two basic problems and two reactor physics models. The first problem is used to verify the ROMUSE single physics gradient-based range finding algorithm capability using an abstract quadratic model. The second problem is the Brusselator problem, which is a coupled problem representative of multi-physics problems. This problem is used to test the capability of constructing surrogates via ROMUSE-DAKOTA. Finally, light water reactor pin cell and sodium-cooled fast reactor fuel assembly problems are simulated via SCALE 6.1 to test ROMUSE capability for uncertainty quantification and sensitivity analysis purposes.}, number={4}, journal={NUCLEAR ENGINEERING AND TECHNOLOGY}, author={Khuwaileh, Bassam and Williams, Brian and Turinsky, Paul and Hartanto, Donny}, year={2019}, month={Jul}, pages={968–976} }
 @article{arbanas_williams_leal_dunn_khuwaileh_wang_abdel-khalik_2015, title={Advancing Inverse Sensitivity/Uncertainty Methods for Nuclear Fuel Cycle Applications}, volume={123}, ISSN={["1095-9904"]}, DOI={10.1016/j.nds.2014.12.009}, abstractNote={The inverse sensitivity/uncertainty quantification (IS/UQ) method has recently been implemented in the Inverse Sensitivity/UnceRtainty Estimator (INSURE) module of the AMPX cross section processing system [M.E. Dunn and N.M. Greene, “AMPX-2000: A Cross-Section Processing System for Generating Nuclear Data for Criticality Safety Applications,” Trans. Am. Nucl. Soc. 86, 118–119 (2002)]. The IS/UQ method aims to quantify and prioritize the cross section measurements along with uncertainties needed to yield a given nuclear application(s) target response uncertainty, and doing this at a minimum cost. Since in some cases the extant uncertainties of the differential cross section data are already near the limits of the present-day state-of-the-art measurements, requiring significantly smaller uncertainties may be unrealistic. Therefore, we have incorporated integral benchmark experiments (IBEs) data into the IS/UQ method using the generalized linear least-squares method, and have implemented it in the INSURE module. We show how the IS/UQ method could be applied to systematic and statistical uncertainties in a self-consistent way and how it could be used to optimize uncertainties of IBEs and differential cross section data simultaneously. We itemize contributions to the cost of differential data measurements needed to define a realistic cost function.}, journal={NUCLEAR DATA SHEETS}, author={Arbanas, G. and Williams, M. L. and Leal, L. C. and Dunn, M. E. and Khuwaileh, B. A. and Wang, C. and Abdel-Khalik, H.}, year={2015}, month={Jan}, pages={51–56} }
 @article{khuwaileh_abdel-khalik_2015, title={Subspace-based Inverse Uncertainty Quantification for Nuclear Data Assessment}, volume={123}, ISSN={["1095-9904"]}, DOI={10.1016/j.nds.2014.12.010}, abstractNote={Safety analysis and design optimization depend on the accurate prediction of various reactor attributes. Predictions can be enhanced by reducing the uncertainty associated with the attributes of interest. An inverse problem can be defined and solved to assess the sources of uncertainty, and experimental effort can be subsequently directed to further improve the uncertainty associated with these sources. In this work a subspace-based algorithm for inverse sensitivity/uncertainty quantification (IS/UQ) has been developed to enable analysts account for all sources of nuclear data uncertainties in support of target accuracy assessment-type analysis. An approximate analytical solution of the optimization problem is used to guide the search for the dominant uncertainty subspace. By limiting the search to a subspace, the degrees of freedom available for the optimization search are significantly reduced. A quarter PWR fuel assembly is modeled and the accuracy of the multiplication factor and the fission reaction rate are used as reactor attributes whose uncertainties are to be reduced. Numerical experiments are used to demonstrate the computational efficiency of the proposed algorithm. Our ongoing work is focusing on extending the proposed algorithm to account for various forms of feedback, e.g., thermal-hydraulics and depletion effects.}, journal={NUCLEAR DATA SHEETS}, author={Khuwaileh, B. A. and Abdel-Khalik, H. S.}, year={2015}, month={Jan}, pages={57–61} }