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RISS 인기검색어
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- 저자 : Bassam Khuwaileh (검색결과 3 건)
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Verification of Reduced Order Modeling Based Uncertainty/Sensitivity Estimator (ROMUSE)
Bassam Khuwaileh,Brian Williams,Paul Turinsky,Donny Hartanto 한국원자력학회 2019 Nuclear Engineering and Technology Vol.51 No.4
This paper presents a number of verification case studies for a recently developed sensitivity/uncertaintycode package. The code package, ROMUSE (Reduced Order Modeling based Uncertainty/SensitivityEstimator) 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 beenwritten in Cþþ and is currently capable of performing various types of parameter perturbations andassociated sensitivity analysis, uncertainty quantification, surrogate model construction and subspaceanalysis. The current version 2.0 has the capability to interface with the Design Analysis Kit for Optimizationand Terascale Applications (DAKOTA) code, which gives ROMUSE access to the various algorithmsimplemented within DAKOTA, most importantly model calibration. The verification study isperformed via two basic problems and two reactor physics models. The first problem is used to verify theROMUSE single physics gradient-based range finding algorithm capability using an abstract quadraticmodel. The second problem is the Brusselator problem, which is a coupled problem representative ofmulti-physics problems. This problem is used to test the capability of constructing surrogates viaROMUSE-DAKOTA. Finally, light water reactor pin cell and sodium-cooled fast reactor fuel assemblyproblems are simulated via SCALE 6.1 to test ROMUSE capability for uncertainty quantification andsensitivity analysis purposes
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Surrogate based model calibration for pressurized water reactor physics calculations
Bassam A. Khuwaileh,PAUL J. TURINSKY 한국원자력학회 2017 Nuclear Engineering and Technology Vol.49 No.6
In this work, a scalable algorithm for model calibration in nuclear engineering applications is presentedand tested. The algorithm relies on the construction of surrogate models to replace the original modelwithin the region of interest. These surrogate models can be constructed efficiently via reduced ordermodeling and subspace analysis. Once constructed, these surrogate models can be used to performcomputationally expensive mathematical analyses. This work proposes a surrogate based model calibrationalgorithm. The proposed algorithm is used to calibrate various neutronics and thermal-hydraulicsparameters. The virtual environment for reactor applications-core simulator (VERA-CS) is used tosimulate a three-dimensional core depletion problem. The proposed algorithm is then used to constructa reduced order model (a surrogate) which is then used in a Bayesian approach to calibrate the neutronicsand thermal-hydraulics parameters. The algorithm is tested and the benefits of data assimilationand calibration are highlighted in an uncertainty quantification study and requantification after thecalibration process. Results showed that the proposed algorithm could help to reduce the uncertainty inkey reactor attributes based on experimental and operational data.
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Gaussian process approach for dose mapping in radiation fields
Khuwaileh Bassam A.,Metwally Walid A. 한국원자력학회 2020 Nuclear Engineering and Technology Vol.52 No.8
In this work, a Gaussian Process (Kriging) approach is proposed to provide efficient dose mapping for complex radiation fields using limited number of responses. Given a few response measurements (or simulation data points), the proposed approach can help the analyst in completing a map of the radiation dose field with a 95% confidence interval, efficiently. Two case studies are used to validate the proposed approach. The First case study is based on experimental dose measurements to build the dose map in a radiation field induced by a D-D neutron generator. The second, is a simulation case study where the proposed approach is used to mimic Monte Carlo dose predictions in the radiation field using a limited number of MCNP simulations. Given the low computational cost of constructing Gaussian Process (GP) models, results indicate that the GP model can reasonably map the dose in the radiation field given a limited number of data measurements. Both case studies are performed on the nuclear engineering radiation laboratories at the University of Sharjah
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