Cross-Section Adjustment Techniques for BWR Adaptive Simulation
Cross-Section Adjustment Techniques for BWR Adaptive Simulation

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Published: 2004

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Computational capability has been developed to adjust multi-group neutron cross-sections to improve the fidelity of boiling water reactor (BWR) modeling and simulation. The method involves propagating multi-group neutron cross-section uncertainties through BWR computational models to evaluate uncertainties in key core attributes such as core k-effective, nodal power distributions, thermal margins, and in-core detector readings. Uncertainty-based inverse theory methods are then employed to adjust multi-group cross-sections to minimize the disagreement between BWR modeling predictions and measured plant data. For this work, measured plant data were virtually simulated in the form of perturbed 3-D nodal power distributions with discrepancies with predictions of the same order of magnitude as expected from plant data. Using the simulated plant data, multi-group cross-section adjustment reduces the error in core k-effective to less than 0.2% and the RMS error in nodal power to 4% (i.e. -- the noise level of the in-core instrumentation). To ensure that the adapted BWR model predictions are robust, Tikhonov regularization is utilized to control the magnitude of the cross-section adjustment. In contrast to few-group cross-section adjustment, which was the focus of previous research on BWR adaptive simulation, multi-group cross-section adjustment allows for future fuel cycle design optimization to include the determination of optimal fresh fuel assembly designs using the adjusted multi-group cross-sections. The major focus of this work is to efficiently propagate multi-group neutron cross-section uncertainty through BWR lattice physics calculations. Basic neutron cross-section uncertainties are provided in the form of multi-group cross-section covariance matrices. For energy groups in the resolved resonance energy range, the cross-section uncertainties are computed using an infinitely-dilute approximation of the neutron flux. In order to accurately account for spatial a.

Inverse Method Applied to Adaptive Core Simulation
Inverse Method Applied to Adaptive Core Simulation

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Published: 2002

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The work presented in this thesis is a part of an ongoing research project conducted to gain insight into the applicability of inverse methods to developing adaptive simulation capabilities for core physics problems. Adaptive simulation is a simulation that utilizes past and current reactor measurements of reactor observables (e.g. core reactivity and incore instrumentation readings) to adapt the simulation in a meaningful way to improve agreement with reactor observables. To perform such adaption, we utilize a group of mathematical techniques which address the problem of given a current core simulator model and the associated input data (e.g. cross-sections, thermal-hydraulic parameters), how should the values of selected input data be adjusted to improve agreement with observables without changing the core simulator model, (i.e. how can we obtain the best agreement utilizing our current modeling capability). This is usually referred to as an inverse problem, which is difficult to solve due to its ill-posedness nature. Major advances have been made by mathematicians to overcome the ill-posedness nature of such problems. The proposed project is of an exploratory nature serving to develop expertise in this area, to which the nuclear power community has not participated to any great extent over the last two decades since their earlier contribution during the design, research and developments stages of a proto-typical fast breeder reactor. Exploratory research projects, such as this one, serve to develop insight, form general ideas about areas where little expertise is available, and to provide a basis on whether there is potential for the proposed techniques to be useful and successful. The current work addresses BWR core simulators since their prediction accuracy is inferior to PWRs', providing marginally acceptable agreement between measured and predicted core attributes. This implies that BWRs could benefit from utilizing an adaptive simulation tool. In the work do.