Abstract
The work presented in this thesis is a continuation of a master?s thesis research project conducted by the author to gain insight into the applicability of inverse methods to developing adaptive simulation capabilities for core physics problems. Use of adaptive simulation is intended to improve the fidelity and robustness of important core attributes predictions such as core power distribution, thermal margins and core reactivity. Adaptive simulation utilizes a selected set of past and current reactor measurements of reactor observables to adapt the simulation in a meaningful way that is reflected in higher fidelity and robustness of the adapted core simulators models. We propose an inverse theory approach in which the multitudes of input data to core simulators, i.e. reactor physics and thermal-hydraulic data, are to be adjusted to improve agreement with measured observables while keeping core simulators models unadapted. At a first glance, devising such adaption for typical core simulators models would render the approach impractical. This follows, since core simulators are based on very demanding computational models, i.e. based on complex physics models with millions of input data and output observables. This would spawn not only several prohibitive challenges but also numerous disparaging concerns. The challenges include the computational burdens of the sensitivity-type calculations required to construct Jacobian operators for the core simulators models. Also, the computational burdens of the uncertainty-type calculations required to estimate the uncertainty information of core simulators input data presents a demanding challenge. The concerns however are mainly related to the reliability of the adjusted input data. We demonstrate that the power of our proposed approach is mainly driven by taking advantage of this unfavorable situation and show that significant reductions in both computational and storage burdens can be attained for a typical BWR core simulator adaption problem without compromising the quality of the adaption.
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