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A strong correlation exists between subcooled boiling in assembly subchannels and CRUD deposition. In this work a genetic algorithm is used to optimize a 17 x 17 PWR fuel assembly to have minimized subcooled boiling, minimized peak kinf, and maximized end of cycle kinf. Optimization of these parameters act as a surrogate for the optimization of CRUD deposition in fuel assemblies due to their strong correlation. Subcooled boiling, measured by vapor void in a sub channel, and values of kinf, are calculated using VERA-CS. Due to the high computational cost of VERA-CS, artificial neural networks are used as surrogate models to VERA-CS in order for the optimization to be performed in a timely manner making design work possible. Two neural networks were trained using a training library of 1200 randomly generated assembly designs and a validation library of 100 assembly designs evaluated using VERA-CS. The combination of neural networks and genetic algorithms formed an extremely fast optimization algorithm capable of evaluating designed a set of optimized pin lattices in a matter of minutes. The optimization showed a clear reduction in vapor void in the optimization solutions. This provides a proof of principle that complex phenomena requiring coupled, Multiphysics calculations, such as CRUD deposition, may be optimized.

Accurate reactor core steady state safety analysis requires coupling between thermal-hydraulics and three dimensional multigroup pin by pin neutronics. Concerning the neutronics modeling, the Nodal Expansion Method (NEM) code is developed at North Carolina State University in the framework of high fidelity multiphysics coupling with CTF. NEM includes a simplified third-order Spherical Harmonic (SP3) solver. In this work, the solver has been improved by incorporating higher order scattering matrix library. The boundary conditions were corrected with one dimensional P3 theory and a consistent coupling coupling between zeroth- and second-order flux moments was established. Two methods for generating second order discontinuity factors (DFs) has ben developed, one based on the Generalized Equivalence Theory (GET) and one based on Parial Current Equivalence Theory (PCET). DFs were generated with three lattice sizes: single pin, 2 pins and assembly level. These developments were tested using the C5G7 benchmark. The results of the SP3 solver improvement, by using P2 and P3 scattering cross sections, show a 50% decrease in the eigenvalue (keff) prediction error compared to the reference transport solution. The GET DFs are applied in the C5G7 core pin by pin calculation and are compared with PCET DFs. The results show that PCET have a better performance in global results (eigenvalue). Concerning the different lattice sizes studies, the results show that DFs generated in smalll colorsets can improve local solutions. However, in order to reveal strong global trends, DFs should be generated in a larger corloset representative of the whole core. For the core calculations, DFs generated with the three colorsets together with an additional mixed type DFs were tested. For the mixed type, DFs generated from assembly size lattice were used for the internal interfaces and DFs generated from 2 pins size lattice were used for the assemblies boundary interfaces. These mixed DFs outperformed all the other configurations indicating that they manage to accomplish a satisfying compromise between global and local trends.

State-of-the-art core nodal diffusion calculation involves the use of assembly discontinuity factor (ADF) to improve computational accuracy by introducing degree of freedom describing the relationship between interfacial discontinuities in nodal calculation [1]. The form of ADF known as the Flux based ADF (FDF) generated from flux information is recommended in the conventional two-level core calculation scheme. The multi-group cross-sections were generated using SCALE 6.2 NEWT and verified with KENO-VI [2]. A lattice module has been created for the high-fidelity neutron transport code MOCEX [3] to generate the group constants and side-independent ADFs. This new capability is verified against the reference code SCALE 6.2 NEWT under both serial and parallel modes. The implementation of ADF is performed in this work and further verified by comparing core keff. The calculation results show that the newly implemented ADF module consistently improved the accuracy of the PROTEUS-NODAL (NODAL) diffusion solver, which will become an affordable candidate for the following research of High-to-Low (Hi2Lo) transport-to-diffusion informing scheme [4].