High-Fidelity Multi-Physics Guidelines for Model Validation and Uncertainty Quantification

Gregory Kyriakos Delipei^*Quentin FaureMaria AvramovaKostadin Ivanov

• North Carolina State University, Raleigh, United States

Verification, validation, and uncertainty quantification (VVUQ) of high-fidelity, high-resolution multi-physics modeling and simulation in nuclear engineering applications are essential for assessing the predictive credibility of developed models. Appropriate practices and methods are required to address ongoing challenges. Some key examples include the large dimensionality of the input and output spaces, the modeling complexity, the high computational cost, the scarcity of relevant experimental data, and the lack of guidelines and protocols for the development of multi-physics benchmarks. In this work, several guidelines and recommendations are provided. Dimensionality reduction and screening approaches can be used to address the high-dimensional input and output spaces. To manage modeling complexity, a multi-level validation hierarchy is suggested, where the coupling level is increased progressively. A validation scoring method is proposed to compare the different coupling levels and to identify gaps in the modeling. Regarding the computational cost, surrogate models can be used, though they require the estimation of an additional model uncertainty. For consistent uncertainty propagation, sample-processing diagrams are introduced that can help avoid sampling errors between the multiple inputs. For the validation of multivariate outputs, such as time series, local, regional, and global univariate metrics can be used together with more complicated multivariate methods based on U-pooling. Some of the proposed recommendations are demonstrated on the multi-physics modeling of the first cold ramp test from the OECD/Nuclear Energy Agency (NEA) Multi-physics Pellet Cladding Mechanical Interaction Validation (MPCMIV) benchmark. The multi-level modeling hierarchy ranges from single-physics fuel performance models to coupled multi-physics models. The MOOSE-based tools Griffin, Bison, and THM are employed alongside the fuel performance code OFFBEAT. The measurements considered in this work include the cladding axial elongation and the coolant temperature at three different locations during the cold ramp test. Validation metrics are computed at local, regional, and global scales. Validation scores are computed for each model and physics domain. The results showcase the need for at least a coupling between the RP and FP to accurately predict the cladding axial elongation, whereas the coolant temperatures are less sensitive to the coupling level due to their small variations during the cold ramp test.