Surrogate Modeling and Global Sensitivity Analysis towards Efficient Simulation of Nuclear Reactor Stochastic Dynamics

Surrogate modeling is used to support global sensitivity analyses (GSA) for the modeling and simulation of nuclear reactor assembly structural dynamics to demonstrate the pertinence of such methods to this application as well as the significant physical insights provided by GSA. In addition to the knowledge gained related to the system sensitivity, insight gained from the accuracy of the GSA results may be used to compare with goodness-of-fit metrics that are traditionally used for verification of the surrogate model. The coupled use of surrogate modeling and GSA reduces the number of full-order simulations required, substantially reducing total computational cost. This work focuses on the use of Gaussian Process surrogates in particular, and examines the robustness of these techniques to evaluate sensitivity by considering a variety of design of experiment strategies used to create the surrogate models. Numerical experiments based upon two finite element models representing stochastic dynamics for a pressurized water reactor, are used to evaluate the relationship between sensitivities computed from a full-order model versus those computed from a surrogate model, highlighting the effectiveness of utilizing GSA and surrogate modeling. For the examples presented herein the historical significance of both forcing function characterization and model parameter definition is substantiated, in terms of the GSA providing insight as to dominant contributors to structural dynamic behavior. For large sample sizes, negligible variation in the resultant sensitivities is shown with respect to the particular method by which a computational design of experiment is constructed to train the surrogates, that demonstrates stability and veracity of the results. For small sample sizes, the use of Latinized Partially Stratified Sampling (LPSS) provided surrogates and associated sensitivities with lower error as compared to Latin Hypercube Sampling (LHS) and sampling via the Fourier Amplitude Sensitivity Test (FAST). Differences in GSA results imparted by examining time-domain versus spectral acceleration results, as well as increasing model parameter variation further illustrated the effectiveness of advanced sampling methods. Furthermore, the use of adaptive sampling and aggregate surrogate modeling techniques are introduced, with which incremental improvements were realized regarding the number of samples required to achieve accurate surrogate models

Efficient Global Sensitivity Analysis of Structural Vibration for a Nuclear Reactor System Subject to Nonstationary Loading

The structures associated with the nuclear steam supply system (NSSS) of a pressurized water reactor (PWR) include significant epistemic and aleatory uncertainties in the physical parameters, while also being subject to various non-stationary stochastic loading conditions over the life of a nuclear power plant. To understand the influence of these uncertainties on nuclear reactor systems, sensitivity analysis must be performed. This work evaluates computational design of experiment strategies, which execute a nuclear reactor equipment system finite element model to train and verify Gaussian Process (GP) surrogate models. The surrogate models are then used to perform both global and local sensitivity analyses. The significance of the sensitivity analysis for efficient modeling and simulation of nuclear reactor stochastic dynamics is discussed

Ultrafast 2D-IR and optical Kerr effect spectroscopy reveal the impact of duplex melting on the structural dynamics of DNA

Changes in the structural and solvation dynamics of a 15mer AT DNA duplex upon melting of the double-helix are observed by a combination of ultrafast two-dimensional infrared (2D-IR) and optical Kerr-effect (OKE) spectroscopies. 2D-IR spectroscopy of the vibrational modes of the DNA bases reveal signature off-diagonal peaks arising from coupling and energy transfer across Watson-Crick paired bases that are unique to double-stranded DNA (ds-DNA). Spectral diffusion of specific base vibrational modes report on the structural dynamics of the duplex and the minor groove, which is predicted to contain a spine of hydration. Changes in these dynamics upon melting are assigned to increases in the degree of mobile solvent access to the bases in single-stranded DNA (ss-DNA) relative to the duplex. OKE spectra exhibit peaks that are assigned to specific long-range phonon modes of ds- and ss-DNA. Temperature-related changes in these features correlate well with those obtained from the 2D-IR spectra although the melting temperature of the ds-DNA phonon band is slightly higher than that for the Watson-Crick modes, suggesting that a degree of long-range duplex structure survives the loss of Watson-Crick hydrogen bonding. These results demonstrate that the melting of ds-DNA disrupts helix-specific structural dynamics encompassing length scales ranging from mode delocalisation in the Watson-Crick base pairs to long-range phonon modes that extend over multiple base pairs and which may play a role in molecular recognition of DNA

Efficient global sensitivity analysis for flow-induced vibration of a nuclear reactor assembly using Kriging surrogates

In this work, surrogate modeling is used to support a global sensitivity analysis (GSA) for a nuclear reactor assembly as a proof-of-concept to demonstrate both the pertinence of such methods to this application as well as the significant physical insights provided by GSA. In addition to the knowledge gained relating to the system sensitivity, insight gained from the accuracy of the GSA results may be used to compare with goodness-of-fit metrics which are traditionally used to support the verification of the surrogate model. The coupled use of surrogate modeling and GSA reduces the number of full-order (i.e., standard computationally expensive finite element analysis) simulations required, substantially reducing total computational cost. This work focuses on the use of Kriging surrogates in particular, and examines the robustness of these techniques to evaluate sensitivity by considering a variety of design of experiment strategies used to create the surrogate models. Numerical experiments based upon an inverted top-hat upper internals assembly of a pressurized water reactor subjected to base motion and fluctuating lift and drag cross-flow loadings are used to evaluate the relationship between sensitivities computed from a full-order model versus those computed from a surrogate model, highlighting the effectiveness of utilizing GSA and surrogate modeling. For large sample sizes, negligible variation in the resultant sensitivities is shown with respect to the particular method by which a computational design of experiment is constructed to train the Kriging surrogates which lends credence to the stability and veracity of the results. Additionally, for the example presented herein the historical significance of the downcomer forcing function characterization is substantiated in the sense that loads from the downcomer which act indirectly on the upper internals are shown to dominate the response relative to direct-applied cross-flow loads