The Spent Fuel and Waste Science and Technology (SFWST) Campaign of the U.S. Department of Energy (DOE) Office of Nuclear Energy (NE), Office of Spent Fuel & Waste Disposition (SFWD) is conducting research and development (R&D) on geologic disposal of spent nuclear fuel (SNF) and high-level nuclear waste (HLW). Two high priorities for SFWST disposal R&D are design concept development and disposal system modeling (DOE 2011, Table 6). These priorities are directly addressed in the SFWST Geologic Disposal Safety Assessment (GDSA) work package, which is charged with developing a disposal system modeling and analysis capability for evaluating disposal system performance for nuclear waste in geologic media.
This report details initial investigations of neural network surrogate models for combustion applications. The models are assessed with respect to averaged predictive fidelity over a test data set as well as with respect to the accuracy of resolving landmark statistics of interest, as a function of increasing training data volume.
Communication networks have evolved to a level of sophistication that requires computer models and numerical simulations to understand and predict their behavior. A network simulator is a software that enables the network designer to model several components of a computer network such as nodes, routers, switches and links and events such as data transmissions and packet errors in order to obtain device and network level metrics. Network simulations, as many other numerical approximations that model complex systems, are subject to the specification of parameters and operative conditions of the system. Very often the full characterization of the system and their input is not possible, therefore Uncertainty Quantification (UQ) strategies need to be deployed to evaluate the statistics of its response and behavior. UQ techniques, despite the advancements in the last two decades, still suffer in the presence of a large number of uncertain variables and when the regularity of the systems response cannot be guaranteed. In this context, multifidelity approaches have gained popularity in the UQ community recently due to their flexibility and robustness with respect to these challenges. The main idea behind these techniques is to extract information from a limited number of high-fidelity model realizations and complement them with a much larger number of a set of lower fidelity evaluations. The final result is an estimator with a much lower variance, i.e. a more accurate and reliable estimator can be obtained. In this contribution we investigate the possibility to deploy multifidelity UQ strategies to computer network analysis. Two numerical configurations are studied based on a simplified network with one client and one server. Preliminary results for these tests suggest that multifidelity sampling techniques might be used as effective tools for UQ tools in network applications.
Two surrogate models are under development to rapidly emulate the effects of the Fuel Matrix Degradation (FMD) model in GDSA Framework. One is a polynomial regression surrogate with linear and quadratic fits, and the other is a k-Nearest Neighbors regressor (kNNr) method that operates on a lookup table. Direct coupling of the FMD model to GDSA Framework is too computationally expensive. Preliminary results indicate these surrogate models will enable GDSA Framework to rapidly simulate spent fuel dissolution for each individual breached spent fuel waste package in a probabilistic repository simulation. This capability will allow uncertainties in spent fuel dissolution to be propagated and sensitivities in FMD inputs to be quantified and ranked against other inputs.
The UQ Toolkit (UQTk) is a collection of libraries and tools for the quantification of uncertainty in numerical model predictions. Version 3.0.4 offers intrusive and non-intrusive methods for propagating input uncertainties through computational models, tools for sensitivity analysis, methods for sparse surrogate construction, and Bayesian inference tools for inferring parameters from experimental data. This manual discusses the download and installation process for UQTk, provides pointers to the UQ methods used in the toolkit, and describes some of the examples provided with the toolkit.
