Data for EMSL Project 60748 from January 2024
Explore the source record for details and available documents.
Vienna, John↗
Engineering topics
Vienna, John
Publications and source records attributed to Vienna, John.
The Center for Performance and Design of Nuclear Waste Forms and Containers (WastePD) Energy Frontier Research Center (Final Report)
The DOE Office of Environmental Management is responsible for high level nuclear waste that must be safely isolated from humans and the environment for extremely long periods. The waste forms and containers are made of glass, ceramics, and metals. Verified safe disposal requires understanding the fundamental mechanisms of waste form degradation and the design of new waste forms with improved performance, which comprise the goals of the Energy Frontier Research Center known as the Center for the Performance and Design of Nuclear Waste Forms and Containers, WastePD. WastePD was constructed to develop innovative approaches and solutions to those goals through the synergistic interactions of individuals who are experts in the degradation behavior, modeling, and design of glasses, ceramics and metal alloys. WastePD is the first center ever created to address this diverse group of materials in a comprehensive and coordinated manner. The science goals are grouped into three common topics: corrosion mechanisms via advanced characterization, environmental impacts, and materials design. Synergistic interactions in these areas were a key component of WastePD. The fundamental understanding of the degradation mechanisms of the waste forms and containers as well as the development of new materials with improved properties will allow DOE to prevent environmental contamination and to explore totally new repository concepts. WastePD was operational from August 2016 through July 2022, but the DOE support was drastically reduced for the last two years. This final technical report covers the full period of performance. However, much of what was accomplished in the first four years is nicely summarized in a review paper published in 2021, which is appended to this report. Therefore, this final report focuses on the technical findings from the last two years of WastePD activities. Considerable progress was made in the areas of a) the environmental and compositional impacts on the corrosion of borosilicate and aluminosilicate glasses, b) the mechanism of glass corrosion and the structure and evolution of the surface alteration layer, c) the effects of environment and composition on the corrosion of pyrochlore, perovskite, hollandite, and other oxide ceramics, d) the corrosion mechanism of multi-principal element metallic alloys, and e) a new framework for understanding the pitting corrosion of metals.
12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
Machine Learning with Gradient-Based Optimization of Nuclear Waste Vitrification with Uncertainties and Constraints
Gekko is an optimization suite in Python that solves optimization problems involving mixed-integer, nonlinear, and differential equations. The purpose of this study is to integrate common Machine Learning (ML) algorithms such as Gaussian Process Regression (GPR), support vector regression (SVR), and artificial neural network (ANN) models into Gekko to solve data based optimization problems. Uncertainty quantification (UQ) is used alongside ML for better decision making. These methods include ensemble methods, model-specific methods, conformal predictions, and the delta method. An optimization problem involving nuclear waste vitrification is presented to demonstrate the benefit of ML in this field. ML models are compared against the current partial quadratic mixture (PQM) model in an optimization problem in Gekko. GPR with conformal uncertainty was chosen as the best substitute model as it had a lower mean squared error of 0.0025 compared to 0.018 and more confidently predicted a higher waste loading of 37.5 wt% compared to 34 wt%. The example problem shows that these tools can be used in similar industry settings where easier use and better performance is needed over classical approaches. Future works with these tools include expanding them with other regression models and UQ methods, and exploration into other optimization problems or dynamic control.
12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗