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Gurecky, William

Publications and source records attributed to Gurecky, William.

7 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

FY24 Mole Development Updates and SAM-Mole Coupling for MSR Species Transport Applications

In this report, a new coupling strategy linking Mole to SAM is demonstrated where SAM is used to drive the solution and Mole is used primarily as a library for mass transport coefficient models. Groundwork is laid to transition Mole into a harbor for various mass transfer coefficient models, which generally take the form of scalar, auxiliary Multiphysics Object-Oriented Simulation Environment (MOOSE) kernels. It is envisioned that these models will be agnostic to the spatial discretization scheme and can be used across the MOOSE ecosystem, providing a centralized library for certain mass transfer coefficients to MOOSE codes that must account for liquid-to-vapor and vapor-to-liquid mass transport phenomena for both high-volatility and low-volatility species.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2023

Differences In High Burnup Fuel Management Strategies to Minimize FFRD and Increase Economic Viability

The nuclear industry is pursuing approval of an increase in the length of the pressurized water reactor (PWR) cycle from 18 months to 24 months to reduce reactor downtime and enhance the economic competitiveness of nuclear energy. Such an increase in reactor cycle length will require that the maximum rod average burnup exceeds the current regulatory limit of 62 GWd/MTU, and it could peak at approximately 75 GWd/MTU, posing potential reactor safety and performance concerns. One such concern is that fuel fragmentation, relocation, and dispersal (FFRD) could occur during a severe loss-of coolant accident (LOCA) in which a fuel rod balloons and bursts, and pulverized fuel fragments are dispersed throughout the reactor’s primary coolant system. Previous analyses have identified which reactor operating conditions leave the core more susceptible to FFRD and have shown that FFRD susceptibility is strongly linked to fuel rod burnup and linear heat rate (LHR) history. The work described in this report uses an optimization strategy known as parallel simulated annealing (PSA) and a coarse mesh Purdue Advanced Reactor Core Simulator (PARCS) reactor physics model to develop two core fuel loading patterns, each with a different optimization objective. One core optimization maximized the core’s cycle length while still respecting regulatory limits on the radial peaking factor and soluble boron concentration with a peak rod average burnup of 75 GWd/MTU. The second optimization was aimed at minimizing FFRD susceptibility while still targeting a 24-month cycle length and respecting regulatory limits. PARCS model predictions were verified using the high-fidelity Virtual Environment for Reactor Applications (VERA). The two core designs were compared to highlight core design strategies to minimize FFRD susceptibility and to maximize economic viability.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2023

Knowledge-Informed Uncertainty-Aware Machine Learning for Time Series Forecasting of Dynamical Engineered Systems

The high complexity and multiscale nature of many engineered systems—such as those in nuclear power plants—make representing and forecasting their dynamic behavior challenging. Physics-based models can be overly complex and computationally intractable, whereas machine learning (ML) tools are often data-hungry and prone to unphysical solutions. This study proposes a knowledge-informed ML-aided hybrid residual modeling approach that offers accurate and efficient time series forecasting for the operation of dynamical engineered systems. Hybrid residual modeling entails a baseline solution from domain knowledge and known physics expressions about the system dynamics integrated with an ML model to capture undiscovered information from the mismatch (i.e., residuals) between true states from measurements and baseline-predicted outputs. This study further quantifies the ML model uncertainty to provide trustworthy solutions. Real-time operational data from thermal-hydraulic flow loops of the cryogenic moderator system in Oak Ridge National Laboratory’s Spallation Neutron Source facility were used to demonstrate the potential of knowledge-informed uncertainty-aware ML in real-world applications. The state variables of the cryogenic helium loop were modeled with (1) first principles–based system identification (sysID), (2) long short-term memory (LSTM) neural network, and (3) hybrid sysID (baseline) + LSTM (residual). The superior predictive capability of the sysID+LSTM model versus stand-alone sysID and LSTM is confirmed by average performance metrics and individual data points across different prediction horizons. By creating a robust representation of the underlying physical system, the widely applicable hybrid residual modeling approach will enable the future development of digital twins for performance prediction, prognostics, and operation control.

Zhao, Xingang↗
DOE OSTI2023

Digital Twin Development of FASTR

This work package is focused on creating a digital twin (DT) of the Facility to Alleviate Salt Technology Risks (FASTR) experiment. FASTR is a high-profile facility tasked with maturing molten salt applications such as liquid salt energy storage, concentrated solar power facilities, and molten salt cooling systems supporting other advanced reactor technologies, all of which may be incorporated as components within the Integrated Energy Systems (IES) program. As part of creating a digital twin (DT), verification and validation (V&V) has been performed for the physics-based model, and gaps have been identified. Additionally, a preliminary integration of the FASTR model has been completed with the existing physics-based model of the Thermal Energy Distribution System (TEDS)/Microreactor AGile Non-nuclear Experimental Testbed (MAGNET) facility at Idaho National Laboratory (INL). A Functional Mock-up Unit (FMU) of the physics-based system model and a reduced-order model (ROM) were both used to test for a preliminary hardware in the loop operation when the physical loop was offline. Future tasks could include improving the physics-based model to better match the available experimental data, improving the hardware in the loop integration, and improving the integration with the TEDS model.

14 SOLAR ENERGY↗
DOE OSTI2023

Elastic Flow Modeling for Hydropower Digital Twins

This report details the elastic, unsteady, one-dimensional flow equations that are used to model flow through a penstock in a hydropower facility. The elastic flow model is accurate even in cases of fast transients, long penstock length, and high gravitation head. The compressibility of the water and elasticity of the pipe walls are explicitly accounted for in the developed models so that the water hammer phenomena can be accurately captured. The elastic flow model is coupled to a mechanistic turbine model to resolve the dynamic feedbacks between the elastic water column and the turbine rotation rate. A finite volume method is employed to solve the governing equations, and the method is shown to have low numerical diffusion in sharp gradient phenomena, such as those encountered when simulating water hammer. The method is applied to single dimensional linear advection benchmark problem and numerical results are compared with analytic results. This is followed on by an application to a full hydropower system with a coupled turbine. The elastic flow model results are compared against an inelastic model.

13 HYDRO ENERGY↗