DYMOND: DYnamic MOtif-NoDes Network Generative Model
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Mathematics and Computing, Computer science↗
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Sensitivity and Uncertainty Quantification of Transition Scenario Simulations
This report documents the first collective attempt at developing and applying capabilities to quantify uncertainties, assess parametric sensitivities, and optimize multiple parameters and metrics in fuel cycle simulations generated by the SA&I Campaign. To do this, external codes that were designed to perform sensitivity analysis and uncertainty quantification (SA&UQ) needed to be coupled to the SA&I Campaign’s nuclear fuel cycle simulators (NFCS). In FY20, two approaches were pursued: 1) coupling Cyclus to an ORNL-internal code called MOT (Metaheuristic Optimization Tool) and 2) coupling DYMOND to the opensource SA&UQ tool kit Dakota. The primary objective of having these NFCS/SA&UQ coupled capabilities is to better inform DOE-NE and other stakeholders on the results generated from the NFCS. For a given set of fuel cycle strategies, policies, and technology assumptions that make up a fuel cycle scenario, these NFCS have traditionally been used by the SA&I Campaign to provide quantitative answers in terms of year-by-year mass flows, infrastructure requirements, costs, etc. With these newly developed coupled capabilities, the SA&I Campaign can now efficiently simulate hundreds or thousands of these scenarios, sample large ranges of parameters and assumptions, and use the unique features of the SA&UQ tools to process the data. This enables providing answers with known and propagated uncertainties, determining the sensitivity of important metrics to different parameters and assumptions, quantifying how much fuel cycle and technology parameters impact each other, and producing optimized fuel cycle strategies for single and multiple variables. To demonstrate these new capabilities, the Cyclus/MOT was used to model several scenarios ranging from simple fleet retirements to transitions to advanced reactors. Specifically, for a transition scenario from LWRs to SFRs and advanced LWRs, uncertainty quantification, sensitivity analysis, and optimization studies were applied to cases involving single and multiple parameter (input) and single and multiple metric (output) variations. In addition, a similar transition scenario was modeled to demonstrate how to optimize the reprocessing capacity parameter to minimize two performance metrics while taking into account uncertainties from two other parameters. Lastly, a depletion module based on SCALE/ORIGEN was added in Cyclus to simulate the third scenario that was designed to quantify the impact of the modeling assumption that all LWR used nuclear fuel have the same burnup. The newly developed DYMOND/Dakota capability was also applied to a transition scenario from the existing fleet to small modular reactors and fast reactors. This particular scenario involves not only explicit isotopic depletion via ORIGEN-2, but also includes multirecycling and utilizing the criticality search feature to determine the fresh fuel composition of recycled fuel, a feature unique to the DYMOND NFCS. A large database of simulations were run with 4 main parameters that were sampled: start date of reprocessing, reprocessing capacity, energy demand growth rate, and advanced reactor share of the fleet. The 4 main metrics were uranium consumption, enrichment requirements, waste generation, and levelized cost of electricity using data from the Cost Basis Report. The demonstrated SA&UQ results include those that inform on how to choose parameters to avoid “failed” scenarios, Sobol’ indices that inform on the importance of various parameters individually and synergistically, and Analysis of Variance (ANOVA) studies that decompose parameter ranges into groups and informs on whether variations are statistically significant.
Feng, B.↗
Standardized verification of the C YCLUS fuel cycle simulator
Many nuclear fuel cycle simulators can analyze transitions from once-through to advanced nuclear fuel cycles. Verification studies compare various fuel cycle analysis tools to test agreement and identify sources of difference. A recent verification study, Feng et al. (2016) established transition scenario test case specifications and accordingly evaluated national laboratory nuclear fuel cycle simulators, DYMOND, VISION, ORION, and MARKAL. This work verifies the performance of C YCLUS , the agent-based, open-source fuel cycle simulator, using the test case specifications in Feng et. al. We report, C YCLUS demonstrates agreement with the results from the previous verification study. Minor differences reflect intentional, detailed material tracking in the C YCAMORE reactor module. These results extend the example results in Feng et al. to further enable future verification of additional nuclear fuel cycle simulation tools.
11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
Application of sensitivity analysis in DYMOND/Dakota to fuel cycle transition scenarios
The ability to perform sensitivity analysis has been enabled for the nuclear fuel cycle simulator DYMOND through its coupling with the design and analysis toolkit Dakota. To test and demonstrate these new capabilities, a transition scenario and multi-parameter study were devised. The transition scenario represents a partial transition from the US nuclear fleet to a closed fuel cycle with small modular LWRs and fast reactors fueled by reprocessed used nuclear fuel. Four uncertain parameters in this transition were studied – start date of reprocessing, total reprocessing capacity, the nuclear energy demand growth, and the rate at which the fast reactors are deployed – with respect to their impact on four response metrics. The responses – total natural uranium consumed, maximum annual enrichment capacity required, total disposed mass, and total cost of the nuclear fuel cycle – were chosen based on measures known to be of interest in transition scenarios and to be significantly impacted by the varying parameters. Furthermore, analysis of this study was performed both from the direct sampling and through surrogate models developed in Dakota to calculate the global sensitivity measures Sobol’ indices. This example application of this new capability showed that the most consequential parameter to most metrics was the share of new build capacity that is fast reactors. However, for the cost metric, the scaling factor of the energy demand growth was significant and had synergistic behavior with the fast reactor new build share.
11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
Core and Fuel Cycle Performance of a Molten Salt Fast Reactor
This paper discusses the reactor core design and modeling approaches for a specific molten salt fast reactor (MSFR) design that is started up with high-assay low-enriched uranium (HALEU) within a chloride fuel salt and focuses on the technical assumptions and design choices. These include the fuel salt properties, first core and make-up heavy metal requirements, fuel composition evolution, equilibrium fission product concentration, etc. This MSFR concept was developed as part of a wider study that sought to compare the fuel cycle performance tradeoffs between deploying a fleet of sodium-cooled fast reactors (SFRs) and deploying a fleet of MSFRs. MSFRs that use flowing liquid fuel salt and on-line salt processing are of particular interest because these technology characteristics are significantly different from those of SFRs and can lead to major differences in fuel cycle impacts. The goal of this study was not to draw general conclusions based on a direct comparison of two very specific point designs for an SFR and MSFR, but rather to use this example MSFR design and associated parametric studies to gain insight into the key technology characteristics that impact fuel cycle performance as it relates to resource utilization (i.e., uranium consumption). The MSFR reactor physics, fission product removal, and fuel salt mass flows were modeled using Argonne’s recently-developed molten salt reactor (MSR) analysis tools while the time-dependent fuel cycle performance of this specific reactor was compared to that of a representative SFR design using the fuel cycle system dynamics code DYMOND.
chloride salt↗
Soil frost controls streamflow generation processes in headwater catchments
The relationship between snowmelt and spring streamflow is changing under warming temperatures and diminishing snowpack. At the same time, the hydrologic connectivity across catchment landscape elements, such as snowpack and surface wetlands, can play a critical role in controlling the routing of snowmelt to streams. The role of hydrologic connectivity is important in headwater regions of the continental northern latitudes, where catchments have low topographic relief and seasonally frozen ground. Nevertheless, the effects of soil frost on the sequence, timing, and magnitudes of hydrologic events that drive the movement of water from a snowpack to a stream are not fully understood. Therefore, we examine two questions: First, what is the flowpath that snow melt and precipitation from spring rain events takes to generate spring streamflow, and second, what hydrologic, climatic, or landscape variables exert the most control on the magnitude of streamflow? In this work, we use long-term hydrological records from the two reference basins at the Marcell Experimental Forest in northern Minnesota to analyze the cascading effects across precipitation, snow, water table elevation, soil frost, and streamflow in peatland-dominated headwater catchments. We identify a sequence of fill-and-spill effects across the landscape that control the timing of spring streamflow generation. Then, we use stepwise regression to show that soil frost is a key supporting predictor for both the magnitude of streamflow in the spring as it adds significantly to the predictive power of precipitation and water table elevation. Our results highlight the importance of recognizing the role of soil frost, when present, on the partitioning of snowmelt between overland runoff and water table recharge during the critical snowmelt period, as well as the later partitioning between evapotranspiration and subsurface flows.
54 ENVIRONMENTAL SCIENCES↗
Automated point dendrometer, soil moisture and temperature, and meteorological variables datasets, Oct 2024 – Nov 2025, G.A. Pearson Natural Area, Flagstaff, AZ, USA
This data package includes parsed, cleaned, and calibrated data from 48 TOMST automated point dendrometers, 48 TOMST 15 cm soil moisture sensors, and 12 TOMST 30 cm soil moisture sensors. The point dendrometers were cleaned with the “dendRoAnalyst” package in RStudio. The soil sensors were cleaned and calibrated for volumetric water content (VWC) with the “myClim” package in RStudio using the soil texture of the site (sandy clay loam). Additionally, this data package also includes raw data from 2 METER weather stations. Dendrometers and soil sensors have both their sensor ID, as well as the ID for the specific tree they were instrumented on at the G.A. Pearson Natural Area (GPNA) site and their experimental group. The purpose of these data is to understand how ponderosa pine trees in restored (thinned and burned) vs. unrestored (no treatment) areas are responding to drought and seasonal precipitation. These data use radial growth and soil moisture data to answer the following question: how are active season length, growth on different time scales (weekly, monthly, seasonally, and annually), growth during dry periods and after precipitation events, and environmental and biological drivers of radial growth different between restored versus unrestored areas?
Air temperature↗
On the Need for a Unified and Collaboratively Developed Residential Building Simulation Platform: Preprint
Residential building energy simulations are becoming ever more pervasive; they form the basis of energy efficient building design, zero energy goals, codes and standards, home certification and ratings industries, utility programs, regional/state planning, and technology assessments. And yet the residential building simulation community is highly fragmented with a patchwork of simulation engines with varying models, inputs, assumptions, and results. This inconsistency degrades confidence in these tools, increases industry-wide development and maintenance costs, and slows the availability of new efficiency technology models. The need for collaboration on an impartial, transparent, and capable residential building simulation platform has never been greater. But to develop and maintain a state-of-the-art simulation platform requires a dedicated and sustained investment. By leveraging existing resources and expertise from multiple entities/regions, a unified and collaboratively developed simulation platform can provide increased levels of energy efficiency at an accelerated pace. This paper presents the advantages and disadvantages of a unified residential building simulation platform in relationship to four real-world examples (RESNET HERS ratings, utility programs, California's Title 24, and DOE). The paper covers recent developments and improved capabilities that may move us closer to an ideal simulation platform and what challenges remain. The hope is to spur a frank and honest dialogue about how to work together towards the vision of achieving deeper and more reliable energy savings in the residential buildings industry.
32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
On the Need for a Unified and Collaboratively Developed Residential Building Simulation Platform
Residential building energy simulations are becoming ever more pervasive; they form the basis of energy efficient building design, zero energy goals, codes and standards, home certification and ratings industries, utility programs, regional/state planning, and technology assessments. And yet the residential building simulation community is highly fragmented with a patchwork of simulation engines with varying models, inputs, assumptions, and results. This inconsistency degrades confidence in these tools, increases industry-wide development and maintenance costs, and slows the availability of new efficiency technology models. The need for collaboration on an impartial, transparent, and capable residential building simulation platform has never been greater. But to develop and maintain a state-of-the-art simulation platform requires a dedicated and sustained investment. By leveraging existing resources and expertise from multiple entities/regions, a unified and collaboratively developed simulation platform can provide increased levels of energy efficiency at an accelerated pace. This paper presents the advantages and disadvantages of a unified residential building simulation platform in relationship to four real-world examples (RESNET HERS ratings, utility programs, California's Title 24, and DOE). The paper covers recent developments and improved capabilities that may move us closer to an ideal simulation platform and what challenges remain. The hope is to spur a frank and honest dialogue about how to work together towards the vision of achieving deeper and more reliable energy savings in the residential buildings industry.
32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
Observing atmospheric gravity waves from the Space Station
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Mathematics and Computing, Geosciences, Environmen↗