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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Updating and Evaluating Anthropogenic Emissions for NOAA’s Global Ensemble Forecast Systems for Aerosols (GEFS-Aerosols): Application of an SO 2 Bias-Scaling Method

We updated the anthropogenic emissions inventory in NOAA’s operational Global Ensemble Forecast for Aerosols (GEFS-Aerosols) to improve the model’s prediction of aerosol optical depth (AOD). We used a methodology to quickly update the pivotal global anthropogenic sulfur dioxide (SO 2 ) emissions using a speciated AOD bias-scaling method. The AOD bias-scaling method is based on the latest model predictions compared to NASA’s Modern-Era Retrospective analysis for Research and Applications, version 2 (MERRA2). The model bias was subsequently applied to the CEDS 2019 SO 2 emissions for adjustment. The monthly mean GEFS-Aerosols AOD predictions were evaluated against a suite of satellite observations (e.g., MISR, VIIRS, and MODIS), ground-based AERONET observations, and the International Cooperative for Aerosol Prediction (ICAP) ensemble results. The results show that transitioning from CEDS 2014 to CEDS 2019 emissions data led to a significant improvement in the operational GEFS-Aerosols model performance, and applying the bias-scaled SO 2 emissions could further improve global AOD distributions. The biases of the simulated AODs against the observed AODs varied with observation type and seasons by a factor of 3~13 and 2~10, respectively. The global AOD distributions showed that the differences in the simulations against ICAP, MISR, VIIRS, and MODIS were the largest in March–May (MAM) and the smallest in December–February (DJF). When evaluating against the ground-truth AERONET data, the bias-scaling methods improved the global seasonal correlation (r), Index of Agreement (IOA), and mean biases, except for the MAM season, when the negative regional biases were exacerbated compared to the positive regional biases. The effect of bias-scaling had the most beneficial impact on model performance in the regions dominated by anthropogenic emissions, such as East Asia. However, it showed less improvement in other areas impacted by the greater relative transport of natural emissions sources, such as India. The accuracies of the reference observation or assimilation data for the adjusted inputs and the model physics for outputs, and the selection of regions with less seasonal emissions of natural aerosols determine the success of the bias-scaling methods. A companion study on emission scaling of anthropogenic absorbing aerosols needs further improved aerosol prediction.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1997

Detection of chaotic determinism in time series from randomly forced maps

Time series from biological system often display fluctuations in the measured variables. Much effort has been directed at determining whether this variability reflects deterministic chaos, or whether it is merely "noise". Despite this effort, it has been difficult to establish the presence of chaos in time series from biological sytems. The output from a biological system is probably the result of both its internal dynamics, and the input to the system from the surroundings. This implies that the system should be viewed as a mixed system with both stochastic and deterministic components. We present a method that appears to be useful in deciding whether determinism is present in a time series, and if this determinism has chaotic attributes, i.e., a positive characteristic exponent that leads to sensitivity to initial conditions. The method relies on fitting a nonlinear autoregressive model to the time series followed by an estimation of the characteristic exponents of the model over the observed probability distribution of states for the system. The method is tested by computer simulations, and applied to heart rate variability data.

Non-NASA Center↗
DOE OSTI2022

Estimation of process steady state with autoregressive models and Bayesian inference

To improve efficiency, separations engineers will typically design process circuits containing recirculating streams, which mix one or more of the process outputs with the feed material. Doing so can improve efficiency, but will cause a delay in the system reaching steady state conditions until the recirculating load mass flows stabilize. In testing separation circuits, engineers will often test a variety of factors and complete an analysis from sample results. Knowledge of if a process is at steady state, as well as the steady state conditions of a process, is essential for a valid techno-economic analysis. However, the definition of process steady state is often poorly defined, or does not include uncertainty quantification. If the performance of a process operating under two different sets of conditions are compared, an engineer who does not test for steady state or quantify steady state conditions risks producing a faulty analysis. In this work, a Bayesian statistical method for testing if all streams are at steady state is further motivated and then derived. Then after testing for steady state, the same model is used with a prior distribution that enforces a steady state assumption to estimate steady state conditions. Further, these methods were validated in a solvent extraction pilot plant where steady state conditions for all outflows were inferred with uncertainty quantification. Analysis is completed with functions available to the reader as part of the BayesMassBal (V 1.1.0) software package written in R.

01 COAL, LIGNITE, AND PEAT↗
DOE OSTI2019

Postclosure Criticality Consequence Analysis - Scoping Phase

Commercial generation of energy via nuclear power plants in the United States (U.S.) has generated thousands of metric tons of spent nuclear fuel (SNF), the disposal of which is the responsibility of the U.S. Department of Energy (DOE) (Nuclear Waste Policy Act of 1982). Any repository licensed to dispose of the SNF must meet requirements regarding the long-term performance of the repository. In evaluating the long-term performance of the repository, one of the events that may need to be considered is the SNF achieving a critical configuration. Of particular interest is the potential behavior of SNF in dual-purpose canisters (DPCs), which are currently being used to store the SNF but were not designed for permanent disposal. As part of a multiyear plan that is currently being developed for the DOE, a two-phase study has been initiated to examine the potential consequences, with respect to long-term repository performance, of criticality events that might occur during the postclosure period in a hypothetical repository containing DPCs. Phase I, a scoping phase, consists of generating an approach intended to be a starting point for the development of the modeling tools and techniques that may eventually be required either to exclude criticality from or include criticality in a performance assessment (PA) as appropriate. The Phase I approach will be used to guide the analyses and simulations done in Phase II to further the development of these modeling tools and techniques as well as the overall knowledge base. The purpose of this report is to document the approach created during Phase I. The study discussed herein focuses on the consequences of criticality in a DPC; it does not address the probability of occurrence of a criticality event. This approach examines two types of criticality events for SNF disposed of in a single type of DPC: a steady-state criticality and a transient criticality. The steady-state critical event is characterized by a relatively low constant power output over 10,000 years, while the transient critical event is characterized by a power spike that lasts on the order of seconds. Possible effects of the criticality are an increase in the radionuclide inventory; an increase in temperature; and a change in the chemistry inside the waste package, along with a change in radionuclide solubilities, fuel degradation rates, and steel corrosion rates. Additionally, for transient criticality the possibility of mechanical damage to the engineered and natural barriers also exists.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2014

GEOCHEMICAL INVESTIGATIONS OF CO₂-BRINE-ROCK INTERACTIONS OF THE KNOX GROUP IN THE ILLINOIS BASIN

Increased output of greenhouse gases, particularly carbon dioxide (CO₂), into the atmosphere from anthropogenic sources is of great concern. A potential technology to reduce CO₂ emissions is geologic carbon sequestration. This technology is currently being evaluated in the United States and throughout the world. The geology of the Illinois Basin exhibits outstanding potential as a carbon sequestration target, as demonstrated by the ongoing Illinois Basin – Decatur Project that is using the Mt. Simon Sandstone reservoir and Eau Claire Shale seal system to store and contain 1 million tonnes of CO₂. The Knox Group-Maquoketa Shale reservoir and seal system, located stratigraphically above the Mt. Simon Sandstone-Eau Claire Shale reservoir and seal system, has little economic value as a resource for fossil fuels or as a potable water source, making it ideal as a potential carbon sequestration target. In order for a reservoir-seal system to be effective, it must be able to contain the injected CO₂ without the potential for the release of harmful contaminants liberated by the reaction between CO₂-formation fluids and reservoir and seal rocks. This study examines portions of the Knox Group (Potosi Dolomite, Gunter Sandstone, New Richmond Sandstone) and St. Peter Sandstone, and Maquoketa Shale from various locations around the Illinois Basin. A total of 14 rock and fluid samples were exposed to simulated sequestration conditions (9101–9860 kPa [1320–1430 psi] and 32°–42°C [90°– 108°F]) for varying amounts of time (6 hours to 4 months). Knox Group reservoir rocks exhibited dissolution of dolomite in the presence of CO₂ as indicated by petrographic examination, X-ray diffraction analysis, and fluid chemistry analysis. These reactions equilibrated rapidly, and geochemical modeling confirmed that these reactions reached equilibrium within the time frames of the experiments. Pre-reaction sample mineralogy and postreaction fluid geochemistry from this study suggests only limited potential for the release of United States Environmental Protection Agency regulated inorganic contaminants into potable water sources. Short-term core flood experiments further verify that the carbonate reactions occurring in Knox Group reservoir samples reach equilibrium rapidly. The core flood experiments also lend insight to pressure changes that may occur during CO₂ injection. The Maquoketa Shale experiments reveal that this rock is initially chemically reactive when in contact with CO₂ and brine. However, due to the conservative nature of silicate and clay reaction kinetics and the rapid equilibration of carbonate reactions that occur in the shale, these reactions would not present a significant risk to the competency of the shale as an effective seal rock.

Yoksoulian, Lois↗
DOE OSTI2025

MOOSE ProbML: Parallelizable Probabilistic Machine Learning and Uncertainty Quantification Capabilities

The Multiphysics Object Oriented Simulation Environment (MOOSE) is a widely used open- source finite element software for performing multiphysics multiscale simulations in a massively parallel fashion. Recently, the computational team at Idaho National Laboratory (INL) has implemented Probabilistic Machine Learning (ProbML) capabilities in MOOSE—in a parallelized fashion—and enable active learning with large-scale computational models for tasks such as surrogate model development, scale bridging, forward/inverse uncertainty quantification (UQ), Bayesian optimization, etc. This presentation summarizes these developments in MOOSE along with demonstrations on several real applications relevant to nuclear energy. At the fundamental level, samplers like Monte Carlo/Latin Hypercube, variance reduction, parallelized Markov Chain Monte Carlo (MCMC) support uncertainty propagation in both forward and inverse settings. These samplers can be integrated with the Gaussian processes (GP) suite in MOOSE, which offer several variants like scalar GPs, multi-output GPs, and deep GPs, to enable active learning. These GPs can be tuned using gradient-based optimization methods like Adam and its variants or gradient-free methods like the elliptical slice sampler (a variant of MCMC adept under Gaussian settings) for more complex covariance kernels or likelihoods whose gradient computations can be cumbersome. A variety of batch acquisition functions permit parallelized evaluation of the computational model and support different learning objectives with high efficiency like Bayesian inference, global surrogate development, optimization, etc. Furthermore, libtorch integration supports training, evaluation, and re-training of neural networks and other complex machine learning models in active learning settings. The impacts of these developments are shown on several real applications: (1) nuclear fuel inverse UQ and model inadequacy assessment using the Kennedy O’Hagan framework; (2) uncertainty aware surrogate modeling for additive manufacturing to predict field quantities; (3) nuclear reactor rare events analysis; and (4) complex fluid flow prediction using a global surrogate with quantified prediction uncertainty. Finally, the outlook of MOOSE ProbML is discussed for both outer-loop and inner-loop computations in the broad view to accelerate fuels and materials qualification, address gaps in knowledge and data, and assess new reactor/fuel systems.

11 - NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2017

Dynamic Analysis of the Next Generation Molten-Salt Breeder Reactor System

The conceptual design of a 1000 MWe two-fluid molten-salt breeder reactor (MSBR) station was performed during 1966-1970 at Oak Ridge National Laboratory (ORNL) with the goal of using the U-233 fuel cycle. This modular design with four reactor modules has an electrical output of 250 MW/module. The abundance of Th-232 was attractive in converting it to U-233 in a thermal breeder reactor. The two-fluid reactor is graphite-moderated 'with a {sup 7}LiF-BeF{sub 2}-UF{sub 4} fuel salt circulated through the core and a {sup 7}LiF-ThF{sub 4}-BeF{sub 2} blanket salt circulated through separate flow channels distributed throughout the core, as well as in a surrounding under-moderated region'. This reactor design has the advantages of not producing Pu isotopes and easy chemical separation of U-233 in the blanket material. The low fuel cost and the graphite replacement cost are attractive for such a design, with an estimated cost of $141/kW at 1968-dollar value. Potential non-proliferation features of MSBR, the low costs of raw materials, and established chemical separation of fission products are important factors in further development of MSBRs as an alternative to current light water reactors. These reactors are often referred to as liquid fluoride thorium reactor (LFTR). The current research on the modeling of the MSBR is based on an earlier work on the preliminary dynamic model of the MSBR concept. Revisions are made to the original model. The physics parameters have been modified for the current modeling effort based on additional information including more recent values of model parameters from and several typographical misprints in equations and values. Details of this effort will be presented in a full follow-up paper. This work presents the detailed nodal models of reactor fluid core, blanket fluid loop, graphite moderator, fuel fluid heat exchanger, and the blanket fluid heat exchanger. A set of simulations has been performed to validate the model using the commercial software MATLAB{sup TM}-Simulink. Simulations were performed for the cases of source perturbation and reactivity perturbation. The model is inherently nonlinear. A description of the MSBR conceptual design, along with a tabulation of relevant system parameters, is presented. The results of simulation demonstrate stable behavior of the MSBR dynamics, temperature feedback effects, and load-following capability of the MSBR system.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2016

VERA and VERA-EDU 3.5 Release Notes

The Virtual Environment for Reactor Applications components included in this distribution include selected computational tools and supporting infrastructure that solve neutronics, thermal-hydraulics, fuel performance, and coupled neutronics-thermal hydraulics problems. The infrastructure components provide a simplified common user input capability and provide for the physics integration with data transfer and coupled-physics iterative solution algorithms. Neutronics analysis can be performed for 2D lattices, 2D core and 3D core problems for pressurized water reactor geometries that can be used to calculate criticality and fission rate distributions by pin for input fuel compositions. MPACT uses the Method of Characteristics transport approach for 2D problems. For 3D problems, MPACT uses the 2D/1D method which uses 2D MOC in a radial plane and diffusion or SPn in the axial direction. MPACT includes integrated cross section capabilities that provide problem-specific cross sections generated using the subgroup methodology. The code can be executed both 2D and 3D problems in parallel to reduce overall run time. A thermal-hydraulics capability is provided with CTF (an updated version of COBRA-TF) that allows thermal-hydraulics analyses for single and multiple assemblies using the simplified VERA common input. This distribution also includes coupled neutronics/thermal-hydraulics capabilities to allow calculations using MPACT coupled with CTF. The VERA fuel rod performance component BISON calculates, on a 2D or 3D basis, fuel rod temperature, fuel rod internal pressure, free gas volume, clad integrity and fuel rod waterside diameter. These capabilities allow simulation of power cycling, fuel conditioning and deconditioning, high burnup performance, power uprate scoping studies, and accident performance. Input/Output capabilities include the VERA Common Input (VERAIn) script which converts the ASCII common input file to the intermediate XML used to drive all of the physics codes in the VERA Core Simulator (VERA-CS). VERA component codes either input the VERA XML format directly, or provide a preprocessor which can convert the XML into native input. VERAView is an interactive graphical interface for the visualization and engineering analyses of output data from VERA. The python-based software is easy to install and intuitive to use, and provides instantaneous 2D and 3D images, 1D plots, and alpha-numeric data from VERA multi-physics simulations. Testing within CASL has focused primarily on Westinghouse four-loop reactor geometries and conditions with example problems included in the distribution.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
NASA NTRSDate not supplied

Cardiovascular Responses to Simulated Spaceflight: Molecular Signatures and Surrogate Outputs to Measure CVD Risk

During extended space missions beyond low Earth orbit, astronauts will encounter prolonged periods of weightlessness and low dose space radiation. Previous studies have shown that exposure to small doses of high LET radiation (< 50 cGy) can lead to both short-term and long-term alterations in heart function, structure and underlying molecular mechanisms. In this study, we aim to identify the molecular signature associated with the cardiovascular response to simulated galactic cosmic radiation (5-ion GCR) alone or in combination with simulated weightlessness at time intervals relevant to mission length and recovery. Additionally, we aim to determine whether sex impacts cardiovascular responses to these spaceflight factors. Our overarching goal is to enhance our understanding of the cardiovascular risks associated with extended space missions and the clinical endpoints they suggest. We hypothesize that exposure to simulated space radiation leads to enduring alterations in the transcriptome, redox signaling and cytokine environment of cardiovascular tissue, some which have known links with reduced cardiovascular performance, aging, and increased risk of cardiovascular disease (CVD). Furthermore, we posit that simulated space radiation exposure in combination with simulated microgravity exacerbates cardiovascular deficits compared to single factor exposure. Female and male C57BL/6J mice, aged 23-24 weeks, were exposed to a single dose of 5, 15, or 50 cGy of 5-ion GCR, or sham-treated (0 cGy). Euthanasia was performed at 14 days and ~4 months post-irradiation. Hearts, aorta and blood plasma were collected shortly thereafter. RNA-sequencing of left ventricles at ~4 months post-GCR exposure revealed sex differences in the heart transcriptome with a few genes showing radiation-dependent changes in expression levels. Notably, some of the differentially expressed genes in 15 and 50 cGy GCR groups are known to play roles in the development of CVD. Analysis of protein levels of a subset of inflammatory cytokines in the heart indicated sex differences but no differences between sham and 50 cGy groups. Results also showed correlations among differentially expressed genes and a subset of inflammatory cytokines, with some correlations altered by GCR exposure. These findings suggest that GCR exposure can modify protein and gene networks linked to inflammation and CVD progression. In the aorta, telomere lengths were comparable across treatment groups sexes. Mitochondrial copy number is a biomarker for mitochondrial function with decreased copy numbers associated with cardiometabolic disease traits. Mitochondrial copy numbers of aorta also showed no sex nor dose differences. In a second study, mice underwent one week of simulated microgravity by hindlimb unloading (HU) and then exposed to a single dose of 15 cGy of 5-ion GCR. HU was conducted for an additional two weeks following GCR exposure. Single factor exposure groups (HU or GCR only) also were included in the study. Euthanasia was then performed and the same tissues were collected. Protein levels of select inflammatory cytokines in the heart showed sex-dependent differences in expression. In the aorta, telomere lengths and mitochondrial copy number also showed sex differences. In summary, our results indicate differences between sexes in biomarkers related to cardiovascular health. Exposure to 5-ion GCR or HU, alone or in combination, did not result in changes in most of the cardiovascular biomarkers that were examined. However, in the heart, simulated space radiation at doses of 15 and 50 cGy led to long-term alterations in the expression levels of a small group of genes known to be associated with the progression of CVD. The long-term transcriptomic changes resulting from exposure to simulated space radiation should be carefully investigated to mitigate adverse cardiovascular events during and after deep space missions. Our results also highlight the importance of sex-specific strategies in monitoring and maintaining cardiovascular health during and after deep space missions.

cardiovascular↗
DOE OSTI2019

Validation of RELAP-7 for Forced Convective and Natural Circulation Reactor Flows

New reactor designs such as small modular reactors and newly proposed passive safety systems rely on natural circulation two-phase flow cooling of the reactor core under accident conditions. Without a pump to drive the flow, the circulation rate is a strong function of void fraction. Under these conditions where the flow rate, pressure drop, and heat transfer are all closely coupled through the mixture density, the accurate prediction of void fraction is pivotal. Furthermore, while these passive systems boast a more reliable cooling option, the sensitivity of the system to two-phase flow instabilities is more likely than in forced convective cooling and can result in dangerous density wave or flow excursion conditions. Improvement of one-dimensional system codes and further development of three-dimensional code capability are all important efforts in addressing these issues. The proposed work is a critical step towards providing confidence through validation of the capability of thermal-hydraulic tools to meet the emerging challenges in reactor simulation. Historically, system analysis codes develop a stiffness over time due to fine-tuning of adjustable code parameters that result in a reluctance or inability to adopt improved models. Therefore, a rigorous effort should be invested in preparation for RELAP-7 to identify required modeling improvements of constitutive relations and validation of the code applicability to the wide range of possible reactor flow conditions. Part of this validation involves the uncertainty quantification which is a critical step in the Code Scaling, Applicability, and Uncertainty (CSAU) methodology and Best-Estimate Plus Uncertainty (BEPU) licensing method. BEPU allows recovery of safety margins and potentially optimizing plant performance, resulting in economic benefit for the reactor operators. Beginning with the collection of past experimental data covering prototypic LWR conditions, this document reviews three critical datasets available for the validation of system codes. The experimental methods, procedures, facilities, instrumentation, and supplementary information are consolidated to aid the validation efforts of this work and future projects. These three datasets (824 conditions) are chosen due to their wide acceptance and demonstrated application for code validation. Two experimental datasets from the Nuclear Power Engineering Corporation (NUPEC) span prototypic conditions of Boiling Water Reactors (BWR) and Pressurized Water Reactors (PWR). These datasets are labeled as the BFBT (BWR Full-size Fine-mesh Bundle Test) and the PSBT (PWR Subchannel and Bundle Test) datasets respectively. The third dataset is taken in an internally heated annulus that is scaled based on a BWR subassembly and includes two-phase flows important to post-blowdown analysis. Together, the datasets consider the full range of pressure and mass flux expected in LWR forced convective flows from steady-state to long term cooling conditions. Recognizing the deficiency of available data under low-pressure, low-flow, a new natural circulation dataset consisting of 107 conditions is obtained. With system pressures ranging between 145 and 950 kPa, natural circulation is driven by a heat flux up to 275 kW/m2 in a 3 m long heated region of the annulus test section which is followed by a 2 m long unheated region before entering a condenser. Stable natural circulation flow rates in this facility were measured to be between 182 and 590 kg/m2-s. Pressure and temperature are measured at ten locations in the facility. In addition, measurements of void fraction, interfacial area concentration, and gas velocity are obtained at five axial locations in the annulus test section. The RELAP5-3D code demonstrates good accuracy for void fraction predictions in BFBT and PSBT benchmarks, but shows relatively large discrepancy in the annulus experiments at lower pressures. In two-phase natural circulation, large oscillations are observed in many system parameters during the simulation of 19 experiment cases which were conducted at low pressures, and the model shows good predictive performance with the remaining 88 experiment cases. However, some discrepancies in void fraction and temperature predictions are found which may require improved constitutive modeling under these low-pressure, low-flow condition. The quantified uncertainty in void fraction predictions in three forced convection benchmarks due to uncertainties in boundary conditions and geometries are relatively small, except for a few cases in the annulus experiment. The output uncertainty of the natural circulation model is quantified by considering the measurement error in boundary conditions, and it is shown that the output uncertainty ranges are relatively small for the range of conditions considered. Through this work, a clear pathway is provided for validation of RELAP7 once the code is available.

42 ENGINEERING↗
DOE OSTI2026

Refining a Novel Process Monitoring Method to Safeguard Continuously Cycling Designs Using Isotopic Ratios

In advanced reactor (AR) designs, a common feature is continuous chemical processing and circulation of the nuclear material. This work bridges a significant measurement gap in safeguarding reactors with circulating fuel or continuous refueling by leveraging and building on the isotope ratio method first developed by our team under an FY21 Advanced Reactors International Safeguards Engagement (ARISE) project (Uribe et al. 2021). In circulating fuel designs, the radioisotope inventory changes from traditional effects (e.g., radioactive decay, fission) but also includes material transport due to pressure and temperature gradients. Such designs may also require regular or continuous additions or removals during operation, which significantly increases the rate of inventory change compared to traditional pressurized water reactor (PWR)s. Thus, directly tracking the nuclear inventory is ineffective since the isotopes are continuously added and removed. The isotope ratio method instead focuses on detecting changes to the input and output flows of radioisotopes. Previous work showed that for well-chosen pairs of isotopes, the isotopic ratio provides a sensitive and lasting indicator of deviation from normal conditions (e.g., startup, shutdown, diversion). The isotope ratio method is a process monitoring method with potential for application in for forward-looking approaches to International Atomic Energy Agency (IAEA) safeguards. The original process monitoring method was developed for a specific case—the decay tank of a thorium-fueled molten salt breeder reactor. In this expanded work, we explored other types of reactors and processes with nonstationary (e.g., flowing) nuclear material, which are difficult to safeguard with traditional methods because of the transient nature of the systems. The goal of the present work is to generalize the isotope ratio method for use in processes with continuously flowing nuclear material. All continuous processes have an average time for isotopes to be replaced in the system. The isotope ratio method works by choosing radioisotopes with half-lives both above and below the processing time. The present work seeks to explore which isotopes are suitable for the method by simulating the nuclear inventory, radioisotope emissions, and detector responses for several classes of advanced reactors. While the method can in principle be applied to other processes (e.g., enrichment or reprocessing facilities), the present work limits scope to ARs with continuously flowing fuel. Section 2 details the mathematics supporting the isotope ratio method, and Section 3 introduces the representative ARs selected for this work. Section 4 discusses how each reactor was analyzed, and Section 5 showcases the results for each representative reactor. Finally, Section 6 provides concluding remarks and suggests pathways for further analysis.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗
DOE OSTI2021

Techno-Economic Analysis of a Concentrating Solar Power Plant Using Redox-Active Metal Oxides as Heat Transfer Fluid and Storage Media

We present results for a one-dimensional quasi-steady-state thermodynamic model developed for a 111.7 MW e concentrating solar power (CSP) system using a redox-active metal oxide as the heat storage media and heat transfer agent integrated with a combined cycle air Brayton power block. In the energy charging and discharging processes, the metal oxide CaAl 0.2 Mn 0.8 O 2.9-δ (CAM28) undergoes a reversible, high temperature redox cycle including an endothermic oxygen-releasing reaction and exothermic oxygen-incorporation reaction. Concentrated solar radiation heats the redox-active oxide particles under partial vacuum to drive the reduction extent deeper for increased energy density at a fixed temperature, thereby increasing storage capacity while limiting the required on sun temperature. Direct counter-current contact of the reduced particles with compressed air from the Brayton compressor releases stored chemical and sensible energy, heating the air to 1,200°C at the turbine inlet while cooling and reoxidizing the particles. The cool oxidized particles recirculate through the solar receiver subsystem for another cycle of heating and reduction (oxygen release). We applied the techno-economic model to 1) size components, 2) examine intraday operation with varying solar insolation, 3) estimate annual performance characteristics over a simulated year, 4) estimate the levelized cost of electricity (LCOE), and 5) perform sensitivity analyses to evaluate factors that affect performance and cost. Simulations use hourly solar radiation data from Barstow, California to assess the performance of a 111.7 MW e system with solar multiples (SMs) varying from 1.2 to 2.4 and storage capacities of 6–14 h. The baseline system with 6 h storage and SM of 1.8 has a capacity factor of 54.2%, an increase from 32.3% capacity factor with no storage, and an average annual energy efficiency of 20.6%. Calculations show a system with an output of 710 GWh e net electricity per year, 12 h storage, and SM of 2.4 to have an installed cost of $329 million, and an LCOE of 5.98 ¢/kWh e . This value meets the U.S. Department of Energy’s SunShot 2020 target of 6.0 ¢/kWh e ( U. S Department of Energy, 2012 ), but falls just shy of the 5.0 ¢/kWh e 2030 CSP target for dispatchable electricity ( U. S Department of Energy, 2017 ). The cost and performance results are minimally sensitive to most design parameters. However, a one-point change in the weighted annual cost of capital from 8 to 7% (better understood as a 12.5% change) translates directly to an 11% decrease (0.66 ¢/kWhe) in the LCOE.

Gorman, Brandon T.↗
DOE OSTI2020

Neural MUSE Analysis

Researchers at Oak Ridge National Laboratory (ORNL) created data as part of the MUSE (Multi-Agency Urban Search Experiment Detector and Algorithm Test Bed) project simulating illicit nuclear materials located in various buildings along a road. In the simulation, a truck containing a radiation detector drives down the road gathering listmode data (counting the and energy of incident gamma radiation). Building materials, source shielding, driving speed, truck direction, truck location on the road, source type, and source placement are all varied between runs of the data set. This data was created using deterministic neutron transport and Monte Carlo methods through a combination of SCALE, MAVRIC, MCNP, and GADRAS. As part of a follow-on NA-22 project, two Kaggle competitions were created to determine the best algorithms for finding and identifying gamma sources in this simulated urban environment. The winning algorithm was neural network-based and had a test accuracy of 76.4% accuracy for source identification. This work seeks to build upon this work and improve the results through the application of novel machine learning techniques. As a first step, the data was classified by a simple Convolutional Neural Network (CNN) To accomplish this, the data was first preprocessed into “waterfall plots.” These plots are composed of energy vs count plots that are stacked vertically to show progression in time. The horizontal axis indicating the particle energy incorporated user defined bin spacing with options for in linear-, logarithmic-, square root-, and user-spaced bins. The z or color dimension showed the number of counts corresponding the energy-time combination. This data was then used to generate more data, by generating a local estimate of the mean of the distribution for a bin and then randomly re-sampling that bin from a Poisson distribution. Once all of this data was generated, it was fed into a well-known CNN architecture, ResNet50. The output layer of this model was removed and replaced with layers corresponding to the shape desired isotope outputs. The provided training data was used to train the classifier and the remaining testing data was used to evaluate the model. Results are soon to be forthcoming.

61 RADIATION PROTECTION AND DOSIMETRY↗
DOE OSTI2015

SU-C-BRD-06: Sensitivity Study of An Automated System to Acquire and Analyze EPID Exit Dose Images

Purpose: The dosimetric consequences of errors in patient setup or beam delivery and anatomical changes are not readily known. A new product, PerFRACTION (Sun Nuclear Corporation), is designed to detect these errors by comparing the EPID exit dose image from each field of each fraction to those from baseline fraction images. This work investigates the sensitivity of PerFRACTION to detect the deviation of induced errors in a variety of realistic scenarios. Methods: Eight plans were created mimicking potential delivery or setup errors. The plans consisted of a nominal field and the field with an induced error. These were used to irradiate the EPID simulating multiple fractions with and without the error. Integrated EPID images were acquired in clinical mode and saved in ARIA. PerFRACTION automatically pulls the images into its database and performs the user defined comparison. In some cases, images were manually pushed to PerFRACTION. We varied the distance-to-agreement or dose tolerance until PerFRACTION showed failing pixels in the affected region and recorded the values. We induced errors of 1mm and greater in jaw, MLC, and couch position, 2 degree collimation rotation (patient yaw), and 0.5% to 1.5% in machine output. Both static and arc fields with the rails in or out were also acquired and compared. Results: PerFRACTION detected position errors of the jaws, MLC, and couch with an accuracy of better than 0.5 mm, and 0.2 degrees for collimator and gantry error. PerFRACTION detected a machine output error within 0.2% and detected the change in rail position. Conclusion: A new automated system for monitoring daily treatments for machine or patient variations from the first fraction using integrated EPID images was found to be sensitive enough to detect small positional, angular, and dosimetric errors within 0.5mm, 0.2 degrees, and 0.2%, respectively. Sun Nuclear Corporation has provided a software license for the product described.

60 APPLIED LIFE SCIENCES↗
NASA NTRS2014

The Impact of Microphysics and Planetary Boundary Layer Physics on Model Simulation of U.S. Deep South Summer Convection

Inspection of output from various configurations of high-resolution, explicit convection forecast models such as the Weather Research and Forecasting (WRF) model indicates significant sensitivity to the choices of model physics pararneterizations employed. Some of the largest apparent sensitivities are related to the specifications of the cloud microphysics and planetary boundary layer physics packages. In addition, these sensitivities appear to be especially pronounced for the weakly-sheared, multicell modes of deep convection characteristic of the Deep South of the United States during the boreal summer. Possible ocean-land sensitivities also argue for further examination of the impacts of using unique ocean-land surface initialization datasets provided by the NASA Short-term Prediction Research and Transition (SPoRn Center to select NOAAlNWS weather forecast offices. To obtain better quantitative understanding of these sensitivities and also to determine the utility of the ocean-land initialization data, we have executed matrices of regional WRF forecasts for selected convective events near Mobile, AL (MOB), and Houston, TX (HGX). The matrices consist of identically initialized WRF 24-h forecasts using any of eight microphysics choices and any of three planetary boWldary layer choices. The resulting 24 simulations performed for each event within either the MOB or HGX regions are then compared to identify the sensitivities of various convective storm metrics to the physics choices. Particular emphasis is placed on sensitivities of precipitation timing, intensity, and coverage, as well as amount and coverage oflightuing activity diagnosed from storm kinematics and graupel in the mixed phase layer. The results confirm impressions gleaned from study of the behavior of variously configured WRF runs contained in the ensembles produced each spring at the Center for the Analysis and Prediction of Storms, but with the benefit of more straightforward control of the physics package choices. The design of the experiments thus allows for more direct interpretation of the sensitivities to each possible physics combination. The results should assist forecasters in their efforts to anticipate and correct for possible biases in simulated WRF convection patterns, and help the modeling community refine their model parameterizations.

McCaul, Eugene W., Jr.↗
NASA NTRS2014

The Impacts of Microphysics and Planetary Boundary Layer Physics on Model Simulations of U. S. Deep South Summer Convection

Inspection of output from various configurations of high-resolution, explicit convection forecast models such as the Weather Research and Forecasting (WRF) model indicates significant sensitivity to the choices of model physics parameterizations employed. Some of the largest apparent sensitivities are related to the specifications of the cloud microphysics and planetary boundary layer physics packages. In addition, these sensitivities appear to be especially pronounced for the weakly-sheared, multicell modes of deep convection characteristic of the Deep South of the United States during the boreal summer. Possible ocean-land sensitivities also argue for further examination of the impacts of using unique ocean-land surface initialization datasets provided by the NASA Short-term Prediction Research and Transition (SPoRT Center to select NOAA/NWS weather forecast offices. To obtain better quantitative understanding of these sensitivities and also to determine the utility of the ocean-land initialization data, we have executed matrices of regional WRF forecasts for selected convective events near Mobile, AL (MOB), and Houston, TX (HGX). The matrices consist of identically initialized WRF 24-h forecasts using any of eight microphysics choices and any of three planetary boundary layer choices. The resulting 24 simulations performed for each event within either the MOB or HGX regions are then compared to identify the sensitivities of various convective storm metrics to the physics choices. Particular emphasis is placed on sensitivities of precipitation timing, intensity, and coverage, as well as amount and coverage of lightning activity diagnosed from storm kinematics and graupel in the mixed phase layer. The results confirm impressions gleaned from study of the behavior of variously configured WRF runs contained in the ensembles produced each spring at the Center for the Analysis and Prediction of Storms, but with the benefit of more straightforward control of the physics package choices. The design of the experiments thus allows for more direct interpretation of the sensitivities to each possible physics combination. The results should assist forecasters in their efforts to anticipate and correct for possible biases in simulated WRF convection patterns, and help the modeling community refine their model parameterizations.

McCaul, E. W., Jr.↗
DOE OSTI2019

Dilute Source CO 2 Capture: Management of Atmospheric Coal-Produced Legacy Emissions (Final Technical Report)

This report presents the scientific and technical findings by Carbon Engineering Ltd (CE) resulting from the DOE sponsored research and development project on Direct Air Capture (DAC) process. The work was performed by CE under the Award DE-FE0026861 from the US Department of Energy’s (DOE) National Energy Technology Laboratory (NETL). The work plan was centered on advancing the Recipient’s DAC technology, developing a better understanding of its performance and cost. The report outlines the results from two key activities that were performed under this award: Studying the performance characteristics of the DAC plant at both pilot and lab scale Techno-economic analysis (TEA) to study two implementations of CE’s proprietary DAC process: i) An early commercial DAC process that captures ~1 MT CO 2 /year of atmospheric CO 2 (Baseline case), and ii) A modified early commercial DAC process that captures 1 MT CO 2 /year of flue gas CO 2 downstream of a subcritical PC post-CCS power plant (Case 1 Option C). Within the first key activity of the project, significant operational improvements and learnings were realized, including for key equipment in the DAC process in addition to the overall system. Learnings included impact of equipment design on the capture, calcination and slaking processes, and operational parameter modifications for optimizing the pelletization process, along with the complex interactions and associated considerations of these on overall system performance. In parallel, benchtop scale equipment was successfully utilized to validate pilot scale data, such as, retention in the pellet reactor, and capture performance parameters of the air contactor. CE continues to conduct core research and development activities; the learnings from this DOE project will contribute to the design, testing and operation activities of CE’s fully integrated DAC validation plant, as well as the first commercial DAC plant. The second key activity incorporated the above learnings, as well as input from vendors and EPC experts, to produce the TEA report. More specifically, the TEA results were based on measured pilot plant performance data for all major unit operations, and on an energy and material balance for the process at commercial scale computed using an Aspen One® process simulation. The cost estimation methodology follows AACE International recommended practices; with an uncertainty range of -15%/+30%. Depending on financial assumptions, energy costs, and the specific choice of inputs and outputs, the levelized cost per ton CO 2 captured from the atmosphere for an early commercial plant for the baseline case ranges from 227 to 304 $/t-CO 2 . And, the levelized cost to capture 98% of the flue gas CO 2 emissions in a subcritical PC power plant (Case 1 Option C) through a combination of Cansolv carbon capture system and a modified DAC system was estimated to range from 109 to 160 $/MWh of net power generated. Process improvement opportunities identified during this work, in addition to cost reduction opportunities identified during the Value Engineering exercise associated with the Baseline case indicate that significant reductions in capital costs are available as CE scales up and proceeds through deployment of initial facilities.

20 FOSSIL-FUELED POWER PLANTS↗
DOE OSTI2021

Flow Redirection and Induction in Steady State (FLORIS) Wind Plant Power Production Data Sets

This dataset contains turbine- and plant-level power outputs for 252,500 cases of diverse wind plant layouts operating under a wide range of yawing and atmospheric conditions. The power outputs were computed using the Gaussian wake model in NREL's FLOw Redirection and Induction in Steady State (FLORIS) model, version 2.3.0. The 252,500 cases include 500 unique wind plants generated randomly by a specialized Plant Layout Generator (PLayGen) that samples randomized realizations of wind plant layouts from one of four canonical configurations: (i) cluster, (ii) single string, (iii) multiple string, (iv) parallel string. Other wind plant layout parameters were also randomly sampled, including the number of turbines (25-200) and the mean turbine spacing (3D-10D, where D denotes the turbine rotor diameter). For each layout, 500 different sets of atmospheric conditions were randomly sampled. These include wind speed in 0-25 m/s, wind direction in 0 deg.-360 deg., and turbulence intensity chosen from low (6%), medium (8%), and high (10%). For each atmospheric inflow scenario, the individual turbine yaw angles were randomly sampled from a one-sided truncated Gaussian on the interval 0 deg.-30 deg. oriented relative to wind inflow direction. This random data is supplemented with a collection of yaw-optimized samples where FLORIS was used to determine turbine yaw angles that maximize power production for the entire plant. To generate this data, a subset of cases were selected (50 atmospheric conditions from 50 layouts each for a total of additional 2,500 cases) for which FLORIS was re-run with wake steering control optimization. The IEA onshore reference turbine, which has a 130 m rotor diameter, a 110 m hub height, and a rated power capacity of 3.4 MW was used as the turbine for all simulations. The simulations were performed using NREL's Eagle high performance computing system in February 2021 as part of the Spatial Analysis for Wind Technology Development project funded by the U.S. Department of Energy Wind Energy Technologies Office. The data was collected, reformatted, and preprocessed for this OEDI submission in May 2023 under the Foundational AI for Wind Energy project funded by the U.S. Department of Energy Wind Energy Technologies Office. This dataset is intended to serve as a benchmark against which new artificial intelligence (AI) or machine learning (ML) tools may be tested. Baseline AI/ML methods for analyzing this dataset have been implemented, and a link to their repository containing those models has been provided. The .h5 data file structure can be found in the GitHub repository under explore_wind_plant_data_h5.ipynb.

AI↗