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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

Requirements Description of GAMSOR and GAMSRC

This report presents the modeling and simulation capabilities of Argonne National Laboratory’s GAMSOR and GAMSRC code that is used in present reactor analysis activities. These capabilities will then be used to establish the set of verification tasks necessary to verify GAMSOR for use on commercial projects. A similar approach was taken for the PERSENT, REBUS and DIF3D software packages. The GAMSOR program is a modified version of DIF3D used to obtain the coupled neutron gamma flux distributions for a given reactor system. GAMSOR was first developed in the 1980s and its work has progressed in parallel with DIF3D throughout its various development phases. While GAMSOR is compatible with DIF3D-FD and DIF3D-Nodal, DIF3D-VARIANT is most commonly used today and will be the focus of the present verification effort. As covered in [1], GAMSOR was built to provide more accurate heating distributions by explicitly solving for both the gamma and neutron heating distributions. Without this consideration, gamma heat is normally assumed to be deposited locally at the site of gamma production. This is acceptable for modeling some phenomena because the total energy production is preserved, but it can lead to inaccurate approximations of fluence-dependent behaviors such as fuel performance, swelling, cladding embrittlement, radiation induced creep, and power peaking. The outputs from GAMSOR are contained in multiple intermediate and final output files: FIXSRC, VARSRC, GTFLUX, GHFLUX, PWDINT, NPDINT, and GPDINT. DIF3D normally produces a PWDINT file containing power density results, with the assumption that gamma rays are absorbed where they are emitted (i.e., energy deposition is local with no modeled transport). With GAMSOR, neutron power density (NPDINT) and gamma power density (GPDINT) are both produced, with their summed power densities replacing the results in PWDINT for total heating (i.e., recoverable) power density.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2025

Applying Gaussian Process Machine Learning and Modern Probabilistic Programming to Satellite Data to Infer CO 2 Emissions

Satellite data provides essential insights into the spatiotemporal distribution of CO 2 concentrations. However, many atmospheric inverse models fail to adequately incorporate the spatial and temporal correlations inherent in satellite observations and often lack rigorous methods for estimating parameters like spatial length scales. We introduce an inference model that processes the spatiotemporal covariance in satellite data and estimates hyperparameters such as covariance length scales. Our approach uses the Gaussian process (GP) machine learning (ML) and modern probabilistic programming languages (PPLs) to perform atmospheric inversions of emissions from satellite data. We develop a GP ML inversion system based on modern PPLs and the GEOS-Chem chemical transport model, simulating atmospheric CO 2 concentrations corresponding to the Orbiting Carbon Observatory-2/3 (OCO-2/3) data for July 2020. In our supervised learning framework, we treat the GEOS-Chem simulated data set as the target, with predictors derived by scaling the target with sector-specific factors hidden from the GP machine. Our results show that the GP model, combined with GPU-enabled PPLs, effectively retrieves true emission scaling factors and infers noise levels concealed within the data. This suggests that our method could be applied over larger areas with more complex covariance structures, enabling comprehensive analysis of the spatiotemporal patterns observed in OCO-2/3 and similar satellite data sets.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Real-World Cyber Security Demonstration for Networked Electric Drives

In this article, we present the design and implementation of a cyber-physical security testbed for networked electric drive systems, aimed at conducting real-world security demonstrations. To our knowledge, this is one of the first security testbeds for networked electric drives, seamlessly integrating the domains of power electronics and computer science, and cybersecurity. By doing so, the testbed offers a comprehensive platform to explore and understand the intricate and often complex interactions between cyber and physical systems. The core of our testbed consists of four electric machine drives, meticulously configured to emulate small-scale but realistic information technology (IT) and operational technology (OT) networks. This setup both provides a controlled environment for simulating a wide array of cyber-attacks, and mirrors potential real-world attack scenarios with a high degree of fidelity. The testbed serves as an invaluable resource for the study of cyber-physical security, offering a practical and dynamic platform for testing and validating cybersecurity measures in the context of networked electric drive systems. As a concrete example of the testbed's capabilities, we have developed and implemented a Python-based script designed to execute step-stone attacks over a wireless local area network (WLAN). This script leverages a sequence of target IP addresses, simulating a real-world attack vector that could be exploited by adversaries. To counteract such threats, we demonstrate the efficacy of our developed cyber-attack detection algorithms, which are integral to our testbed's security framework. Furthermore, the testbed incorporates a real-time visualization system using InfluxDB and Grafana, providing a dynamic and interactive representation of networked electric drives and their associated security monitoring mechanisms. This visualization component not only enhances the testbed's usability but also offers insightful, real-time data for researchers and practitioners, thereby facilitating a deeper understanding of cyber-physical security dynamics in networked electric drive systems.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2023

SCALE 6.2 Lattice Physics Performance Assessment

The US Nuclear Regulatory Commission relies on the lattice physics analysis capabilities of the SCALE code system to perform confirmatory licensing analyses. Either SCALE lattice physics code—TRITON/NEWT or Polaris—can be used to generate cross section data used by the PARCS nodal core simulator for full-core neutronics calculations. This report presents an assessment of the accuracy of SCALE lattice physics codes for preparation of lattice physics data that are used to support simulator codes such as the NRC’s PARCS, for UO 2 -mixed oxide (MOX)/Zr fueled light water reactor (LWR) analyses. Due to the nature of lattice physics calculations, critical reactor experiment benchmarks cannot be modeled in explicit detail in a lattice physics code. However, this limitation does not mean that these measurement data are not usable for lattice physics studies. Therefore, either geometry approximations or axial buckling must be implemented to determine the critical water height. These modeling limitations have led to development of a three-phase assessment strategy. In the first phase, selected critical experiment benchmarks are modeled using the SCALE 3D continuous-energy (CE) Monte Carlo (MC) code KENO, which is the most rigorous neutron transport method available in SCALE, with no approximations in the spatial, angle, or energy treatments. Biases and statistical uncertainties in quantities of interest such as k eff and pin power distributions are determined by comparing CE KENO results to experimental data. This first phase in the accuracy assessment is to demonstrate and establish the use of CE KENO as a reference solution for the second phase. In the second phase, 14 numerical test suites are used to compare SCALE lattice physics calculations with CE KENO as a reference solution. In the third phase, SCALE lattice physics depletion calculations are performed, and the spent fuel isotopic results are compared with available radiochemical assay measurements. Isotopic measurement comparisons provide quantitative assessment of isotopic density distribution predictions with the depletion models in SCALE lattice physics codes. This report documents results for all test suites. The assessment was performed using standard production techniques unless otherwise noted. Both TRITON/NEWT and Polaris exhibited acceptable accuracy for most test cases. For the few test cases in which acceptable accuracy criteria were not met, further code and data development are planned. The computer codes used in this assessment are as follows: SCALE 6.2 rev19189 (pre-release of 6.2.1) was used for CE KENO, TRITON/NEWT and Polaris (PWR only) calculations; SCALE 6.2.2 was used for Polaris calculations to address several updates in support of support boiling water reactor (BWR) lattice geometries. For this work, there is no difference between SCALE 6.2.1 and SCALE 6.2.2 beyond the inclusion of the Polaris BWR analysis capability; The ENDF/B-VII.1 continuous energy and 252 group libraries deployed in SCALE 6.2 were generated with AMPX 6.2 that is distributed with SCALE.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2025

Learning robust parameter inference and density reconstruction in flyer plate impact experiments

Estimating physical parameters or material properties from experimental observations is a common objective in many areas of physics and material science. In many experiments, especially in shock physics, radiography is the primary means of observing the system of interest. However, radiography does not provide direct access to key state variables, such as density, which prevents the application of traditional parameter estimation approaches. Here we focus on flyer plate impact experiments on porous materials, and resolving the underlying parameterized equation of state (EoS) and crush porosity model parameters given radiographic observation(s). We use machine learning as a tool to demonstrate with high confidence that using only high impact velocity data does not provide sufficient information to accurately infer both EoS and crush model parameters, even with fully resolved density fields or a dynamic sequence of images. We thus propose an observable data set consisting of low and high impact velocity experiments/simulations that capture different regimes of compaction and shock propagation, and proceed to introduce a generative machine learning approach which produces a posterior distribution of physical parameters directly from radiographs. We demonstrate the effectiveness of the approach in estimating parameters from simulated flyer plate impact experiments, and show that the obtained estimates of EoS and crush model parameters can then be used in hydrodynamic simulations to obtain accurate and physically admissible density reconstructions. Finally, we examine the robustness of the approach to model mismatches, and find that the learned approach can provide useful parameter estimates in the presence of out-of-distribution radiographic noise and previously unseen physics, thereby promoting a potential breakthrough in estimating material properties from experimental radiographic images.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Integrated edge-to-exascale workflow for real-time steering in neutron scattering experiments

We introduce a computational framework that integrates artificial intelligence (AI), machine learning, and high-performance computing to enable real-time steering of neutron scattering experiments using an edge-to-exascale workflow. Focusing on time-of-flight neutron event data at the Spallation Neutron Source, our approach combines temporal processing of four-dimensional neutron event data with predictive modeling for multidimensional crystallography. At the core of this workflow is the Temporal Fusion Transformer model, which provides voxel-level precision in predicting 3D neutron scattering patterns. The system incorporates edge computing for rapid data preprocessing and exascale computing via the Frontier supercomputer for large-scale AI model training, enabling adaptive, data-driven decisions during experiments. This framework optimizes neutron beam time, improves experimental accuracy, and lays the foundation for automation in neutron scattering. Although real-time experiment steering is still in the proof-of-concept stage, the demonstrated potential of this system offers a substantial reduction in data processing time from hours to minutes via distributed training, and significant improvements in model accuracy, setting the stage for widespread adoption across neutron scattering facilities and more efficient exploration of complex material systems.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Model-based iterative reconstruction with adaptive regularization for artifact reduction in electron tomography

Obtaining high-quality 3D reconstructions from electron tomography of crystalline particles embedded in lighter support elements is crucial for various material systems such as catalysts for fuel cell applications. However, significant challenges arise due to the limited tilt range, sparse and low signal-to-noise ratio of the measurements. In addition, small metal particles can cause strong streaking and shading artifacts in the 3D reconstructions when using conventional reconstruction algorithms due to the presence of Bragg diffraction and the large scattering cross-section difference between the materials of the particles and the background support regions. These artifacts lead to errors in the downstream characterization affecting extraction of critical features such as the size of the metal particles, their distribution and the volume of the lighter support regions. In this paper, we present a two-stage algorithm based on metal artifact reduction, utilizing model-based iterative reconstruction methods with adaptive adjustment of regularization parameters. Our approach yields high-quality 3D reconstructions compared to traditional algorithms, accurately capturing both the metal particles as well as the background support. We demonstrate the effectiveness of our algorithm through simulated and experimental bright-field electron tomography data, showing significant improvements in reconstruction quality compared to traditional methods.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Editorial: Particulate Matter Emissions From Conventional and Reformulated Fuel Combustion: Advances in Experiments and Simulations

Particulate matter (PM or soot) emissions generated by combustion of conventional and reformulated fuels adversely impact the environment and human health (Bond et al., 2013; Landrigan et al., 2018; World Health Organization (WHO), 2016), generating public awareness and motivating efforts towards the mitigation of their harmful effects. Soot formation is one of the most complex phenomena in combustion, involving interactions between combustion chemistry, fluid mechanics, mass/heat transport, and particle dynamics, spanning different spatial and temporal scales. A fundamental understanding of soot formation process is thus necessary to achieve a strong reduction of PM emissions and design cleaner and more efficient combustion systems. This has motivated a longstanding and ongoing research activity aimed at improving our understanding of the physical and chemical processes involved in soot formation, well reviewed in these recent works (Wang and Chung, 2019; Michelsen et al., 2020; Martin et al., 2022). Despite the wide interest, the transition from gas-phase molecules to incipient soot particles is still elusive and the successive particle growth and oxidation processes are far from being fully understood, especially in conditions relevant to real-world applications. Recent advances in combustion PM emission diagnostic and computational capabilities helped in improving the predictability of fundamental chemical and aerosol models for practical applications, thus tackling some of the above-mentioned challenges. The aim of this research topic is to display the ongoing research efforts in addressing the existing gaps on particulate formation from various fuel sources and in conditions typical of practical combustion applications (e.g. flames, engines, pool fires), through both experimental and numerical approaches.

42 ENGINEERING↗
DOE OSTI2022

Development of Numerical Model of Metal Foam with PCM for the Estimation of Effective Thermal Conductivity

Global warming due to climate change is a threat to humankind. Nuclear energy is one of the promising solutions to reduce fossil fuel usage. Nuclear energy can handle the base load, compensating for the volatility of renewable energy. If nuclear energy could achieve load following capability, the combination with renewable energy would be more suitable. Thermal energy storage (TES) is one of the options for enabling load following of nuclear reactors. The TES makes it possible to store surplus nuclear thermal energy and release it later as needed. In Idaho National Laboratory (INL), a new concept of latent heat TES integrated with high-temperature heat pipe has been proposed and is under development, which is called Heat pipe-Integrated Thermal Battery (HITB). HITB exchanges thermal energy between the reactor system and TES via heat pipe. The heat transferred to TES medium, made of phase change material (PCM), stores energy as sensible heat and/or latent heat. As PCM typically has poor thermal conductivity, however, various heat transfer enhancement techniques are required to achieve a rapid charging cycle. There are many techniques to enhance the heat transfer ability of TES medium such as disk, fin, and metal foam. Among them, metal foam is an appropriate option to enhance the heat transfer because it maximizes the heat transfer area through metal wicks. Metal foam is a lightweight metal structure that has a high porosity of over 0.9. The typical materials for metal foam are Aluminum, Copper, Nickel, and Silicon Carbide (SiC). Metal foam not only enhances heat transfer via conduction but also increases contact surface area. In the HITB design , the metal foam is being considered as one of the options to enhance the heat transfer of TES medium (PCM) [1]. To predict the enhanced thermal performance of TES, one should properly estimate the effective thermal conductivity of metal foam combined with PCM material or calculate heat transfer in distributed model. There are many experimental works that provides effective thermal conductivity of metal foam with various PCM [2,3]. Also, many theoretical models were developed based on the unit cell model of metal foam [4,5]. With a distributed model, on the other hand, detail heat transfer characteristics between metal foam and PCM material can be analyzed considering the geometry or buoyancy effect. However, due to the complex geometry of metal foam pores, the computational cost for three-dimensional modeling highly increases. Therefore, if metal foam structure can be modeled in simple and repetitive design, the computational cost would decrease Among the various metal foam models [2], lattice model is one of the simple and extendable design. The porosity and pores per inch (PPI) can be characterized by the size and spatial distance of lattice structure. If the three-dimensional metal foam model consists of lattice structure could properly estimate the heat transfer, which is characterized by effective thermal conductivity, it would be a good option to assess the thermal performance of metal foam with PCM. In this study, a three-dimensional numerical model was developed to simulate conductive heat transfer between metal foam and PCM. The three-dimensional lattice structure of square pillars was selected as a basic structure of the metal foam. The calculation result was characterized by the effective thermal conductivity of the whole domain. A sensitivity study was conducted for mesh size, domain size, and PPI to check whether the calculation result gives a converged result or not. Lastly, the effective thermal conductivity from the lattice model was compared with existing experimental data to validate the model result

25 ENERGY STORAGE↗
DOE OSTI2022

FORCE-DISPATCHES Integration - Initial Demonstration

Integrated energy systems (IES) combine, in mutually beneficial ways, power from variable renewable energy sources and nuclear power plants (NPP) to improve economic viability under uncertain market and weather conditions. The open-source Framework for Optimization of Resources and Economics (FORCE) tool suite, developed at Idaho National Laboratory (INL), has enabled comprehensive modeling and simulation of IES. The capabilities within FORCE include grid portfolio optimization through the Holistic Energy Resource Optimization Network (HERON) and the transient process model analysis library HYBRID, among others. Continuous efforts and investments from the IES programs have been made to expand and improve the versatility of the FORCE toolset in fiscal year 2022. Code-coupling and cross-tool communication have been important methods for improving this versatility. This report focuses on an additional workflow in the HERON tool for capacity and dispatch stochastic optimization through integration with the external tool Design Integration and Synthesis Platform to Advance Tightly Coupled Hybrid Energy Systems (DISPATCHES). DISPATCHES was primarily developed by the National Energy Technology Laboratory, in collaboration with other national laboratories, which included INL, universities, and industry partners. It is coupled to a library of algebraic models for specific plant components, and to a framework for stochastic optimization different from that provided in the current Risk Analysis Virtual Environment (RAVEN)-running-RAVEN algorithm in HERON. HERON currently conducts stochastic optimization via an outer-inner loop: it optimizes over variable capacity on the outer loop, and at each step within the capacity parameter space, conducts an inner optimization over scenarios (of market signals, demand, and/or weather patterns) and hourly dispatch throughout a user-specified number of years. On the other hand, DISPATCHES conducts stochastic optimization via an “all-at-once” strategy in which capacity variables are optimized at the same level as dispatch variables, as all scenarios are considered at once. The latter method works especially well for projects of limited size and project length, as the necessary computational power and memory increases with the number of variables and scenarios. The new capability to use the DISPATCHES workflow in HERON enhances standalone simulations by leveraging FORCE tools—namely, the economic metrics from the Tool for Economic Analysis (TEAL) and reduced-order model (ROM) sampling from RAVEN. The initial demonstration of the DISPATCHES workflow simulates an existing nuclear-case flowsheet within the DISPATCHES repository—this models a NPP with a secondary revenue stream for hydrogen production. Electrical output from the plant is converted to hydrogen via a proton-exchange membrane (PEM) electrolyzer, hydrogen tanks are used for storage, and an additional turbine is added for hydrogen combustion. Continued work regarding this FORCE-DISPATCHES integration will include automatic generation of DISPATCHES models from HERON inputs, offering analysts the option of using either the RAVEN-runsRAVEN or DISPATCHES workflow to solve technoeconomic optimization problems.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2020

Model Development for Multi-Component Fuel Vaporization and Flash Boiling

The objectives of this project are to improve the multi-component fuel droplet and film vaporization models used in internal combustion engine simulation, and to develop a comprehensive model to predict the characteristics of multi-component flash boiling spray. This work explores two approaches to fuel composition treatment for modeling multi-component fuel vaporization: one based on discretization and surrogates, and the other based on continuous thermodynamic distribution of fuel properties. The experimental data collected for model validation are done under three fuel form factors: droplet, spray, and thin film. The study on sprays also include experimentation under non-flash and flash boiling conditions, a phenomenon that enhances fuel vaporization. The main goals of this work are: Design and develop a multi-component fuel droplet and wall film vaporization model using both discrete and continuous thermodynamics methods. Design and develop an analytical model for multi-component flash boiling. Integrate the multi-component droplet and film model into multi-dimensional engine calculations to predict the fuel vaporization process under engine operation condition. Conduct multi-component droplet and fuel film vaporization experiments in a non-combusting chamber to verify the proposed vaporization models. Characterize flash boiling phenomena of multi-component fuel sprays by optical and laser diagnostic techniques. This report will detail the experimental setup and the numerical basis for developing a model to achieve the main goals listed above. Key features of observed multi-component fuel vaporization will be summarized at the end of each experimental sections, and corresponding model performance evaluation will be presented at the end of each model development section.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2024

Integrated protonic ceramic electrochemical cell for sustainable energy economy using water-energy nexus framework

Reliance on fossil fuels will continue for the next decades even though there are global pushes away from it to mitigate the overarching climate challenge, most especially by its highest consumers and availability. While there is a hastening global shift away from fossil fuel, integrating its assets into this technology helps limit the risk and future losses of stranded assets and reduce the cost of investment in the new technologies. Moreover, the generation of electricity from intermittent renewable sources like solar and wind has witnessed a significant surge in recent years, leading to a pressing demand for practical energy storage systems. Electrical energy storage is anticipated to play a pivotal role in the future global energy system, facilitating load-leveling operations to support the greater integration of renewable and distributed generation. Reversible electrochemical cells (RECs) offer a promising option for addressing the fossil fuel assets integration and energy storage challenges through the interconversion between electrical and chemical energy and concurrent utilizing carbon emission. In their electrolysis mode, the RECs convert electricity into durable, storable, and portable valuable chemical fuels such as syngas and methane. Conversely, the produced chemical fuels can be used as reactants in the fuel cell mode to generate electricity on demand with minimal (hydrocarbons) or zero when H2 or NH3 is used emissions. However, a challenging goal for this type of technology remains to achieve optimal operation and high roundtrip efficiencies, which has hindered the deployment of previous electrochemical cells. This dissertation demonstrates how reversible protonic ceramic electrochemical cells (RePCECs) can be integrated with fossil fuel power plants and renewable energy sources as a potential energy storage system. In this work, integrated RePCEC systems are designed and examined using computational modeling at scales to determine appropriate system configurations and operating conditions that achieve high roundtrip efficiencies. Cell level design of the PCEC is the first approach, several cells are assembled for the stack level model that is integrated into combined cycle powerplant and solar photovoltaic for the system level model. After critical literature review, this answered the operational and integration research questions proposed to address these challenges. The designed systems perform two functions, utilizing captured CO2 and storing renewable energy through co-electrolysis of steam and CO2. The co-electrolysis reaction involves endothermic water electrolysis and exothermic methanation reaction. To enhance high roundtrip efficiency, there is a need for thermal balance and management in the electrolysis mode. This involves operating the RePCEC stack under conditions that favor methane production to balance out heat needed by water electrolysis, it crucial for the RePCEC system operation. Methanation is enhanced by low temperatures. Leveraging on fabricated BCZYYb-electrolyte RePCEC, the cell model designed revealed that the optimum temperature for methane production is 450℃ at atmospheric pressure. Thus, to achieve optimum system performance, operating in the temperature range 450-525℃ is recommended at the given configuration, combining between the optimum temperature for methane production and temperature for the optimum stack roundtrip efficiency. Configuration with carbon capture system and purge stream is the optimum configuration from the seven conceptualized and evaluated. The modeling outcomes include a thermodynamic examination of integrated RePCEC systems, calibration of cell and stack level models, and steady-state simulation and integration into a 600MW combined cycle power plant retrofitted with two two-stage membrane-based carbon capture system and a wastewater treatment and recovery unit. At 100% powerplant loading, the stack and system roundtrip efficiencies are 72% and 51.37% respectively. Adding a purge stream for produced hydrogen at the system downstream improves the efficiencies to 74 and 55.48% respectively. At atmospheric pressure and 525℃, the system model suggests that a stack roundtrip of 82% is achievable, and overall system efficiency increases by reducing the energy consumption by the balance of plant components for steam generation and storage. Economic analysis of the process gives levelized cost of methane as $2.24/MMBtu lower than the conventional production route that range between $3.46/MMBtu and $9.85/MMBtu. The lifecycle analysis shows that the global warming potential for the production of methane and hydrogen from the RePCEC system is 3.83 kg CO2 eq which is lower than 9.35 kg CO2 eq emission during steam methane reforming for hydrogen production. This answered both the environmental and economic concerns in the raised research question. The proposed RePCEC configuration and analysis carried out in this dissertation to address the surge in renewable energy and challenges with PCEC technology hold significant potential in achieving large-scale energy storage while simultaneously reducing carbon emissions. These advancements, coupled with suitable governmental policies and incentive programs, have the potential to economically disrupt the natural gas industries by using RePCEC systems for methane production, thereby making them more favorable for eventual implementation and commercialization.

25 ENERGY STORAGE↗
DOE OSTI2020

Models and Strategies for Optimal Demand Side Management in the Chemical Industries

Deregulation and the increase of renewable electricity generation from wind and solar photovoltaics have transformed the U.S. electricity market. Economic and environmental benefits notwithstanding, the presence of renewables has increased variability and uncertainty on the supply side of the grid. Managing demand, rather than generation – a strategy referred to as “demand response (DR)” – is an attractive approach for mitigating this imbalance. DR efforts aim to reduce electricity usage during peak demand times, lessening stress on the grid. Industrial users are particularly attractive entities for DR participation since they present large, localized loads that can provide significant relief on grid demand and –unlike other large loads, such as buildings – are minimally dependent on human needs and preferences. In this project, we accomplished three main objectives. (1) We developed data-driven low-order DR scheduling-relevant dynamic models of chemical processes. Concurrently, we studied the formulation and solution of the associated optimal DR production scheduling problems. (a) A prototype air separation unit (ASU) model was used to generate simulated operating data for initial modeling efforts, which enabled the later use of industrial data for data-driven modeling. (b) We utilized Hammerstein-Wiener (HW) and Finite Step Response (FSR) models to represent nonlinear plant dynamics. (c) The HW models were linearized using exact linearization so they could potentially be embedded in power system models, which are formulated as mixed integer linear programs (MILPs). (d) We solved DR optimization problems under uncertainty and found that even naïve predictions of electricity price and product demand led to significant cost savings benefits. (2) Our DR scheduling optimization problem formulations are amenable to real-time solution. (a) We utilized Lagrangian Relaxation (LR) to efficiently solve the optimization problem by decoupling subproblems linked by complicating constraints. (b) We have achieved computation times for the 3-day DR scheduling problem of an ASU as low as 1.88 minutes. (3) Our representations of the DR behavior of chemical process as grid-level batteries were embedded in power system models. (a) For a small-scale grid, we found that incorporating the dynamics of the chemical plant in the optimal power flow calculations resulted in better resource management leading to up to 15% and 46% cost reduction for the grid and chemical plant operations, respectively, during periods of power line congestion. We have published several works dedicated to modeling and solving DR optimization problems from the user side. These were published in top peer-reviewed journals and are summarized in this report. The most recent work (and papers in preparation) considers DR scheduling from the grid side. Future efforts will consider networked plants (e.g., air separation units operating on a common pipeline) for DR participation, which is expected to amplify the capabilities of industrial DR participants to perform load-shifting. Our consideration of uncertainty in DR has inspired future directions in this area as well: we plan to develop multistage methods to fully account for the effects of uncertainty in DR scheduling.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

Sparge Sampling of Molten Salts for Online Monitoring via Laser-Induced Breakdown Spectroscopy

A method was developed to sample molten salts by sparging to generate and transport aerosols to an isolated instrument for compositional analysis by laser-induced breakdown spectroscopy (LIBS). Real-time monitoring of molten salt composition is critical to developing molten salt nuclear reactors, which offer enhanced safety and efficiency. In this article, the sparge sampling method is described and compared with sampling using a Collison nebulizer. The size distribution and transport of aerosols produced from molten eutectic NaNO 3 –KNO 3 salt were compared for multiple gas flow rates (75–1200 mL min –1 ) and transport distances (0.68–2.61 m). Both methods produced aerosols ranging from 0.5 to 5.0 μm determined using a cascade impactor. Aerosols were effectively transported without pre- or trace-heating of gas lines, but transport efficiency was reduced by the formation of agglomerates. Sparge sampling was found to use less sample and less gas than a Collison nebulizer while producing a more concentrated aerosol stream (up to 5 μg L –1 ). The effects of laser energy and delay time on the signal quality of LIBS measurements of these aerosols were also studied. High energy and short delay times were found to enhance signal and repeatability, whereas signal-to-background and signal-to-noise ratios were highest at low energy and longer delay times. The capabilities of this system for online monitoring of molten salts were demonstrated with calibrations for Sr and Li with relative standard deviations of 2.6% and 1.5% and limits of detection of 380 and 180 μg g –1 , respectively.

Aerosols↗
DOE OSTI2023

Machine-Learning-aided Approach for Predicting the Thermal Expansion Behaviors in Advanced Test Reactor Capsules (NURETH-20 full paper)

Instrumented experiments at test reactors are essential to deploying new advanced reactor systems. Designing new experiments and generating data on specific conditions require both time and cost investment. A high-fidelity model of the experiment environment can be created using finite element analysis software to support the actual experiments, but computation time is still a concern in applying outcomes to real-time usage (e.g., a digital twin). This research proposes a machine-learning-aided approach to temperature and displacement predictions, based on the thickness of the outer gas gap on the experimental capsule used for the in-pile demonstration of a novel thermal conductivity probe in the Advanced Test Reactor. The capsule consisted of U10Zr fuel, a rodlet, sodium, and inner and outer capsules. There were gas gaps between the fuel and rodlet and between the inner and outer capsule. The learning data consisted of an experimental capsule’s radial distributions of temperature and displacement, as obtained from Abaqus and the physical features. For the first step, temperature was predicted using three positional parameters. Then the displacement was predicted using six different positional parameters. Each physical feature was normalized to be both nondimensional and standardized. The temperature and displacement predictions showed good agreement in all cases involving interpolation and extrapolation. Also, data similarity enhancement increased the similarity between training and target data increasing the predictive accuracy of machine-learning models. In some cases of extrapolation, the accuracy of the machine-learning model showed limited performance, but still data similarity enhancement improved the accuracy.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2026

Development of an Unbiased Future Solar Dataset for Solar Resource Adequacy Research Over CONUS

A high-resolution, long-term solar dataset is essential for capturing the variability of solar energy resources and informing strategies to ensure grid reliability and resilience in systems with high levels of solar energy integration. This study focuses on generating unbiased, high-resolution projections of solar irradiance through a statistical downscaling framework, using Earth system model (ESM) simulations obtained from the North American Coordinated Regional Climate Downscaling Experiment (NA-CORDEX). The National Solar Radiation Database (NSRDB) is used to calibrate statistical downscaling models. The newly developed dataset provides solar irradiance, surface air temperature, and surface wind speed at 4-km and hourly resolutions across the contiguous United States (CONUS), based on two future scenarios (RCP4.5 and RCP8.5). This study outlines key steps in developing the high-resolution future solar dataset, including (1) regridding ESM data to a common 20-km resolution grid, (2) correcting ESM biases using the NSRDB, and (3) applying temporal and spatial downscaling methods to generate high-resolution (4-km, hourly) solar projections. Preliminary results indicate that downscaled projections (4-km) captured reasonable spatial patterns when compared to observations across CONUS for four variables. On average across all pixels, 4-km daily-total GHI and DNI projections showed normalized bias (nBias) less than 1% and 6% for GHI and DNI against NSRDB, respectively (nBias less than 1% and 5% for daily-average surface air temperature and surface wind speed). In terms of long-term trend for GHI and DNI, there was no strong increasing or decreasing trend (when compared to surface air temperature), but it showed a very weak decreasing trend.

14 SOLAR ENERGY↗
DOE OSTI2021

Validation and Demonstration of Control System Functional Capabilities within the IES Plug-and-Play Simulation Environment

The concept of an integrated energy system (IES) is meant to combine different energy technologies in synergistic ways to achieve a more secure and economical energy supply. The RAVEN-based HYBRID framework is used to find the optimal installed capacity and the optimal economical dispatch of each component of the IES. The new RAVEN plugin for grid and capacity optimization (HERON) only addresses the limits that affect the production variables and the corresponding rates of variation (explicit constraints). However, other variables are subject to constraints, and the associated limits should be accounted for (implicit constraints). In particular, for the power dispatch problem, the optimization algorithm takes into account the limits on the electrical power output and the corresponding hourly power variations but does not consider other constraints on process variables whose response affects the service life of the IES. This report describes a scheme that allows accounting for implicit constraints without increasing the size of the optimization problem. To obtain a more accurate approximation of the nonlinear dynamic behavior, a parametric version of the dynamic mode decomposition with control (DMDc) algorithm was developed to derive the state-space representation matrices of the IES components at different scheduling parameter. Thanks to this approach, a more accurate approximation of the system response can be obtained, the limits imposed by thermal mechanical implicit constraints can be translated into power dispatch limits, and the feedbacks to HERON power dispatcher can be provided. To assess the developed methodology, a power dispatching test case composed of three power generating and storage units (Balance of Plant, Secondary Energy Source, Thermal Energy Storage) was developed. The power output of each one of the three units was optimized to meet the imposed time-dependent load demand trajectory and to maximize the IES profitability by meeting both the explicit and implicit constraints.

24 POWER TRANSMISSION AND DISTRIBUTION↗