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DOE OSTI2023

A biomass map of the Brazilian Amazon from multisource remote sensing

The Amazon Forest, the largest contiguous tropical forest in the world, stores a significant fraction of the carbon on land. Changes in climate and land use affect total carbon stocks, making it critical to continuously update and revise the best estimates for the region, particularly considering changes in forest dynamics. Forest inventory data cover only a tiny fraction of the Amazon region, and the coverage is not sufficient to ensure reliable data interpolation and validation. This paper presents a new forest above-ground biomass map for the Brazilian Amazon and the associated uncertainty both with a resolution of 250 meters and baseline for the satellite dataset the year of 2016 (i.e., the year of the satellite observation). A significant increase in data availability from forest inventories and remote sensing has enabled progress towards high-resolution biomass estimates. This work uses the largest airborne LiDAR database ever collected in the Amazon, mapping 360,000 km 2 through transects distributed in all vegetation categories in the region. The map uses airborne laser scanning (ALS) data calibrated by field forest inventories that are extrapolated to the region using a machine learning approach with inputs from Synthetic Aperture Radar (PALSAR), vegetation indices obtained from the Moderate-Resolution Imaging Spectroradiometer (MODIS) satellite, and precipitation information from the Tropical Rainfall Measuring Mission (TRMM). A total of 174 field inventories geolocated using a Differential Global Positioning System (DGPS) were used to validate the biomass estimations. The experimental design allowed for a comprehensive representation of several vegetation types, producing an above-ground biomass map varying from a maximum value of 518 Mg ha -1 , a mean of 174 Mg ha -1 , and a standard deviation of 102 Mg ha -1 . This unique dataset enabled a better representation of the regional distribution of the forest biomass and structure, providing further studies and critical information for decision-making concerning forest conservation, planning, carbon emissions estimate, and mechanisms for supporting carbon emissions reductions.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2026

Data-Driven Modeling of High-Resolution Residential Load Profiles Using Low-Resolution Smart Meter Measurements

Accurate and high-resolution residential load profiles are essential for power system modeling, demand response planning, and effective grid operation. As the energy sector moves towards a more actively managed distribution system, the ability to understand residential energy consumption at a minute-by-minute scale becomes increasingly critical. High-resolution load profiles provide key insights into demand patterns and user behavior, enabling grid operators to design more effective energy solutions; however, residential load measurements in the field are typically recorded at low resolutions, such as 15-60 minutes, which makes it hard to study the characteristics of different residential customers. This paper addresses these challenges by introducing a data-driven approach to generate realistic, high-resolution residential load profiles based on lowre-solution measurements and weather information. The proposed method retains the key features of the actual residential load measurements while offering appliance-level energy consumption details for each residential building. The results demonstrate the effectiveness of the proposed load profile generator, proving its capability to support utilities in optimizing residential energy management and ensuring a more reliable and resilient grid.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2025

Characterizing falling particle curtain receivers at commercially relevant scales: Research Performance Progress Report (RPPR-1)

Sandia will construct a cold flow receiver test platform in order to characterize falling particle curtain receivers at commercially relevant scales. While Sandia has extensive experience in R&D of falling particle curtain receivers, most have been at pilot scale and smaller - on the order of 1 MWth with characteristic dimensions of nominally 1-2 m to adequately collect solar energy from the heliostat field at the National Solar Thermal Test Facility (NSTTF). However, scaling up receivers to commercially relevant scales (25 MWth and above) will require a thorough understanding of particle curtain dynamics at larger scales, especially longer drop heights, for design certainty. The goal of this project will be to construct a cold falling particle curtain test rig capable of simulating particle characteristics that are expected in a commercial scale CSP plant, namely the drop height, curtain thickness, and particle mass flow rate (normalized by length of curtain). This will enable data collection on curtain opacity and spread, both of which are correlated to receiver efficiency and reliable construction, for commercially relevant scales. It will also permit validation of numerical models that will enable detailed receiver characterization and design past currently validated scales.

14 SOLAR ENERGY↗

DOE OSTI2024

Digital Tools for the Preventive Conservation of Built Heritage: The Church of Santa Ana in Seville

Historic Building Information Modelling (HBIM) plays a pivotal role in heritage conservation endeavours, offering a robust framework for digitally documenting existing structures and supporting conservation practices. However, HBIM’s efficacy hinges upon the implementation of case-specific approaches to address the requirements and resources of each individual asset and context. This paper defines a flexible and generalisable workflow that encompasses various aspects (i.e., documentation, surveying, vulnerability assessment) to support risk-informed decision making in heritage management tailored to the peculiar conservation needs of the structure. This methodology includes an initial investigation covering historical data collection, metric and condition surveys and non-destructive testing. The second stage includes Finite Element Method (FEM) modelling and structural analysis. All data generated and processed are managed in a multi-purpose HBIM model. The methodology is tested on a relevant case study, namely, the church of Santa Ana in Seville, chosen for its historical significance, intricacy and susceptibility to seismic action. The defined level of detail of the HBIM model is sufficient to inform the structural analysis, being balanced by a more accurate representation of the alterations, through linked orthophotos and a comprehensive list of alphanumerical parameters. This ensures an adequate level of information, optimising the trade-off between model complexity, investigation time requirements, computational burden and reliability in the decision-making process. Field testing and FEM analysis provide valuable insight into the main sources of vulnerability in the building, including the connection between the tower and nave and the slenderness of the columns.

Chaves, Estefanía↗

DOE OSTI2022

Comparison of planetary boundary layer height from ceilometer with ARM radiosonde data

Abstract. Ceilometer measurements of aerosol backscatter profiles have been widely used to provide continuous planetary boundary layer height (PBLHT) estimations. To investigate the robustness of ceilometer-estimated PBLHT under different atmospheric conditions, we compared ceilometer- and radiosonde-estimated PBLHTs using multiple years of U.S. Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) ceilometer and balloon-borne sounding data at ARM fixed-location atmospheric observatories and from ARM mobile facilities deployed around the world for various field campaigns. These observatories cover from the tropics to the polar regions and over both ocean and land surfaces. Statistical comparisons of ceilometer-estimated PBLHTs from the Vaisala CL31 ceilometer data with radiosonde-estimated PBLHTs from the ARM PBLHT-SONDE Value-added Product (VAP) are performed under different atmospheric conditions including stable and unstable atmospheric boundary layer, low-level cloud-free conditions, and cloudy conditions at these ARM observatories. Under unstable conditions, good comparisons are found between ceilometer- and radiosonde-estimated PBLHTs at ARM low- and mid-latitude land observatories. However, it is still challenging to obtain reliable PBLHT estimations over ocean surfaces even using radiosonde data. Under stable conditions, ceilometer- and radiosonde-estimated PBLHTs have weak correlations. We compare different PBLHT estimations utilizing the Heffter, the Liu–Liang, and the bulk Richardson number methods applied to radiosonde data with ceilometer-estimated PBLHT. We find that ceilometer-estimated PBLHT compares better with the Liu–Liang method under unstable conditions and compares better with the bulk Richardson number method under stable conditions.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2023

Local position-space two-nucleon potentials from leading to fourth order of chiral effective field theory

We present local, position-space chiral NN potentials through four orders of chiral effective field theory ranging from leading order (LO) to next-to-next-to-next-to-leading order (N 3 LO, fourth order) of the Δ-less version of the theory. The long-range parts of these potentials are fixed by the very accurate πN LECs as determined in the Roy-Steiner equations analysis. At the highest order (N 3 LO), the NN data below 190 MeV laboratory energy are reproduced with the respectable χ 2 /datum of 1.45. A comparison of the N 3 LO potential with the phenomenological Argonne v 18 (AV18) potential reveals substantial agreement between the two potentials in the intermediate range ruled by chiral symmetry, thus, providing a chiral underpinning for the phenomenological AV18 potential. Our chiral NN potentials may serve as a solid basis for systematic ab initio calculations of nuclear structure and reactions that allow for a comprehensive error analysis. In particular, the order by order development of the potentials will make possible a reliable determination of the truncation error at each order. Our new family of local position-space potentials differs from existing potentials of this kind by a weaker tensor force as reflected in relatively low D-state probabilities of the deuteron (P D ≲ 4.0 % for our N 3 LO potentials) and predictions for the triton binding energy above 8.00 MeV (from two-body forces alone). As a consequence, our potentials may lead to different predictions when applied to light and intermediate-mass nuclei in ab initio calculations and, potentially, help solve some of the outstanding problems in microscopic nuclear structure.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2025

Phase-field modeling of diffusion bonding in 316H stainless steel: Impact of processing conditions on grain morphology and bonding quality

A novel multi-phase, multi-component phase‐field model is presented to study the diffusion bonding of 316H stainless steel. Combined with targeted experimental investigations, this model simulates the bond-growth process and predicts the bonding quality. Unlike previous models, our approach captures the simultaneous evolution of voids and grain structures, while quantifying bonding quality using defined bonding ratio. A comprehensive analysis of bond process control is performed by changing temperature, pressure and surface roughness observing the resulting bond structure, which is consistent with experimental observations and analytical predictions. Temperature is determined to be the dominant factor, with the transition from a flat to a robust bond occurring between 1000 °C and 1050 °C. At the ideal bonding temperature of 1050 °C, a surface roughness exceeding 0.6 μm or an applied stress below 4 MPa results in poor bonding quality. Beyond this, higher pressures and smoother surfaces reduce void size, accelerate void shrinkage, and lead to improved bond integrity. This diffuse-interface model can be extended to other material systems if supplied with appropriate thermodynamic and kinetic data. In conclusion, this makes it an effective modeling platform for optimizing high-temperature diffusion bonding and developing reliable bonded components such as compact heat exchangers.

Diffusion bonding↗

DOE OSTI2024

Photometric redshifts probability density estimation from recurrent neural networks in the DECam local volume exploration survey data release 2

Photometric wide-field surveys are imaging the sky in unprecedented detail. These surveys face a significant challenge in efficiently estimating galactic photometric redshifts while accurately quantifying associated uncertainties. In this work, we address this challenge by exploring the estimation of Probability Density Functions (PDFs) for the photometric redshifts of galaxies across a vast area of 17,000 square degrees, encompassing objects with a median 5 σ point-source depth of g = 24.3, r = 23 . 9 , i = 23.5, and z = 22.8 mag. Our approach uses deep learning, specifically integrating a Recurrent Neural Network architecture with a Mixture Density Network, to leverage magnitudes and colors as input features for constructing photometric redshift PDFs across the whole DECam Local Volume Exploration (DELVE) survey sky footprint. Subsequently, we rigorously evaluate the reliability and robustness of our estimation methodology, gauging its performance against other well-established machine learning methods to ensure the quality of our redshift estimations. Our best results constrain photometric redshifts with the bias of − 0 . 0013 , a scatter of 0.0293, and an outlier fraction of 5.1%. These point estimates are accompanied by well-calibrated PDFs evaluated using diagnostic tools such as Probability Integral Transform and Odds distribution. We also address the problem of the accessibility of PDFs in terms of disk space storage and the time demand required to generate their corresponding parameters.We present a novel Autoencoder model that reduces the size of PDF parameter arrays to one-sixth of their original length, significantly decreasing the time required for PDF generation to one-eighth of the time needed when generating PDFs directly from the magnitudes.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2025

srlife : a software tool for estimating the life of high temperature concentrating solar receivers. Part I – metallic receivers

Here, this paper introduces srlife, a tool for estimating the structural service life of concentrating solar power (CSP) receivers operating at high temperatures. Supporting both metallic and ceramic receiver designs, srlife is available as open-source software at https://github.com/applied-material-modeling/srlife and can be installed via the PyPi package manager (https://pypi.org). Given basic receiver geometry and incident heat flux, the tool performs thermohydraulic and structural analysis and estimates the life of a receiver. Designed for easy integration into a software stack, including solar field and levelized cost analysis, the tool can be utilized for optimizing receiver designs to meet service life and economic targets. This paper is Part I in a two-part series. Part I discusses the analysis process used to estimate the life of metallic receivers, along with a description of the required input data. Additionally, several heuristics applied within srlife can reduce analysis time significantly while maintaining accurate life estimations for metallic receivers when compared to full analyses. Several examples demonstrating the utility of srlife in receiver design are also discussed. Part II focuses on the life estimation of ceramic receivers, using time-dependent reliability analysis and various ceramic failure models implemented in srlife.

Creep-fatigue analysis↗

DOE OSTI2022

Analysis of Interpretable Data Representations for 4D-STEM Using Unsupervised Learning

Abstract Understanding the structure of materials is crucial for engineering devices and materials with enhanced performance. Four-dimensional scanning transmission electron microscopy (4D-STEM) is capable of mapping nanometer-scale local crystallographic structure over micron-scale field of views. However, 4D-STEM datasets can contain tens of thousands of images from a wide variety of material structures, making it difficult to automate detection and classification of structures. Traditional automated analysis pipelines for 4D-STEM focus on supervised approaches, which require prior knowledge of the material structure and cannot describe anomalous or deviant structures. In this article, a pipeline for engineering 4D-STEM feature representations for unsupervised clustering using non-negative matrix factorization (NMF) is introduced. Each feature is evaluated using NMF and results are presented for both simulated and experimental data. It is shown that some data representations more reliably identify overlapping grains. Additionally, real space refinement is applied to identify spatially distinct sample regions, allowing for size and shape analysis to be performed. This work lays the foundation for improved analysis of nanoscale structural features in materials that deviate from expected crystallographic arrangement using 4D-STEM.

Bruefach, Alexandra (ORCID:0000000209323477)↗

DOE OSTI2025

Characterizing falling particle curtain receivers at commercially relevant scales: Research Performance Progress Report (RPPR-1)

Sandia will construct a cold flow receiver test platform in order to characterize falling particle curtain receivers at commercially relevant scales. While Sandia has extensive experience in R&D of falling particle curtain receivers, most have been at pilot scale and smaller - on the order of 1 MW th with characteristic dimensions of nominally 1-2 m to adequately collect solar energy from the heliostat field at the National Solar Thermal Test Facility (NSTTF). However, scaling up receivers to commercially relevant scales (25 MW th and above) will require a thorough understanding of particle curtain dynamics at larger scales, especially longer drop heights, for design certainty. The goal of this project will be to construct a cold falling particle curtain test rig capable of simulating particle characteristics that are expected in a commercial scale CSP plant, namely the drop height, curtain thickness, and particle mass flow rate (normalized by length of curtain). This will enable data collection on curtain opacity and spread, both of which are correlated to receiver efficiency and reliable construction, for commercially relevant scales. It will also permit validation of numerical models that will enable detailed receiver characterization and design past currently validated scales.

14 SOLAR ENERGY↗

DOE OSTI2021

Deep Learning for Automated Detection and Identification of Migrating American Eel Anguilla rostrata from Imaging Sonar Data

Adult American eels (Anguilla rostrata) are vulnerable to hydropower turbine mortality during outmigration from growth habitat in inland waters to the ocean where they spawn. Imaging sonar is a reliable and proven technology for monitoring of fish passage and migration; however, there is no efficient automated method for eel detection. We designed a deep learning model for automated detection of adult American eels from sonar data. The method employs convolution neural network (CNN) to distinguish between 14 images of eels and non-eel objects. Prior to image classification with CNN, background subtraction and wavelet denoising were applied to enhance sonar images. The CNN model was first trained and tested on data obtained from a laboratory experiment, which yielded overall accuracies of >98% for image-based classification. Then, the model was trained and tested on field data that were obtained near the Iroquois Dam located on the St. Lawrence River; the accuracy achieved was commensurate with that of human experts.

13 HYDRO ENERGY↗

DOE OSTI2020

Neural networks for modeling and control of particle accelerators

Charged particle accelerators support a wide variety of scientific, industrial, and medical applications. They range in scale and complexity from systems with just a few components for beam acceleration and manipulation, to large scientific user facilities that span many kilometers and have hundreds-to-thousands of individually-controllable components. Specific operational requirements must be met by adjusting the many controllable variables of the accelerator. Meeting these requirements can be challenging, both in terms of the ability to achieve specific beam quality metrics in a reliable fashion and in terms of the time needed to set up and maintain the optimal operating conditions. One avenue toward addressing this challenge is to incorporate techniques from the fields of machine learning (ML) and artificial intelligence (AI) into the way particle accelerators are modeled and controlled. While many promising approaches within AI/ML could be used for particle accelerators, this dissertation focuses on approaches based on neural networks. Neural networks are particularly well-suited to modeling, control, and diagnostic analysis of nonlinear systems, as well as systems with large parameter spaces. They are also very appealing for their ability to process high-dimensional data types, such as images and time series (both of which are ubiquitous in particle accelerators). In this work, key studies that demonstrated the potential utility of modern neural network-based approaches to modeling and control of particle accelerators are presented. The context for this work is important: at the start of this work in 2012, there was little interest in AI/ML in the particle accelerator community, and many of the advances in neural networks and deep learning that enabled its present success had not yet been made at that time. As such, this work was both an exploration of possible application areas and a generator of proof-of-concept demonstrations in these areas.

43 PARTICLE ACCELERATORS↗

DOE OSTI2024

An iterative method to deblend AGN-Host contributions for Integral Field spectroscopic observations

ABSTRACT We present a new iterative deblending method to separate the host galaxy (HG) and their Active Galactic Nuclei (AGNs) emission with the use of Integral Field spectroscopic (IFS) data. The method decomposes the resolved HG emission from the unresolved AGN emission by modelling the two-dimensional surface brightness (SB) profile of the point-spread function (PSF) and the two-dimensional SB HG continuum simultaneously per each monochromatic slide. Our method does not require any prior information about the observed SB profile or a detailed fitting of the PSF, making it ideal for the automatic analysis of large galaxy samples. In this work, we test the quality of our method, its advantages, and its disadvantages. We test our method by using a set of IFS mock data cubes to quantify the reliability of our deblending process and further compare our method with the qdblend3d analysis tool. Furthermore, we applied our method to three data cubes selected from the MaNGA survey according to the dominance of either its HG or its AGN. We show that our deblending method is capable of disengaging the bright, non-resolved AGN emission from the HG continuum and its narrow emission lines. However, the decoupling depends on how well the IFS spatially resolves the PSF, and on the relative flux intensity of the HG-AGN. Therefore, the method is ideal for disentangling the bright-flux contribution from AGN-dominated spectra.

Ibarra-Medel, H. (ORCID:0000000297906313)↗

DOE OSTI2023

Cathode Side-emission Mitigation for Linear Induction Accelerators

Linear induction accelerators (LIAs) are one of the main technologies used in creating flash x-rays used for diagnosing hydrodynamic experiments. The quality of the data produced in these experiments is dependent on the dose of x-rays generated via bremsstrahlung radiation. There are several electron beam quantities that effect the amount of dose extracted from the bremsstrahlung radiation, such as beam instabilities, corkscrew motion, and beam emittance. Injector design is key in delivering well-behaved electron beams to the accelerator, and design choices include components such as anode-cathode (AK) gap dimensions based on required voltage and current, shroud shaping, and cathode type and composition. Thermionic cathodes have shown reliable performance in electron emission, however, if enough of the cathode side is subject to high-enough electric field values, electrons can be emitted. This issue is more likely to happen when the cathode is treated with materials that have lower work functions, which is common for cathodes used in LIAs. These extra electrons can work their way through the rest of the beam, inducing high non-linearities in phase space which can increase the emittance of this beam. In this paper we outline conducted trade-space studies where we examined different shaping parameters of the cathode shroud design to decrease the effect side electrons may have on the beam emittance. Particle trajectories are determined by electric potential contours across the face of the cathode shroud component, and so we hypothesize that the proper shaping of the cathode-shroud interface can tune the electric field to mitigate the effect of side electrons. Electron beam transport is simulated down the length of the injector and the beam quality is diagnosed by studying the emittance as a function of position. Acceptable emittance increase is recorded and noted to meet radiographic requirements.

42 ENGINEERING↗

DOE OSTI2022

AI-Enabled Robots for Automated Nondestructive Evaluation and Repair of Power Plant Boilers. Final Report

Boiler failure could cause loss of life and safety issues, cost hundreds of thousands of dollars in equipment repairs, property damage and production losses, and drive up the cost of electric power. Boiler maintenance is challenging and risky for inspectors working on scaffolding in confined hazardous spaces inside of a boiler and sometimes the space is hard to access. The operation is also time-consuming due to the large area of vertical structures for inspection and the tremendous effort needed for scaffolding. Recently, the use of robotics (e.g., drones and crawlers) in power plants for maintenance is growing rapidly. However, the existing robotics solutions show two notable technological gaps: no live repair capability, and no Artificial Intelligence (AI) for smart autonomy. The objective of this project is to develop an integrated autonomous robotic platform that is equipped with compact non-destructive evaluation (NDE) sensors to perform live inspection, operates onboard repair devices to perform live repair, and uses AI for intelligent data fusion and predictive analysis for automated and smart spatiotemporal inspection, analysis and repair of the furnace walls in coal-fired boilers. The approach to achieve the objective includes developing NDE sensors with signal processing techniques, designing and evaluating repair devices for robots based on fusion and solid-state technologies, and an autonomous robotic platform that can attach to and navigate on boiler furnace walls using magnetic drive tracks. The robot is also powered by AI to automate data gathering (e.g., 3D mapping and damage localization) and predictive analysis. This project has advanced the state-of-the-art by providing technological breakthroughs including compact NDE and repair tools for robots, AI capabilities for smart autonomy, and a robotic platform for automated boiler maintenance. This project has great potential to result in significant benefits including limiting or eliminating the need to send operators to assess difficult-to-access or hazardous areas, enabling automated live inspection and repair, avoiding time consuming scaffolding (especially for partial maintenance during unplanned outage), collecting comprehensive and well-organized data smartly, and avoiding or limiting the need for onsite or remote piloting technicians. The impacts can be tremendous in terms of the time and cost savings, reducing the risk for human operators, and increasing boiler reliability, usability, and efficiency. In addition, by developing the new technologies on the autonomous inspection and repair robot, by involving multiple undergraduate and graduate students working together with the faculty members on this project, and by generating knowledge and building up collaborations with industrial partners, this effort will significantly update the education capabilities, support long-term fundamental research, and maintain the leadership of Colorado School of Mines and Michigan State University in energy fields.

20 FOSSIL-FUELED POWER PLANTS↗

DOE OSTI2020

Benchmarking noise extrapolation with the OpenPulse control framework

Distilling precise estimates from noisy intermediate scale quantum (NISQ) data has recently attracted considerable attention Kandala et al., Nature (London) 567, 491 (2019). In order to augment digital qubit metrics, such as gate fidelity, we discuss analog error mitigability, i.e., the ability to accurately distill precise observable estimates, as a hybrid quantum classical computing benchmarking task. Specifically, using Rabi oscillations as a test program, we characterize single qubit error rates on IBM's Poughkeepsie superconducting quantum hardware, incorporate control-mediated noise dependence into a generalized rescaling protocol, and analyze how noise characteristics influence Richardson extrapolation-based error mitigation. Finally, our results identify regions in the space of Hamiltonian control fields and circuit depth which are most amenable to reliable noise extrapolation, as well as shed light on how low-level hardware characterization can be used as a predictive tool for uncertainty quantification in error-mitigated NISQ computations.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2023

Small Reactors in Microgrids: Technology Modeling and Selection (Net-Zero Microgrid Program Project Report)

This report demonstrates the capabilities of the net-zero microgrid (NZM) Xendee platform for modeling an SR module with electricity, heat extraction and thermal storage in microgrids configurations. The model effectively captures the most important technical and economic considerations for SR technology specific analysis: cost and operational characteristics of SR technology and financial costs and incentives. The model can analyze multiple scenarios to establish metrics for cost-competitive and zero-carbon microgrids connected to the grid or completely isolated. The model is fully integrated within the Xendee platform for modeling and analysis of clean energy microgrids with storage and generation from renewable energy sources. The model captures the capabilities, constraints, and nuances of SR by incorporating parameters related to plant economics, design efficiency and performance, plant operation and component and fuel lifespan. The cost and operational parameters modeled in the SR module are specific to the technology selected for integration in the microgrid. Cost parameters recognize advanced nuclear technology for modular production and installation based on economies of scale from factory manufacture and related commissioning, and cost reduction through technology maturation—first-of-a-kind (FOAK) and nth-of-a-Kind (NOAK). The cost parameters include installation, operations and maintenance (O&M), fuel refueling cycle, and reactor life. Installation cost reflects economies of scale due to unit sizing at scale and colocation. O&M economies of scale for both fixed- and variable-cost fuel life-cycle costs are incurred at every refueling interval, with separate front- and back-end fuel costs, as well as waste-handling and disposition costs. This report investigates key characteristics of different SR technologies suitable for microgrid applications, including design principles, sizing, coolant properties, temperature ratings, fuel structures, and life-cycle considerations. This also includes fuel technologies applicable to these SR systems, alongside strategies for nuclear-waste and spent-fuel management and approaches to address safety, security, and proliferation challenges. Four primary groups of SR technologies are examined: water-cooled, liquid-metal-cooled, high-temperature gas-cooled, and molten-salt-cooled systems. In this report, an initial guideline for technology selection is established, aligning the characteristics of the technologies with the requirements of microgrids. The selection of technology in a microgrid is influenced by various factors, including financial capacity, location and accessibility, demand type and characteristics, reliability and resilience requirements, area constraints, and the lifespan of the microgrid. The types of electrical and non-electrical applications within the microgrid also play a significant role in technology selection. The characteristics of SRs, such as their smaller size, modularity, transportability, long refueling interval, improved safety features, ability to operate in autonomous or semi-autonomous mode, and provision of high-grade heat, are particularly appealing for microgrids. Furthermore, a list of considerations for implementing SRs in microgrids is outlined. The SR model is created to be continuously improved with the acquisition of actual data on investment and operational costs, experience with supply chains, production at scale, and field deployments. In the near term, performance data on applications in microgrids will become available from lessons learned from laboratory tests, such as those planned for the Microreactor Applications Research Validation and Evaluation Project (MARVEL), led by Idaho National Laboratory (INL). The SR model incorporates scenario data and known SR design specifications, enabling technoeconomic analysis for SR deployment in microgrids. It specifically considers the distinctive attributes of SRs as generators in technoeconomic studies. SRs can be modeled and analyzed with generation from renewable-energy sources, energy storage, and flexible loads over a range of functionality and applications. This offers a comprehensive tool for feasibility studies, scenario development, and sensitivity analysis for “what-if” consideration of any range of assumptions about SRs in microgrids and other aggregations of distributed-energy resources, including virtual power plants.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗