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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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At least 91 records · Page 5
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

47 Tuc in Rubin Data Preview 1. Exploring Early LSST Data and Science Potential

We present analyses of the early data from Rubin Observatory’s Data Preview 1 (DP1) for the field of the globular cluster 47 Tuc. The DP1 data set for 47 Tuc includes four nights of observations from the Rubin Commissioning Camera (LSSTComCam), covering multiple bands (ugriy). We address challenges of crowding in the inner region of the cluster and toward the SMC in DP1, and demonstrate improved star–galaxy separation by fitting fifth-degree polynomials to the stellar loci in color–color diagrams and applying multidimensional sigma clipping. We compile a catalog of 3576 probable 47 Tuc member stars selected via a combination of isochrone, Gaia proper-motion, and color–color space matched filtering. We explore the sources of photometric scatter in the 47 Tuc color–color sequence, evaluating contributions from various potential sources, including differential extinction within the cluster. Finally, of the 72 well-characterized variables in the field, we recover three known variable stars, including two RR Lyrae and one eclipsing binary, in the coadd-based object catalog, and identify 62 in the difference image-based object catalog. Although the DP1 lightcurves have sparse temporal sampling, they appear to follow the patterns of densely sampled literature lightcurves well. Despite some data limitations for crowded-field stellar analysis, DP1 demonstrates the promising scientific potential for future LSST data releases.

Choi, Yumi [NSF National Optical-Infrared Astronom↗
DOE OSTI2021

HEPA Filter Age Evaluation Report

In 2020, PNNL completed a literature search for high-efficiency particulate air (HEPA) filter age information. HEPA filters are used in many operations to remove particulate matter from effluent exhaust streams. They are thought to degrade over time both during proper storage and during normal operational service; however, the rate at which the filters degrade remains unknown. This brings into question if age is an adequate indicator of HEPA filter performance. Data from six previous reports were obtained from the literature search and combined to create a data set of 1600 operating filters. Filter usage was identified from multiple facilities. The various types of filters (e.g., axial flow, self-contained, and standard 24 x 24 x 11.5 inches; and both separator and separatorless) reported were constructed to the requirements Section FC (HEPA Filters) or Section FK (Special HEPA Filters) of the American Society of Mechanical Engineers AG-1 code. Filters were presumed to have continuously met the operational criteria and in particular passed both annual efficiency tests and annual DP measurements. The environmental conditions within the exhaust system were also assumed adequate for long-term filter operation. The data, by the nature of the reports, excludes rejected filters from quality assurance evaluations, intake, or installation testing. The collective data set shows over half of were operating past the current 10-year Department of Energy (DOE) limit. Data was evaluated for age lifetime using a linear trendline, survival function, probability functions, and failure rate; financial impacts were also addressed. This report supports the notion that HEPA filters can operate safely and efficiently under proper maintenance well past the 10-year lifetime established by DOE. Using the results of the four age evaluation approaches, they collectively point to the reasonableness of an operating HEPA filter lifetime of 20 years. The results are not necessarily definitive, but nevertheless, they are promising. The analysis provides reasonable assurance that when implemented using a graded approach with well-defined performance and operational requirements, extending the service life for HEPA filters beyond 10 years is low risk.

42 ENGINEERING↗
DOE OSTI2023

Extrapolation of the Rainflow-Counted Load Ranges for Fatigue Assessment of the Wind Turbine's Blades

Wind turbine design standards recommend the use of statistical modeling coupled with extrapolation of the short-term load data to long-term periods for fatigue reliability assessment. However, statistical error and computational expense can limit the accuracy of such approaches. In the case of wind turbine blades, the errors are more significant because of the high material fatigue exponent that makes the damage estimations more sensitive to variations. In addition, due to different excitation sources, the flapwise load range histogram is not unimodal, and thus its statistical modeling is complex. In the present work, we provide three methods for statistical modeling of the flapwise bending moment ranges including a novel approach based on frequency-based separation of the modes. The first two methods are simplified approaches for modeling the most crucial load ranges using unimodal distributions and the third method involves multimodal distribution fitting. The research is based on 3600 10-minute aeroelastic simulations of DTU 10MW case study wind turbine from which a benchmark damage equivalent load (DEL) is calculated. The DEL calculated by each of the three proposed methods is compared to this reference. The results show that the conventional approach based on using 6 seeds as well as using mixture models fitted on the limited data lead to under-conservative results with errors up to 23%. On the other hand, the simplified unimodal approaches provided in this work can provide conservative estimations of the fatigue damage with mean values 5% and 12% higher than the benchmark. However, the variability of the DEL estimates is higher when using unimodal extrapolation of the load ranges, and the data can be conservative by 17.5%. The proposed unimodal fits suggested for modeling and extrapolation of the blade's load ranges provide less errors relatively and most importantly conservative DEL estimations while maintaining computational efficiency.

blade fatigue↗
DOE OSTI2025

Direct Feed High-Level Waste APPS Model Glass Testing (DFHLW APPS) Matrix, Phase 2

This report summarizes the data collected during the batching and melting of a second matrix of Direct Feed High-Level Waste (DFHLW) glasses generated using the preliminary enhanced waste glass models (EWG2.5) and the Britton and Anderson (2024) preliminary DFHLW feed vector. The purpose of these glasses is two-fold: 1. Validate EWG2.5 glass calculations being used in the Aspen Process Performance Simulation (APPS) model. 2. Evaluate and ultimately improve the glass property models and formulation methods used for design of DFHLW glasses as part of an iterative process of data collection and model refinement. Some of the 16 APPS2 glasses tested did not satisfy all target property constraints due to the limited data on DFHLW glass supporting the EWG2.5 models. • One glass, APPS2-10, formed nepheline on canister centerline cooling (CCC) heat-treatment and failed the product consistency test (PCT) response limits. This glass also had high B and Cr release rates for the toxicity characteristic leaching procedure (TCLP). All other glasses were found to satisfy the PCT and TCLP constraints for both quenched and CCC samples. • One glass, APPS2-08, had higher than acceptable viscosity due to magnetite crystallization. • One glass, APPS2-09, formed greater than 2 vol% crystals at 950 °C. As the glass design criterion was that the temperature at 2 vol% crystal (T 2% ) be less than 950 °C, only one glass failed the criteria. However, this criterion is being reevaluated. Four additional glasses formed crystal fractions between 1 and 2 vol% at 950 °C (APPS2-03, -08, -12, and -14). • Four glasses – APPS2-01, -02, -04, and -16 – failed the Monofrax K-3 refractory neck corrosion (k neck ) design limit of 0.04 in. at 1208 °C for 6 d. This is another criterion being reevaluated. Four additional glasses (APPS2-05, -06, -11, and -13) exhibited 0.025 = k neck = 0.04 in. • All 16 glasses passed the sulfur solubility and TCLP constraints. The measured property values were compared to predicted values using EWG2.5 and a selection of other existing models. A few models (e.g., electrical conductivity, TCLP) were found to be adequate for designing DFHLW glasses in the near future, while others require refits or offsets. It is recommended that new property models be developed for EWG3.0, as a large amount of DFHLW glass property data (> 14 × existing data) is expected to be collected in the compositional spaces where no data was previously available. To enable near-term calculations and formulations for designing DFHLW glasses and processing rate estimations, a formulation algorithm with minor modifications will be developed, EWG2.6.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2024

A versatile high-speed x-ray microscope for sub-10 nm imaging

We have developed a next-generation scanning x-ray microscope RASMI (RApid Scanning Microscopy Instrument) for high-throughput tomographic imaging. RASMI is installed at the hard x-ray nanoprobe beamline at NSLS-II and is capable of manipulating 1D multilayer Laue lenses (MLLs) and 2D optics (both zone plates and monolithically assembled 2D MLLs). The sample scanning stage utilizes line-focusing interferometry as an encoder while performing fly-scanning data acquisition. The system can be configured for both position- and time-triggering modes during fly-scanning. The microscope demonstrated a detector-limited data acquisition rate of 1.25 kHz during ptychography measurements. The initial x-ray results yielded a sample-limited resolution of ∼6 nm in 2D. RASMI can be adopted for in-vacuum applications and is a foundation for the next-generation scanning microscopy systems to be developed and commissioned at NSLS-II.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Conceptual Designs for Irradiation Creep Testing of SiC in HFIR

Understanding irradiation creep of nuclear fuel cladding is important to properly size the initial fuel-cladding gap and understand when pellet-cladding contact is expected to occur due to a combination of fuel swelling and cladding creep-down. Irradiation creep also plays a role in relaxing stresses that develop in-pile. Silicon carbide fiber–reinforced silicon carbide matrix (SiC/SiC) composites are the leading long-term accident-tolerant fuel cladding concept for light-water reactors (LWRs). Although some limited data are available regarding irradiation creep of the individual constituents (fibers, matrix), data regarding irradiation creep of SiC/SiC composites are currently insufficient. Additional data regarding irradiation creep compliance and the rupture lifetime (combination of creep and slow crack growth) are needed to understand material limitations. This work describes the design and development of two irradiation vehicles that are being pursued for testing SiC/SiC concepts in the High Flux Isotope Reactor (HFIR). The first is a passive experiment, referred to as the PRECISE experiment, that leverages the constant coolant pressure of HFIR to compress a metallic bellows and provide a well-characterized load to drive creep in a SiC/SiC dog bone specimen. The total creep strain would be quantified post-irradiation by measuring dimensional changes of the specimen length as well as local dimensional changes within the gauge region. Non-stressed specimens would also be irradiated under the same conditions to provide an indication of dimensional changes due to radiation-induced swelling in the absence of creep. A second, more complex experiment, referred to as the INSITE experiment, is being designed in parallel that would use pneumatics to pressurize a metal bellows and linear variable differential transformers (LVDTs) to measure the specimen displacement in situ during irradiation. Such an experiment would provide significantly more data regarding the evolution of the creep compliance as a function of dose and applied stress within a single experiment but would require significantly more development time and cost to execute. The primary concern with the INSITE experiment is the accuracy, reliability, and expected lifetime of the LVDTs during irradiation at elevated temperatures. Efforts are being made to adjust the experiment design and operating procedure to limit LVDT temperatures and mitigate or otherwise compensate for uncertainties due to factors such as temperature fluctuations, creep in the surrounding structural materials, and drift of the LVDTs. This work describes the experiment designs, thermal and structural analysis that were performed to ensure that the desired temperature and stress conditions can be achieved, some initial sensitivity analyses to predict the evolution of the radiation-induced specimen displacements, and potential sources of uncertainty in the measurements. Out-of-pile testing is being performed in parallel to confirm that the test trains achieve the expected stress states in the specimens and do not result in prohibitive stress concentrators (e.g., in the grip regions) that might risk pre-mature failure. The PRECISE experiments are proceeding toward fabrication and assembly with HFIR insertion planned during fiscal year 2026. The INSITE experiment is progressing toward out-of-pile demonstrations, which will provide more conclusive evidence regarding the feasibility of executing these tests in HFIR or whether alternative displacement monitoring techniques may need to be considered.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2025

Dynamic Boundary Microgrids Under Privatization Considerations

Microgrids have physical, electrical, and logical (data, network, and ownership) boundaries. To power unserved customer loads during an outage, microgrids can extend the traditional operational boundaries. This can become complex when considering microgrid-to-microgrid (M2M) interactions where sensitive information such as competitive microgrid operational data is not shared. This work proposes an optimization method coordinated between microgrid controllers and distribution management systems that limits data sharing. The method involves a competitive bidding strategy that maximizes unserved load coverage while minimizing resource utilization and sensitive operational data sharing among entities. The work is validated on a two-microgrid system with photovoltaic and energy storage systems and curves of load derived from real world residential buildings datasets. Results show that the proposed method, when applied for three distinct use cases of energy storage sufficiency to cover the predefined boundary and/or the expanded boundary, can successfully select and bid the available load coverage.

Starke, Michael [ORNL] (ORCID:0000000221211195)↗
DOE OSTI2025

Baseline Cost Model for Hydropower: Documentation (2025)

Hydropower currently contributes about 80 GW of conventional and 23 GW of pumped storage capacity to the United States (US) power grid. Previous studies have estimated a considerable amount of remaining US hydropower resources, including non-powered dams (NPD) (Hadjerioua et al., 2012), new stream-reach developments (NSD) (Kao et al., 2014), pumped storage hydropower (PSH) and canal/conduit (Kao et al., 2022). The combined theoretical capacity potential of these various hydropower resources is comparable to the existing US hydropower capacity. There is a continuing interest in developing this hydropower potential, particularly to help meet the increasing demand for electricity. However, available data (Sasthav and Oladosu, 2022) show that the rate of new hydropower development has slowed considerably over time despite the interest of industry stakeholders. This is partly due to the competition from other energy resources and from the highly dispersed nature of remaining hydropower resources, which lead to high information requirements for evaluating the feasibility of potential projects. Cost information provides the most succinct summary of the feasibility of a potential hydropower project required by stakeholders, including developers, investors, policymakers, consumer groups, etc., considering investment options. The best estimates of hydropower costs can be obtained through detailed engineering design and cost assessments of individual projects. However, this approach has high data and resource (time, funds, cross-disciplinary expertise) requirements that render it inapplicable for rapid cost estimation with limited data. Although innovative approaches can overcome some of these impediments (see Oladosu and Ma, 2024 for such an application to potential NPD projects), the development of such approaches still requires significant amounts of resources and are not generally applicable to all hydropower project types. Therefore, statistical and parametric methods using simpler cost specifications remain of significant utility to hydropower stakeholders and are, at the least, complementary to more detailed approaches, particularly when evaluating many potential projects.

13 HYDRO ENERGY↗
DOE OSTI2025

Integrated Framework of Multisource Data Fusion for Outage Location in Looped Distribution Systems

Accurate outage location is essential for expediting post-outage power restoration, minimizing outage duration, and enhancing the resilience of distribution networks. With the advent of advanced metering infrastructure, data-driven outage location methods have significantly advanced beyond traditional approaches that rely on manual inspections. However, existing methods still face critical challenges, like reliance on single-source data, limited ability to handle partially observable systems or difficulties with loop networks. To the best of our knowledge, no single approach has comprehensively addressed all of these challenges at once. To this end, this paper proposes a comprehensive multisource data fusion framework for outage locations via probabilistic graph networks. The framework consists of three key phases. First, a novel method for reconstituting distribution networks with loops is developed, transforming looped networks into multiple radial subnetworks that retain all outage causalities of the original network. Second, Bayesian network (BN) models are established for each subnetwork, integrating multiple data sources and network structures. Finally, a joint Gibbs sampling mechanism, featuring forward and backward information flow, is designed to merge data from separate BN models and maximize the utilization of limited evidence, ensuring accurate outage location identification. In conclusion, the framework was validated on two modified public test systems, and comparative studies confirmed its effectiveness.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2023

The Design and Implementation of a Secure Datastore Based on Ethereum Smart Contract

In this paper, we present a secure datastore based on an Ethereum smart contract. Our research is guided by three research questions. First, we will explore to what extend a smart-contract-based datastore should resemble a traditional database system. Second, we will investigate how to store the data in a smart-contract-based datastore for maximum flexibility while minimizing the gas consumption. Third, we seek answers regarding whether or not a smart-contract-based datastore should incorporate complex processing such as data encryption and data analytic algorithms. The proposed smart-contract-based datastore aims to strike a good balance between several constraints: (1) smart contracts are publicly visible, which may create a confidentiality concern for the data stored in the datastore; (2) unlike traditional database systems, the Ethereum smart contract programming language (i.e., Solidity) offers very limited data structures for data management; (3) all operations that mutate the blockchain state would incur financial costs and the developers for smart contracts must make sure sufficient gas is provisioned for every smart contract call, and ideally, the gas consumption should be minimized. Our investigation shows that although it is essential for a smart-contract-based datastore to offer some basic data query functionality, it is impractical to offer query flexibility that resembles that of a traditional database system. Furthermore, we propose that data should be structured as tag-value pairs, where the tag serves as a non-unique key that describes the nature of the value. We also conclude that complex processing should not be allowed in the smart contract due to the financial burden and security concerns. The tag-based secure datastore designed this way also defines its applicative perimeter, i.e., only applications that align with our strategy would find the proposed datastore a good fit. Those that would rather incur higher financial cost for more data query flexibility and/or less user burden on data pre- and post-processing would find the proposed database too restrictive.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2025

Understanding Aitken Mode Aerosol Variability over the Southern Ocean and Antarctica: Insights from Cloud Condensation Nuclei Data

Aitken mode aerosol particles play an important role influencing cloud properties and sustenance, acting as a reservoir of potential cloud condensation nuclei against precipitation scavenging. However, there is limited data on Aitken mode aerosols. In this study, we develop a method to estimate Aitken mode aerosol concentrations and size distribution using cloud condensation nuclei measurements (CCN) and κ-Köhler theory. The performance of this method is evaluated using scanning mobility particle sizer (SMPS) data from recent field campaigns to demonstrate its skills and applicability. The method reasonably estimates Aitken- and accumulation-mode aerosol concentrations, achieving correlations of 0.7–0.9 with only modest biases (mean fractional bias within ±23% for Aitken mode and ±34% for accumulation-mode). This method is further applied to measurements collected over the Southern Ocean and Antarctica in recent years from multiple platforms, including ground sites, aircraft, and ships, to derive Aitken and accumulation-mode aerosol concentrations. Using the derived data, we examine the seasonal cycle, latitudinal variations, and vertical distribution of aerosols. Aitken mode aerosol concentrations are elevated over the Southern Ocean and Antarctica during the austral summer similar to the accumulation mode. In the austral summer, the free troposphere has more Aitken mode aerosols and fewer accumulation mode aerosols than the boundary layer, and thus likely serves as an important source of cloud-forming aerosol while also diluting the accumulation mode.

Kang, Litai [University of Washington] (ORCID:0000↗
DOE OSTI2022

Modeling Electric Vehicle Charging Station Siting Suitability with a Focus on Equity

As adoption of electric vehicles increases, the infrastructure to charge them must keep pace. Determining where to add new charging infrastructure is a complex process subject to many factors, including electrical service availability, vehicle dwell time, the type(s) of drivers and vehicles the stations will serve, traffic levels and timing, and land ownership. In addition, advancing social equity is a current priority of federal efforts to invest in electric vehicle charging infrastructure. Conducting Multi-criteria Decision Analysis (MCDA) within Argonne’s Energy Zones Mapping Tool (EZMT) is a useful method for analyzing many of the factors that influence how suitable a location is for potentially adding new charging infrastructure, and we show how equity metrics can be included in the analysis. However, data limitations impose challenges to using MCDA to evaluate and prioritize locations. We use three examples to demonstrate how to use publicly available data and MDCA to analyze different siting objectives. Each example starts with defining a specific objective and ends with how to use the results to identify specific potential locations that could be investigated further. This analysis demonstrates how interested stakeholders can use the EZMT to run the example MCDA models defined in this study, modify them to suit their needs, or create new MCDA models.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2021

Making a Water Data System Responsive to Information Needs of Decision Makers

Evidence-based environmental management requires data that are sufficient, accessible, useful and used. A mismatch between data, data systems, and data needs for decision making can result in inefficient and inequitable capital investments, resource allocations, environmental protection, hazard mitigation, and quality of life. In this paper, we examine the relationship between data and decision making in environmental management, with a focus on water management. We focus on the concept of decision-driven data systems —data systems that incorporate an assessment of decision-makers' data needs into their design. The aim of the research was to examine the process of translating data into effective decision making by engaging stakeholders in the development of a water data system. Using California's legislative mandate for state agencies to integrate existing water and other environmental data as a case study, we developed and applied a participatory approach to inform data-system design and identify unmet data needs. Using workshops and focused stakeholder meetings, we developed 20 diverse use cases to assess data sources, availability, characteristics, gaps, and other attributes of data used for representative decisions. Federal and state agencies made up about 90% of the data sources, and could readily adapt to a federated data system, our recommended model for the state. The remaining 10% of more-specialized data, central to important decisions across multiple use cases, would require additional investment or incentives to achieve data consistency, interoperability, and compatibility with a federated system. Based on this assessment, we propose a typology of different types of data limitations and gaps described by stakeholders. We also propose technical, governance, and stakeholder engagement evaluation criteria to guide planning and building environmental data systems. Data-system governance involving both producers and users of data was seen as essential to achieving workable standards, stable funding, convenient data availability, resilience to institutional change, and long-term buy-in by stakeholders. Our work provides a replicable lesson for using decision-maker and stakeholder engagement to shape the design of an environmental data system, and inform a technical design that addresses both user and producer needs.

Cantor, Alida↗
DOE OSTI2023

Efficient Reinforcement Learning for Real-Time Hardware-Based Energy System Experiments: Preprint

In the context of urgent climate challenges and the pressing need for rapid technology development, Reinforcement Learning (RL) stands as a compelling data-driven method for controlling real-world physical systems. However, RL implementation often entails time-consuming and computationally intensive data collection and training processes, rendering them inefficient for real-time applications that lack non-real-time models. To address these limitations, real-time emulation techniques have emerged as valuable tools for the lab-scale rapid prototyping of intricate energy systems. While emulated systems offer a bridge between simulation and reality, they too face constraints, hindering comprehensive characterization, testing, and development. In this research, we construct a surrogate model using limited data from simulated systems, enabling an efficient and effective training process for a Double Deep Q-Network (DDQN) agent for future deployment. Our approach is illustrated through a hydropower application, demonstrating the practical impact of our approach on climate-related technology development.

deep Q-learning↗
DOE OSTI2025

Reevaluation of Radiation-Protection Standards for Workers and the Public Based on Current Scientific Evidence

President Trump’s recent executive orders to “Usher in a Nuclear Renaissance,” coupled with the global pledge to triple nuclear energy capacity by 2050, underscore nuclear energy’s importance to national security and economic prosperity. This renewed interest has prompted efforts to spur nuclear-energy deployment, including assessing factors impeding it. One issue that has previously been identified as adding to the cost of nuclear energy is excessively conservative requirements, including those related to radiation protection. This technical review, therefore, examines current radiation-protection standards that were established decades ago when more limited data were available and nuclear-energy expansion was not a national priority. The review focuses on scientific evidence regarding the health effects of ionizing radiation at annual doses of 10,000 mrem or less. The review evaluates epidemiological studies, radiobiological research, and positions of relevant professional organizations to assess whether such doses result in discernable or observable increases in negative health outcomes. The review also surveys the literature related to economic and practical implications of current radiation-protection standards and practices. Based on this assessment, we propose maintaining an annual occupational whole-body dose limit of 5,000 mrem/yr and eliminating all “as low as reasonably achievable” requirements and limits below this threshold. This change could potentially reduce radiation-protection costs by millions of dollars annually for each reactor, as well as decrease the overall costs and correct misconceptions about the risks associated with all nuclear technologies. The evidence further supports future consideration of a 10,000 mrem/yr limit that would maintain appropriate safety margins while further reducing protection costs. Similarly, given the data and that the average annual radiation dose per person in the U.S. is 620 mrem, we believe the public dose limits of 100 mrem/yr are unnecessarily restrictive; increasing to 500 mrem/yr would maintain substantial safety margins—a factor of 10 below the occupational limit—while reducing regulatory burdens and associated bureaucracy. While we acknowledge ongoing scientific debate and encourage continued research on the health effects of ionizing radiation, our review indicates current frameworks are overly conservative. These overly stringent limits not only impose unnecessary economic burdens without corresponding health benefits but also divert safety focus and resources from more important considerations. Although this study was motivated by nuclear-power considerations, reforms to radiation-protection requirements have significant positive implications for other areas, such as nuclear medical applications, environmental remediation, nuclear-waste management and disposal, and industrial applications of nuclear technologies.

61 RADIATION PROTECTION AND DOSIMETRY↗
DOE OSTI2023

Correlated Trajectory Uncertainty for Adaptive Sequential Decision Making

One of the great challenges with decision making tasks on real world systems is the fact that data is sparse and acquiring additional data is expensive. In these cases, it is often crucial to make a model of the environment to assist in making decisions. At the same time, limited data means that learned models are erroneous, making it just as important to equip the model with good predictive uncertainties. In the context of learning sequential decision making policies, these uncertainties can prove useful for informing which data to collect for the greatest improvement in policy performance \citep{mehta2021experimental, mehta2022exploration} or informing the policy about unsure regions of state and action space to avoid during test time \citep{yu2020mopo}. Additionally, assuming that realistic samples of the environment can be drawn, an adaptable policy can be trained that attempts to make optimal decisions for any given possible instance of the environment \citep{ghosh2022offline, chen2021offline}. In this work, we examine the so-called ``probabilistic neural network'' (PNN) model that is ubiquitous in model-based reinforcement learning (MBRL) works. We argue that while PNN models may have good marginal uncertainties, they form a distribution of non-smooth transition functions. Not only are these samples unrealistic and may hamper adaptability, but we also assert that this leads to poor uncertainty estimates when predicting multiple step trajectory estimates. To address this issue, we propose a simple sampling method that can be implemented on top of pre-existing models.We evaluate our sampling technique on a number of environments, including a realistic nuclear fusion task, and find that, not only do smooth transition function samples produce more calibrated uncertainties, but they also lead to better downstream performance for an adaptive policy.

Offline Reinforcement Learning↗
DOE OSTI2025

AGR-5/6/7 Experiment Preliminary Findings

AGR-5/6/7 in-pile data have been published: Fuel irradiation conditions, Fission gas release-rate-to-birth-rate (R/B) ratios, PIE and safety testing are still in progress and only limited data have been published, and preliminary results.

11 - NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2025

Micrometer: Micromechanics transformer for predicting full field mechanical responses of heterogeneous materials

Predicting mechanical responses of heterogeneous materials across scales remains a significant challenge. Traditional computational methods often struggle with complex and multiscale nature of these materials, limiting their effectiveness in real-world applications. Here, in this paper, we introduce Micrometer, a vision transformer based deep learning model designed to predict full field mechanical responses of heterogeneous materials, bridging the gap between computer vision and solid mechanics problems. We show that Micrometer, trained on a large-scale high-resolution dataset of 2D fiber-reinforced composites, can achieve state-of-the-art performance in predicting microscale strain fields across a wide range of material properties and loading conditions. Our model demonstrates accuracy and computational efficiency in applications such as computational homogenization and multiscale modeling, reducing computational time by up to two orders of magnitude compared to conventional numerical solvers while maintaining less than 1 % errors in predicting macroscale stress fields. Furthermore, we showcase Micrometer’s adaptability through transfer learning experiments on new materials with limited data, highlighting its potential to tackle diverse scenarios in computational solid mechanics. These results represent a significant step towards AI-driven innovation in materials science, addressing the limitations of traditional numerical methods and paving the way for more efficient simulations of heterogeneous materials across various industrial applications.

Composite materials↗