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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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30 records · Page 2
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Development and Demonstration of a Prototype Molten Salt Sampling System

Molten salt reactors (MSRs) offer potential operability and safety advantages when compared to commercial light water reactors (LWRs). However, operating experience with MSRs is sparse in comparison to what exists for LWRs. Further, the chemical and isotopic composition of the fuel and/or coolant salt is dynamic and difficult to characterize continuously, posing potential safety, operability, and safeguards unknowns that need to be addressed. A molten salt sampling system (MSSS) is regarded as a necessary subsystem within first generation MSRs used to obtain samples of salt for chemical and isotopic analysis in support of the need to monitor and control salt composition during operation. The MSSS is being developed using the Safety-in-Design (SiD) methodology, which incorporates incremental integration of safety analysis into the design process. The MSSS conceptual design emerging from the application of the early stages of the SiD methodology consists of a sample collection system and its housing, a freeze port, and inert gas control and delivery systems. This article describes the prototypes developed to test the functions of these MSSS subsystems, presents the results of testing in both dry and molten salt environments (including reliability data collection performed in accordance with the principles of SiD and the development of a semiquantitative fault tree model), and summarizes the opportunities for future design and testing enhancements based on the results of prototype testing.

molten salt reactor↗
NASA NTRS2004

Automated Classification of ROSAT Sources Using Heterogeneous Multiwavelength Source Catalogs

We describe an on-line system for automated classification of X-ray sources, ClassX, and present preliminary results of classification of the three major catalogs of ROSAT sources, RASS BSC, RASS FSC, and WGACAT, into six class categories: stars, white dwarfs, X-ray binaries, galaxies, AGNs, and clusters of galaxies. ClassX is based on a machine learning technology. It represents a system of classifiers, each classifier consisting of a considerable number of oblique decision trees. These trees are built as the classifier is 'trained' to recognize various classes of objects using a training sample of sources of known object types. Each source is characterized by a preselected set of parameters, or attributes; the same set is then used as the classifier conducts classification of sources of unknown identity. The ClassX pipeline features an automatic search for X-ray source counterparts among heterogeneous data sets in on-line data archives using Virtual Observatory protocols; it retrieves from those archives all the attributes required by the selected classifier and inputs them to the classifier. The user input to ClassX is typically a file with target coordinates, optionally complemented with target IDs. The output contains the class name, attributes, and class probabilities for all classified targets. We discuss ways to characterize and assess the classifier quality and performance and present the respective validation procedures. Based on both internal and external validation, we conclude that the ClassX classifiers yield reasonable and reliable classifications for ROSAT sources and have the potential to broaden class representation significantly for rare object types.

McGlynn, Thomas↗
DOE OSTI2022

Leaf reflectance, leaf transmittance and sky irradiance, at four sites in Panama, 2022

Paired leaf reflectance and transmittance data measured in Panama, at the Parque Natural Metropolitano (PNM), on Barro Colorado Island (BCI), at the San Lorenzo canopy crane site (SLZ), and in Gamboa, Panama from January to April 2022. Measurements were made on leaves from 57 different tree, shrub and liana species, from sunlit canopy and understory locations. Bark reflectance was also measured on the trunks of common tree species at the PNM and SLZ sites. Also included are irradiance measurements and leaf area index (LAI) from eight vertical profiles at PNM. The aim of these data was to refine our understanding of vertical trait and environmental variation in tropical forests, and to improve models which can predict leaf traits from leaf contact spectral measurements. All spectral data and metadata are presented in .csv files and complete instrument output are included in .zip folders. Protocol details are provided as pdf documents. In addition to spectral data described here, some of these samples were also used for measurement of leaf gas exchange, carbon and nitrogen content, and leaf mass per unit leaf area (LMA) these data can be cross linked using the unique sample ID and are provided in separate related data packages.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2009

Developing Large-Scale Bayesian Networks by Composition: Fault Diagnosis of Electrical Power Systems in Aircraft and Spacecraft

In this paper, we investigate the use of Bayesian networks to construct large-scale diagnostic systems. In particular, we consider the development of large-scale Bayesian networks by composition. This compositional approach reflects how (often redundant) subsystems are architected to form systems such as electrical power systems. We develop high-level specifications, Bayesian networks, clique trees, and arithmetic circuits representing 24 different electrical power systems. The largest among these 24 Bayesian networks contains over 1,000 random variables. Another BN represents the real-world electrical power system ADAPT, which is representative of electrical power systems deployed in aerospace vehicles. In addition to demonstrating the scalability of the compositional approach, we briefly report on experimental results from the diagnostic competition DXC, where the ProADAPT team, using techniques discussed here, obtained the highest scores in both Tier 1 (among 9 international competitors) and Tier 2 (among 6 international competitors) of the industrial track. While we consider diagnosis of power systems specifically, we believe this work is relevant to other system health management problems, in particular in dependable systems such as aircraft and spacecraft. (See CASI ID 20100021910 for supplemental data disk.)

Mengshoel, Ole Jakob↗
DOE OSTI2026

Sensor Reduction for Diversion Detection in a Realistic Heat Pipe Microreactor Using Supervised Machine Learning

Microreactors are designed as a smaller, cheaper, and safer alternative to traditional nuclear power plants. Their non-traditional characteristics and prospect of mass production and deployment will likely require new approaches to nuclear safeguards. The primary proliferation concern with microreactors is the diversion of fuel material. Such diversion may produce measurable defects in key physical attributes like neutron flux, which may in turn be detectable using machine learning models. Preliminary work has demonstrated this ability for modeled nominal and diversion scenarios using large quantities of energy integrated neutron flux data. In practice, the number of available sensors for such measurements will be limited and energy integrated flux information will not be available. This work explores the ability of tree-based gradient boosted ensemble models to classify a given microreactor core is nominal or diversion, and determine the number of fuel pins diverted in the case of diversion with reduced numbers of sensors and more realistic detector responses. Classification accuracy of greater than 98% and regression errors as low as 5% of the total number of fuel pins were achieved with as few as 15 sensors, compared to 99% and 4.1% with a maximum of 240 sensors.

22 - GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2023

Foliar isotopic and elemental biochemistry and leaf mass per area (LMA), Parque Natural Metropolitano, Panama, 2022

Foliar N and C isotope composition, bulk elemental N and C composition, and leaf mass per area (LMA) for leaves sampled from the Parque Natural Metropolitano (PNM), Panama, from January to April 2022 are presented. Measurements were made on leaves from 17 different tree, shrub and liana species, from sunlit canopy and understory locations on 8 vertical profiles. Full sample details and leaf area index (LAI) from the vertical profiles at PNM are also included in the data package. The aim of this measurement campaign was two-fold: to improve our understanding of the vertical variation in leaf-level water use efficiency, and to improve models which can predict leaf traits from leaf contact spectral measurements. Biochemistry data and sample metadata are presented in .csv files. The original isotopic data report, which includes details about standards and data accuracy, is provided in .xlsx format. Data and metadata meet the ESS-DIVE reporting format requirements for file level metadata (FLMD) and comma separated values (csv). The protocol details are provided as pdf documents. In addition to foliar biochemistry data reported here these samples were also used for measurement of leaf gas exchange, and leaf optical properties. These data can be linked using the unique sample ID and are provided in separate data packages (NGT0192 and NGT0193).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Leaf gas exchange and fitted parameters, two sites in Panama, 2022

Photosynthetic CO2 response curves (ACi curves), light response curves (AQ curves), dark adapted dark respiration, conductance curves, and survey measurements for leaves measured in the Parque Natural Metropolitano (PNM) and Gamboa, Panama, from January to April 2022 are presented. Measurements were made on leaves from 46 different tree, shrub and liana species, from sunlit canopy and understory locations. Leaf area index from 8 vertical profiles at PNM are also included. The aim of this measurement campaign was three-fold: (i) to improve our understanding of the vertical variation in leaf-level water use efficiency; (ii) to develop an understanding of the sensitivity of stomata to changes in environmental conditions, especially light, humidity, and temperature; (iii) to improve models which can predict leaf traits from leaf contact spectral measurements. All the gas exchange data and metadata are presented in .csv files and complete instrument output are included in .zip folders. Data and metadata meet the ESS-DIVE leaf-level gas exchange reporting format requirements. The protocol details are provided as pdf documents. In addition to gas exchange data reported here these samples were also used for measurement of leaf optical properties, carbon and nitrogen content, and leaf mass per unit leaf area (LMA). These data can be cross linked using the unique sample ID and are provided in separate data packages.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Detection of Diversion in a Realistic Heat Pipe Microreactor Using Supervised Machine Learning

Microreactors (MRs) pose new challenges for international safeguards. Here, their small size and mass reproducibility make them ideal for deployment in greater numbers and in remote locations, making the job of safeguards inspectors more challenging. Machine learning (ML) is currently being applied to many fields to augment human performance and increase automation; in particular, ML could be used to provide insight for international inspectors to help detect the diversion of nuclear fuel from MR cores. Four ML model types (k-nearest neighbors, decision tree, random forest, and histogram-based gradient boosted ensemble) were trained on integrated flux and critical control drum angle data generated with Serpent 2 for a realistic heat pipe MR design, achieving nearly 100% binary classification accuracy of nominal and diversion core configurations by the end of 1 full power year for three of the four model types. Regression model variants were also trained, using the same input data, for predicting the number of fuel pins diverted. Root-mean-square errors below 5% of the total number of fuel pins were achieved by the 1 full power year mark for all models.

22 - GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2025

An Open-source Llm Enhanced-tool Specialized In Helping Moose Related Problems And Tasks

MOOSEenger is an open-source, terminal-first chat application for the MOOSE ecosystem that couples specialized parsing of MOOSE documentation and “.i” input files with retrieval-augmented generation to deliver grounded answers about multiphysics modeling and workflows. It includes dedicated readers for MOOSE-style HTML and a pyhit-based parser that uses the MOOSE syntax tree to preserve block structure and attach retrieval metadata. A data-ingestion pipeline performs semantic chunking into atomic facts and stores them hierarchically in a local Chroma vector database that maintains parent–child relationships across documents; the system can ingest directories, individual files, and single-page web content, and it provides CRUD operations (insert, update, delete) to manage the corpus. At query time, relevant chunks are embedded, retrieved, and fused into the model context, with interactive features such as token streaming, persistent chat history, and dynamic RAG (retrieval triggered by user input or intermediate model output). Deployment is flexible: MOOSEenger runs with local Ollama models or remote Hugging Face/OpenAI backends—typically coordinating generation, lightweight tagging/summarization, and embeddings across three models—and it also supports a server mode and integration with the VS Code Continue interface.

Li, Mengnan [Idaho National Laboratory (INL), Idah↗
DOE OSTI2025

Peatland Plant Community Changes in Annual Production and Composition Through 8 Years of Warming Manipulations Under Ambient and Elevated CO 2 Atmospheres

The Spruce and Peatland Responses Under Changing Environments (SPRUCE) experiment has operated five whole-ecosystem warming manipulations (+0, +2.25, +4.5, +6.75, and +9°C) with paired ambient and elevated CO 2 atmospheres (eCO 2 , +500 ppm) for 8 full calendar years (since August 2015). We tracked shrub-layer vegetation responses to the treatments using annual destructive plot sampling. Tree (Picea and Larix) responses were assessed annually using nondestructive dimensional analyses and allometric conversions. Shrub community changes were assessed for key ericaceous shrubs (Rhododendron, Chamaedaphne, and Kalmia), two Vaccinium species (V. angustifolium, V. oxycoccos), graminoid species (mostly Eriophorum), and one common forb (Maianthemum trifolium), plus minor understory species. We tracked annual aboveground net primary production (ANPP) for vascular plant species in gC m -2 y -1 and overall stand contribution in dry mass. We observed a linear increase in shrub-layer aboveground biomass accumulation with warming over time due primarily to an increase in ericaceous shrub abundance. Cumulative biomass increases across the shrub community showed overall positive responses to eCO 2 after 8 years. Community composition also changed with warming, with increases in woody shrub density, and the reduction or loss of forbs. The tree community showed minimal initial responses to warming early in the treatments, but since 2020, has shown significant increases in ANPP and individual tree growth with warming. The main driver of change in the vascular plant community was temperature, with less pronounced effects of eCO 2 evident. These results indicate an overall increase in ANPP with warming from both the tree and shrub layers of peatland vegetation.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Decayheatml

This code is designed to predict and analyze the decay heat generated in molten salt reactors (MSRs) using a hybrid approach that combines machine learning and segmented polynomial fitting. The accurate prediction of decay heat is essential for reactor safety and the optimization of spent fuel storage. The code operates through several key components: 1) Data Architecture: It incorporates a modular data architecture that handles various MSR-specific operational parameters such as power density, humidity content, and air ingress. These parameters are sampled using Sobol sequences to ensure comprehensive coverage of operational uncertainties. 2) Machine Learning Framework: The code employs a diverse set of machine learning models, including polynomial regression, decision trees, random forests, gradient boosting, support vector regression, k-nearest neighbors, multi-layer perceptrons, and symbolic regression. These models are trained to predict decay heat over a wide temporal range, from immediate shutdown up to 10,000 years. 3) Region-Optimized Training: The temporal domain is divided into multiple regions, each modeled separately to capture distinct decay heat characteristics across different time scales. This approach significantly improves the accuracy and interpretability of predictions. 4) Segmented Polynomial Interpretation (SPI): The SPI method translates machine learning predictions into piecewise polynomial equations. These equations are physically interpretable and can be directly integrated into existing engineering workflows and safety analyses. 5) Front-End Interfaces: The code includes both a Jupyter notebook interface for research development and a Streamlit web application for operational deployment. These interfaces allow users to interactively explore decay heat predictions, adjust operational parameters, and visualize results in real-time. 6) Applications: The framework supports various applications, including safety system validation and spent fuel container optimization. It enables real-time evaluation of worst-case decay heat scenarios, informing the design of passive safety systems and optimizing container designs for long-term storage. Overall, this code provides a robust, accurate, and user-friendly tool for predicting decay heat in MSRs, enhancing reactor safety, and optimizing spent fuel management.

Retamales, Mauricio Eduardo Tano [Idaho National L↗
DOE OSTI2025

Coupling Remote Sensing With a Process Model for the Simulation of Rangeland Carbon Dynamics

Rangelands provide significant environmental benefits through many ecosystem services, which may include soil organic carbon (SOC) sequestration. However, quantifying SOC stocks and monitoring carbon (C) fluxes in rangelands are challenging due to the considerable spatial and temporal variability tied to rangeland C dynamics as well as limited data availability. We developed the Rangeland Carbon Tracking and Management (RCTM) system to track long-term changes in SOC and ecosystem C fluxes by leveraging remote sensing inputs and environmental variable data sets with algorithms representing terrestrial C-cycle processes. Bayesian calibration was conducted using quality-controlled C flux data sets obtained from 61 Ameriflux and NEON flux tower sites from Western and Midwestern US rangelands to parameterize the model according to dominant vegetation classes (perennial and/or annual grass, grass-shrub mixture, and grass-tree mixture). The resulting RCTM system produced higher model accuracy for estimating annual cumulative gross primary productivity (GPP) (R 2 > 0.6, RMSE <390 g C m -2 ) relative to net ecosystem exchange of CO 2 (NEE) (R 2 > 0.4, RMSE <180 g C m -2 ). Model performance in estimating rangeland C fluxes varied by season and vegetation type. The RCTM captured the spatial variability of SOC stocks with R 2 = 0.6 when validated against SOC measurements across 13 NEON sites. Model simulations indicated slightly enhanced SOC stocks for the flux tower sites during the past decade, which is mainly driven by an increase in precipitation. Future efforts to refine the RCTM system will benefit from long-term network-based monitoring of vegetation biomass, C fluxes, and SOC stocks.

54 ENVIRONMENTAL SCIENCES↗