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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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NASA NTRS1991

An international organization for remote sensing

A recommendation is presented for the formation of a new commercially oriented international organization to acquire or develop, coordinate or manage, the space and ground segments for a global operational satellite system to furnish the basic data for remote sensing and meteorological, land, and sea resource applications. The growing numbers of remote sensing programs are examined and possible ways of reducing redundant efforts and improving the coordination and distribution of these global efforts are discussed. This proposed remote sensing organization could play an important role in international cooperation and the distribution of scientific, commercial, and public good data.

Helm, Neil R.↗

NASA NTRSDate not supplied

Murine Host-gut Microbiota Interactions are Modulated During Spaceflight

The rodent habitat on the International Space Station has provided critical insight into the impact of spaceflight on mammalian physiology. These effects include dysfunction of carbohydrate, steroid and lipid metabolism, and immune response, as well as induction of symptoms characteristic of liver disease, insulin resistance, osteopenia and myopathy, which are anticipated to intensify over long-duration spaceflight. Although these physiological responses can involve the microbiome, the host-microorganism interactions during spaceflight are still largely unknown. NASA GeneLab curates a wide range of space research data and the current work harnesses GeneLab multi’omic data from recent Rodent Research studies to explore changes to gut microbiota during spaceflight and their associations with host physiology when compared to ground controls. Using a hybrid analysis of DNA barcoding and whole genome shotgun data, an array of bacteria, fungi and nematodes could be identified at species level, and significant differences in relative abundances associated with spaceflight. Functional prediction based on differential abundance of species and metagenome gene inventories as well as metatranscriptomic gene expression at the host-gut microbiome interface implicate microbiota interactions could contribute to spaceflight pathology. Harnessing carefully curated publicly available data, such as from Genelab, to generate multi‘omic space science discoveries can help decipher the complex host-microbiome interactions that influence both health on Earth and the feasibility of long-duration spaceflight.

Microbiome↗

DOE OSTI2026

A Data-Driven Method for Synthetic Extreme Weather Generation and Solar Impact Assessment: Preprint

High-resolution, high-fidelity weather datasets are essential for testing and evaluating the resilience of power systems, particularly under extreme weather conditions. However, existing extreme weather datasets are typically derived from historical events that are localized and may lack the spatial and temporal resolution or scenario diversity needed to test largescale power systems. In this work, we propose a synthetic extreme weather simulation approach capable of generating targeted extreme events, such as hurricanes, using publicly available data sources. Preliminary results demonstrate the impact of a simulated Category 1 hurricane on renewable generation and critical infrastructure in California. The work aims to provide a flexible approach for creating multiple types of extreme weather scenarios across different regions, enabling comprehensive system stress testing, training, and resilience assessment.

24 POWER TRANSMISSION AND DISTRIBUTION↗

NASA NTRS2024

Analyzing EOSDIS Dataset Research Outputs using Knowledge Graphs and Large Language Models

Datasets, unlike publications, can be updated over time, with each new version receiving a DOI but not always being linked to previous ones. This complicates tracking citations across a dataset’s lifecycle. We address this by integrating dataset versions and citations into a knowledge graph (KG), which helps trace dataset citations and analyze dataset usage in applied research. To categorize publications from various journals, we fine-tuned NASA IMPACT INDUS Large Language Model (LLM) on a labeled publication set, assigning publications to one of twenty applied research areas. By linking datasets to these research areas, we improved dataset searchability and discovery through these domains.

open-source↗

NASA NTRS2019

Using Machine Learning to Predict Core Sizes of High-Efficiency Turbofan Engines

With the rise in big data and analytics, machine learning is transforming many industries. It is being increasingly employed to solve a wide range of complex problems, producing autonomous systems that support human decision-making. For the aircraft engine industry, machine learning of historical and existing engine data could provide insights that help drive for better engine design. This work explored the application of machine learning to engine preliminary design. Engine core-size prediction was chosen for the first study because of its relative simplicity in terms of number of input variables required (only three). Specifically, machine-learning predictive tools were developed for turbofan engine core-size prediction, using publicly available data of two hundred manufactured engines and engines that were studied previously in NASA aeronautics projects. The prediction results of these models show that, by bringing together big data, robust machine-learning algorithms and automation, a machine learning-based predictive model can be an effective tool for turbofan engine core-size prediction. The promising results of this first study paves the way for further exploration of the use of machine learning for aircraft engine preliminary design.

Tong, Michael T.↗

NASA NTRS2019

Using Machine Learning to Predict Core Sizes of High-Efficiency Turbofan Engines

With the rise in big data and analytics, machine learning is transforming many industries. It is being increasingly employed to solve a wide range of complex problems, producing autonomous systems that support human decision-making. For the aircraft engine industry, machine learning of historical and existing engine data could provide insights that help drive for better engine design. This work explored the application of machine learning to engine preliminary design. Engine core-size prediction was chosen for the first study because of its relative simplicity in terms of number of input variables required (only three). Specifically, machine-learning predictive tools were developed for turbofan engine core-size prediction, using publicly available data of two hundred manufactured engines and engines that were studied previously in NASA aeronautics projects. The prediction results of these models show that, by bringing together big data, robust machine-learning algorithms and data science, a machine learning-based predictive model can be an effective tool for turbofan engine core-size prediction. The promising results of this first study paves the way for further exploration of the use of machine learning for aircraft engine preliminary design.

Core Size↗

NASA NTRSDate not supplied

The Nasa SRA Process as It Relates to Open-Source Workflows Developed for GeneLab Data Processing

To release open, standards-compliant processed data sets in the Open Science Data Repository (OSDR), the GeneLab Data Processing team works with the scientific community through the OSDR Analysis Working Groups to design and build open-source data processing pipelines. Once baselined internally, these pipelines are wrapped into workflows and published on the NASA GeneLab Data Processing public GitHub repository along with detailed instructions for installation and use. Each workflow must be approved through NASA's Software Release Authorization (SRA) process prior to publishing. However, the SRA process lacks sufficient documentation and clarity regarding which forms are applicable for new open-source software that utilizes publicly available 3rd party tools, and the SRA process can take several months to complete, making sharing software outside of NASA cumbersome and in contradiction with the concept of Open Science. Furthermore, the SRA process was designed as a one-size fits all approach and thus many of the questions asked are not applicable to our open-source workflows. Here we describe the software provided on the NASA GeneLab Data Processing GitHub repository, summarize our experiences with the SRA process to release these software, and propose a more stream-lined approach for review of open-source projects.

Software Release Authorization↗

NASA NTRSDate not supplied

The NASA SRA Process as it Relates to Open-Source Workflows Developed for GeneLab Data Processing

Software Release Authorization↗

DOE OSTI2025

Acoustic Rocket Signatures Collected by Smartphones

Rockets generate complex acoustic signatures that can be detected over a thousand kilometers from their source. While many far-field acoustic rocket signatures have been collected and released to the public, very few signatures collected at distances less than 100 km are available. This work presents a curated and annotated dataset of acoustic signatures of 243 rocket launches collected by a network of smartphones stationed at distances between 10 and 70 km from the launch sites, resulting in 1089 individual recordings. Due to the frequency dependence of atmospheric attenuation and the relatively short propagation distances, higher-frequency features not preserved in most publicly available data are observed. The signals are time-aligned to allow for different segments of the signal (ignition, launch, trajectory, chronology) to be more easily examined and compared. Initial analysis of the features of these rocket launch stages is performed, observed features are compared to those found in the existing literature, and comparisons between signals from launches of different rocket types are made. The dataset is annotated and made available to the public to aid future analysis of the characteristics and source mechanisms of rocket acoustics as well as applications such as rocket detection and classification models.

33 ADVANCED PROPULSION SYSTEMS↗

NASA NTRSDate not supplied

Ongoing Work: A Prototype Dataset for Low-flying Autonomous Medical UAS Operations

This paper presents ongoing work to create a dataset for low-flying autonomous medical UAS operations, focused on human stance recognition. This is an exploration of the viability of airborne classification for the Drone as a First Responder (DFR) concept in which a UAS arrives at the scene of an incident before emergency response personnel can get there and provides some level of situational awareness for the personnel arriving to the scene. Future incarnations could also see the UAS administer some level of care to injured parties at the scene. The data set, focused on detecting human stance, being developed here is the result of 30 test flights at NASA Langley Research Center in early 2024. In addition to flights where the participant (an anthropomorphic testing device or human) is alone in the viewing area holding a particular stance, two emergency scenes have been fabricated and collected through video - ``bike crash'' and ``difficult camping''. These test flights include four human participants. The contribution of this work upon completion will be a publicly available data set for the development of classification engines focused on human stance, and in the future, even triage.

Uncrewed Aerial Systems↗

DOE OSTI2024

Physical, socio-psychological, and behavioural determinants of household energy consumption in the UK

Determining which attitudes and behaviours predict household energy consumption can help accelerate the low-carbon energy transition. Conventional approaches in this domain are limited, often relying on survey methods that produce data on individuals’ motivations and self-reported activities without pairing these with actual energy consumption records, which are particularly hard to collect for large, nationally representative samples. This challenge precludes the development of empirical evidence on which attitudes and behaviours influence patterns of energy consumption, thus limiting the extent to which these can inform energy interventions or conservation programs. This study demonstrates a novel methodology for estimating energy consumption in the absence of actual energy records by using a large, publicly available data set of energy consumption in the UK. We develop a predictive model using the Smart Energy Research Laboratory (SERL) data portal (with records from nearly 13,000 UK households) and then use this model to predict energy consumption (both electric and gas) for a sample of 1,000 UK householders for which we separately collect over 200 variables relating to climate change attitudes and practices. Our approach uses a set of over 50 independent variables that are shared between the data sets, allowing us to train a model on the SERL data and use it to analyse the relationship between energy consumption and the opinions, motivations, and daily practices of survey respondents. Results show that electricity consumption is influenced by a broader range of factors compared to gas. Household energy use is best explained by physical dwelling characteristics, socio-demographic variables, and certain behavioural and attitudinal measures. Notably, pro-environmental attitudes, frugality, and conscientiousness correlate with lower energy use, while income and consumerism are linked to higher consumption. We discuss how these findings can inform efforts to decarbonise home energy use in the UK.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

NASA NTRSDate not supplied

The NASA Open Science Data Repository: Biomedical Data, Analysis Tools, and Informatic Collaborations

Increased biomedical risks and challenges associated with deep space missions require knowledge discovery, health countermeasures, and biomedical support capabilities. Maximally open-access and reusable data is needed by developers, scientists, and engineers to develop these systems. The NASA Open Science Data Repository (OSDR) is a maximally open access and FAIR database (ie., findable, accessible, interoperable, and reusable), and meets various scientific, technical, and operational needs. It offers users and submitters the ability to upload, download, search, share, analyze, cite, and visualize data across ‘omics, physiological, phenotypic, payload, hardware, behavioral, bioimaging, video, and environmental monitoring telemetry datasets. OSDR is an expanded database, based upon the successes of NASA GeneLab. OSDR has >460 studies with datasets covering model organisms to non-NASA human astronauts. There are ~12 datasets from the Inspiration 4 (I4) mission, spanning metagenomics, comprehensive metabolic panels, clonal hematopoiesis, spatial transcriptomics, proteomics, and cytokine panels. In the interest of data privacy, two I4 datasets with raw files relating to the epitranscriptome, and a new request feature is live in OSDR (with a backend review process established) developed from industry norms. OSDR is collecting and curating biomedical human data from a new sub-orbital research flight and is open to more space life science/biomedical submissions from the international and commercial sectors. OSDR also recently began a collaboration with the European Space Agency (ESA) to collect and curate >200 terabytes of human and model organism data. The OSDR submission portal is designed to ingest and curate ~25 ‘omics and ~50 physiological-phenotypic-imaging assay data types. Tools available for OSDR users include: 1) an Environmental Data Application to compare radiation, CO2, relative humidity, temperature, and other telemetry across missions and subjects, 2) the RadLab database, a collaboration between NASA, ESA, the German and Italian Space Agencies, and the Bulgarian Academy of Sciences, and 3) a Multi-study visualization tool which enables users to look across and combine ‘omics datasets. There are ~600 volunteer OSDR Analysis Working Group (AWG) members providing feedback on scientific data/metadata standards and collaborating to mine-reuse OSDR in research. OSDR/GeneLab has enabled ~60 publications reusing data as of October 2023.

space biology↗

DOE OSTI2026

A Tutorial on Bayesian analysis of linear shock compression data

Gas gun and other shock compression experiments often produce shock wave velocity measurements that are linearly associated with particle velocity. Traditionally, this empirical relationship is quantified with a single Hugoniot curve that is estimated using least squares regression. However, for downstream modeling and simulation tasks, it is often more useful to have multiple Hugoniot curves in the pressure–volume plane that are consistent with the data. We employ Bayesian uncertainty quantification methods as a framework for propagating measurement uncertainty through to model parameters and predictions. Specifically, this Tutorial shows how to sample multiple Hugoniot curves in the pressure–volume plane that are consistent with the shock wave-particle velocity measurements in a two-step Bayesian approach. First, we obtain an analytical expression for the posterior distribution of the linear model parameters using Bayesian linear regression. Second, we propagate samples from the posterior distribution through the Rankine–Hugoniot equations to yield Hugoniot curves in the pressure–volume plane. The procedure is demonstrated with publicly available data on argon, copper, and nickel, and compared against bootstrapping and linear regression. The Bayesian procedure is shown to be interpretable, computationally inexpensive, and less sensitive than an alternative bootstrapping approach to the removal of the point in the copper dataset that has the largest particle velocity. As a Tutorial on Bayesian methodology for the shock compression community, we provide several derivations and explanations that make this paper self-contained, and make all code and data available at github.com/llnl/BALSCD.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2025

Increasing the Scale of the Mass Spectrometry Query Language Compendium with Explainable AI

A significant bottleneck in metabolomics data interpretation is the effective use of domain knowledge to assign structural information based on fragmentation patterns. The mass spectrometry query language (MassQL) aims to make this process accessible and applicable across multiple analysis platforms. While advanced computational methods are capable of predicting compound structures from fragmentation data, AI/ML approaches often rely on complex, opaque criteria that are difficult to interpret or modify. As a result, their predictive patterns cannot be readily translated into human-readable rules, such as those used in MassQL. Here, in this study, we introduce ChemEcho, a machine learning embedding method that converts tandem mass spectrometry data into sparse feature vectors containing peak and neutral mass subformulae to enhance explainable AI/ML-based methods. An advantage of this approach is that decision trees trained using these feature vectors can be directly translated to MassQL. Using a battery of decision trees trained using ChemEcho embeddings to predict molecular attributes, we generated over 1500 MassQL queries for 765 molecular features and evaluated their precision and recall. From these queries, the 50 highest-performing queries were integrated into the MassQL compendium. This set of generated MassQL queries included environmentally and biologically relevant classes such as PFAS and molecules containing phosphate or sulfate substructures. To illustrate the impact these queries would have on a typical metabolomics experiment, these MassQL queries were applied to a public metabolomics data set─resulting in a marked increase in the structural information derived from tandem mass spectra. Access and reuse of these queries is expected to enhance structural annotation in untargeted experiments, leading to more specific claims and advancing many applications in metabolomics.

Harwood, Thomas V. [USDOE Joint Genome Institute (↗

DOE OSTI2026

Distributed Mafic Rock Resources for Carbon Mineralization in Arizona

Ex-situ carbon mineralization is a process by which CO2 is reacted with alkaline silicate minerals and rocks to produce stable carbonate materials, which can be used for other industrial processes. Arizona, U.S.A., hosts abundant surficial mafic rocks in three young volcanic fields, Geronimo-San Bernardino, San Francisco, and Springerville, and other distributed locations throughout the state. We created a Mafic Rock Resource Inventory (MRRI) that categorizes geochemical, physical, and textural characteristics of a diverse suite of surficial mafic rock samples and provide a benchmark reaction dataset parameterizing the temperature, pressure, and pH conditions best suited ex-situ mineralization in different rock types. MRRI data is publicly available online via a map-viewer. We establish two reaction condition sets, varied in temperature, pressure, and pH, where crystal-rich and glassy rocks reach maximum reaction extent and different carbonate phases are formed. Systematic ex-situ mineralization experiments on 21 diverse rock types show trends in geochemical, mineralogical, and reactivity behavior and establish maximum effective capture capacity. From this, scoria cones in three Arizona volcanic fields have a ~62 Gt effective CO₂ storage capacity with one of the fields having a ~42 Gt storage capacity in lava flows. Reactivity results have applications to alkaline mafic rock resources exposed globally, including producing additional effective storage capacity estimates and scaled commercialization of mafic rock ex-situ mineralization, should reaction extents be improved through advances in mineralization techniques. MRRI data were used to create a Direct Air Capture to Mineralization (DACM) systems model, technoeconomic analysis, and life-cycle assessment. These documents are presented as three appendices.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2025

SigTime: Learning and Visually Explaining Time Series Signatures

Understanding and distinguishing temporal patterns in time series data is essential for scientific discovery and decision-making. For example, in biomedical research, uncovering meaningful patterns in physiological signals can improve diagnosis, risk assessment, and patient outcomes. However, existing methods for time series pattern discovery face major challenges, including high computational complexity, limited interpretability, and difficulty in capturing meaningful temporal structures. Here, to address these gaps, we introduce a novel learning framework that jointly trains two Transformer models using complementary time series representations: shapelet-based representations to capture localized temporal structures and traditional feature engineering to encode statistical properties. The learned shapelets serve as interpretable signatures that differentiate time series across classification labels. Additionally, we develop a visual analytics system—SigTime—with coordinated views to facilitate exploration of time series signatures from multiple perspectives, aiding in useful insights generation. We quantitatively evaluate our learning framework on eight publicly available datasets and one proprietary clinical dataset. Additionally, we demonstrate the effectiveness of our system through two usage scenarios along with the domain experts: one involving public ECG data and the other focused on preterm labor analysis.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2024

Proteomics Analysis of Human Contaminant Proteins

Complete characterization of unknowns via proteomics remains challenging. There exist regions of mass spectrometry-based proteomics data where empirical measurements are not attributed to peptides, and/or sequenced peptides from mass spectra are not attributed to any source. These uncharacterized regions are known as the “dark” proteome. Many proteomics tools rely on some a priori knowledge of sample composition; few tools allow for investigation of unknowns without relying on composition assumptions. Further, the potential low abundance of minor traces in these uncharacterized regions can make elucidation of the “dark” proteome challenging. Herein, we describe the development and evaluation of approaches to study the “dark” proteome and move towards an untargeted approach for more complete characterization, namely by studying minor human protein traces in non-human samples and combining that approach with non-human source organism identification without relying on assumptions. Human protein markers, in the form of genetically variant peptides, have been extensively examined in a variety of human matrices, including blood, plasma, and hair, but have yet to be investigated in non-human samples, such as cell cultures, as human contaminant traces. Genetically variant peptides are those that are found in proteins carrying single nucleotide polymorphisms. In this work, we aimed to (1) investigate the feasibility of detecting human contaminant genetically variant peptides (GVPs) in a diverse set of non-human organisms using public proteomics data and a computational pipeline, as well as to (2) develop a combined capability for untargeted source organism characterization and GVP detection. To our knowledge, this is the first report of applying these approaches towards a more complete proteomic characterization of unknowns. We successfully demonstrate the feasibility of broad human contaminant GVP detection in proteomics data, develop a better understanding of GVP detectability, characterize the sample-to-sample variability in GVP detection, and identify a core set of GVPs that can potentially be used as markers indicative of the human contaminant traces portion of the “dark” proteome. Further, we developed and evaluated a combined pipeline, MARLOWE-GVP, that enables both untargeted source organism characterization and GVP detection. We show high accuracy of correct source organism characterization and high degree of similarity of human contaminant GVP detection compared to the conventional approach. Success on both these efforts have allowed us to advance our understanding and characterization of the “dark” proteome.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2026

Semi-Analytical Hierarchical Bayesian Inference of Nonlinear Model Structure in Stochastic Dynamics: Applied to Compartmental Models of Infectious Diseases

A Bayesian computational framework for parsimonious inference in stochastic nonlinear dynamical systems is presented. This framework enables the concurrent estimation of system states, time-varying parameters, time-invariant parameters, and the optimal sparsity structure of the model parameters. Because differential equation-based models are often simplified mechanistic or phenomenological representations, robust inference from noisy measurement data requires explicit treatment of model error and uncertainty. Model error and time-varying parameters can be represented as random processes, enabling inference while making minimal assumptions about the underlying sources of discrepancy and variability. Adopting stochastic differential equation representations affords the model significant flexibility, but can also render it susceptible to overfitting during statistical inversion, where the inferred model may track noise rather than the underlying signal. To alleviate the effects of overfitting and to enable the discovery of the optimal sparse representation of the time-invariant parameters, a Bayesian sparse learning algorithm is embedded within the framework. This sparse learning framework adopts an approximate hierarchical Bayesian setting defined by a series of semi-analytical expressions. The model structure inference framework is validated using a stochastic compartmental model for tracking and forecasting active cases of an infectious disease. Compartmental models describe population-level infectious disease dynamics through interactions among population fractions grouped by disease state. Mathematically, such models consist of a system of coupled ordinary differential equations. This example adopts an expressive compartmental model that includes multiple possible interactions between disease states, motivated by early uncertainty surrounding COVID-19 reinfection dynamics and their implications for long-term epidemic forecasting. The sparse learning exercise permits the inference of a priori unknown epidemiological dynamics from simulated public health data, discovering the nested compartmental model that optimizes the trade-off between average data-fit and model complexity. It is shown that inducing sparsity among the model parameters eliminates redundant interactions between compartments, equivalently revealing the optimal coupling structure between differential equations.

97 MATHEMATICS AND COMPUTING↗