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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

DOE BSSD Performance Management Metrics Report Q2

The vision of the National Microbiome Data Collaborative (NMDC) centers on the concept of connecting data, people, and ideas to advance microbiome innovation and discovery. Building data infrastructure, while key to NMDC’s ability to execute on our vision, can only go so far in creating scientific impact. By fostering strong community partnerships and developing a set of robust community outreach and training programs, we are able to turn our products – the Submission Portal, NMDC EDGE, and the Data Portal – into tools that empower the scientific community. Our multi-pronged community building approach spans individual researchers, research teams, consortia and scientific societies, and institutions and federal agencies. To foster a collaborative and inclusive community-centered environment, we have identified three strategic objectives to promote an inclusive and connected community: (1) recognize and support the diverse research needs and perspectives of the microbiome research community; (2) promote best practices across the microbiome community, from researchers to funders, through community-driven practices (FAIR, CARE, and TRUST); and (3) build a microbiome ecosystem that enables scientific discovery and innovation across stakeholders. These strategic objectives allow our team to focus on impact across a diverse range of activities, from launching the American Society for Microbiology (ASM) Microbiome Data Prize to supporting the Ambassador and Champions programs fostering learning and building a collaborative network. We broadly communicate our work through social media (X/Twitter, LinkedIn, and Instagram), The Microbiome Standard (our quarterly newsletter), and Annual Reports. All our work is underpinned by a strong commitment to diversity, equity, and inclusion as articulated in our Action Plan that tracks progress towards key metrics. A core component of our engagement strategy is user research. User research ensures the Submission Portal, NMDC EDGE, Data Portal, and the new Field Notes mobile app are designed with and for the scientific community. Our user research efforts consist of asking researchers exploratory questions to collect information on researcher priorities, methodologies, and perceptions to ensure that we are aware of the current state of microbiome research. Our usability testing provides researchers with prototypes or test environments of the NMDC products, and we capture valuable information on how users interact with the products to make improvements. Given the diverse nature of microbiome work, we acknowledge that we are not aware of all pressing data challenges and thus rely on the research community to help us identify the most important issues to prioritize. To date, we have conducted 24 interviews and one beta-testing call with 10 participants across all NMDC products, which have generated 321 insights and 120 action items. Herein, we describe the ways we engage with the microbiome research community to advance the NMDC mission.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

A new control score concept for building performance assessment

In buildings, performance assessment often focuses on energy use with metrics such as energy use intensity (EUI) used to benchmark performance. However, energy performance of a building is fundamentally determined by the control system that engages the energy-using systems. There are two aspects of control that are of particular importance: (1) the ability to regulate process variables to their setpoints; and (2) whether the setpoints are at the right levels and/or following desired profiles. Most buildings do not reach their energy efficiency potential due to deficiencies in control performance and operators do not have access to metrics that can illuminate these deficiencies. Here this paper addresses this problem by providing novel techniques that combine these two aspects of control performance into a single standardized score on the scale of 0-10. The concept of a standardized control scores enables all systems in a building to be compared on the same scale and also for scores to be rolled up to different levels in the building and system hierarchy for system-wide analysis. The paper presents the theory for the method, describes a prototype tool for displaying scores, and presents results from application to a large building in Minneapolis.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2020

Investigation of structural effects of aromatic compounds on sooting tendency with mechanistic insight into ethylphenol isomers

Small aromatic molecules with oxygen-containing functional groups are a promising class of fuel additives, as they can be readily sourced from depolymerized lignin. These oxygenated aromatic compounds (OACs) show a lower sooting tendency than aromatic hydrocarbons, but OACs having alkyl groups such as ethylphenol show a higher sooting tendency than other OACs such as phenol and anisole, despite the oxygen moiety. In this study, we investigate the relationship between chemical structure and soot precursor formation to explain observed differences in the sooting tendency of OACs and to gain insight into how alkyl or oxygenated substituents on the aromatic ring affect soot precursor formation. The weakest bond for 15 aromatic compounds was identified and cleavage of these bonds was shown to generate either benzyl or phenoxy radicals. A linear relationship between standard enthalpy of formation (ΔH f o ) of these radicals and the yield sooting index (YSI) was found, and thus ΔH f ° can be applied as a metric to estimate YSIs of various aromatic compounds; higher ΔH f o of a radical indicates an increase in the radical reactivity and leads to more soot precursor formation. Flow reactor experiments were performed for 2-ethylphenol and 3-ethylphenol to elucidate how ortho and meta substitution effects the sooting tendency. Soot precursors were identified from the experiment and their formation pathways were investigated computationally. 2-ethylphenol produces more oxygenated products than 3-ethylphenol since the ortho position has increased resonance stabilization of radical intermediates, which leads to lower YSI. Overall, these results further inform the selection of potential biomass-derived fuel blendstocks that have favorable sooting tendencies.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Assessment methods for determining small changes in hearing performance over time

Although the behavioral pure-tone threshold audiogram is considered the gold standard for quantifying hearing loss, assessment of speech understanding, especially in noise, is more relevant to quality of life but is only partly related to the audiogram. Metrics of speech understanding in noise are therefore an attractive target for assessing hearing over time. However, speech-in-noise assessments have more potential sources of variability than pure-tone threshold measures, making it a challenge to obtain results reliable enough to detect small changes in performance. Here, this review examines the benefits and limitations of speech-understanding metrics and their application to longitudinal hearing assessment, and identifies potential sources of variability, including learning effects, differences in item difficulty, and between- and within-individual variations in effort and motivation. We conclude by recommending the integration of non-speech auditory tests, which provide information about aspects of auditory health that have reduced variability and fewer central influences than speech tests, in parallel with the traditional audiogram and speech-based assessments.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2026

BrickQA: Bridging the Semantic Gap in Building Operations with Dynamic Graph Exploration

While standardized ontologies like the Brick schema address data heterogeneity in Building Automation Systems (BAS), accessing this semantic data remains a challenge as domain experts often lack the expertise to formulate complex SPARQL queries. To bridge this gap, we present BrickQA, a Large Language Model (LLM)-based framework that translates natural language into executable SPARQL queries through structured query decomposition, dynamic schema exploration, and inline validation. BrickQA utilizes an iterative reasoning agent to actively navigate graph topology through dynamic exploration actions without requiring exhaustive context injection or model fine-tuning. This approach effectively mitigates hallucinations, particularly in large-scale building knowledge graphs. Empirical evaluation on BuildingQA, a standardized benchmark, demonstrates that BrickQA significantly outperforms ReAct baselines, delivering a 0.291–0.355 absolute F1 improvement while achieving 3 × –12.7 × higher token cost-efficiency. Beyond these metrics, the framework maintains structural fidelity across heterogeneous buildings and remains resilient to ambiguous queries without requiring site-specific fine-tuning. Furthermore, a case study on operational analytics validates the framework’s capability to handle temporal and aggregation constraints, effectively transforming abstract semantic models into actionable facility management insights.1

Ko, Yun-Dam↗
DOE OSTI2024

Modeling Spatial Asymmetries in Teleconnected Extreme Temperatures

Abstract Combining strengths from deep learning and extreme value theory can help describe complex relationships between variables where extreme events have significant impacts (e.g., environmental or financial applications). Neural networks learn complicated nonlinear relationships from large datasets under limited parametric assumptions. By definition, the number of occurrences of extreme events is small, which limits the ability of the data-hungry, nonparametric neural network to describe rare events. Inspired by recent extreme cold winter weather events in North America caused by atmospheric blocking, we examine several probabilistic generative models for the entire multivariate probability distribution of daily boreal winter surface air temperature. We propose metrics to measure spatial asymmetries, such as long-range anticorrelated patterns that commonly appear in temperature fields during blocking events. Compared to vine copulas, the statistical standard for multivariate copula modeling, deep learning methods show improved ability to reproduce complicated asymmetries in the spatial distribution of ERA5 temperature reanalysis, including the spatial extent of in-sample extreme events.

Krock, Mitchell L.↗
DOE OSTI2025

Circularity Futures Workshop Series: Summary Report

The aim of this report is to synthesize key feedback received from the three-part Circularity Futures workshop series held in Spring 2024. The workshop series was conducted by the National Renewable Energy Laboratory (NREL) on behalf of U.S. Department of Energy, Office Energy Efficiency and Renewable Energy (EERE), and was broken into three workshops: Workshop 1 - Circularity Analysis Needs and Priorities; Workshop 2 - Circularity Metrics and Indicators; and Workshop 3 - Circularity Data. Together, the workshops focused on identifying the existing priorities and gaps in the circularity modeling space, understanding different stakeholders' use and interpretation of circularity metrics and indicators, identifying common data gaps and data quality challenges, and assessing the robustness of available solutions. The workshop series brought a diverse group of stakeholders - including representatives from U.S. government offices, national labs, nonprofit organizations, industry, and academia - to collect first-hand feedback on needs, priorities, challenges and opportunities in the circularity modeling and analysis space. The workshop discussions highlighted numerous common needs, priorities and challenges among the interviewed groups. Several topics were frequently discussed, including: 1) Circularity as a pathway for sustainable economic growth: While circularity is generally defined in terms of resource conservation and reducing wasteful disposal of materials, participants agreed that circular strategies should serve broader economic, environmental, and social goals. It is therefore crucial for circularity analysis to look beyond waste reduction and instead evaluate a variety of impact metrics such as cost savings, job creation, air quality, and pollutant emissions. Mutli-criteria decision-making frameworks may be useful for making sense of disparate metrics and evaluating tradeoffs between impact categories.; 2) Economic and social factors are not well understood: Underdevelopment of existing end-of-life (EOL) management infrastructure, inconsistent standardization codes and policy space in reusing recycled content, and suboptimal collection and sorting strategies collectively contribute to uncertainty about the economic potential of circular pathways. The latter observation is consistent among all technologies but more emphasized for renewable energy systems. Social impacts of circularity practices are less understood and less researched than other sustainability aspects.; 3) Inconsistent methods for assessing emerging technologies: LCA and TEA results vary widely depending on the assumptions made with regards to market adoption of new technologies. Emerging technologies suffer limited availability of data needed to conduct a robust circularity analysis. Yet, understanding projected impacts of proposed nascent technology is a key need for different stakeholder groups.; and 4) Lack of temporally and geospatially explicit data: There is a need for open data that represents variations in circularity technologies over time and location. The lack thereof leads to aggregated and potentially misrepresented results in circularity analysis. Sensitivity analyses should be included to verify whether options perceived as more sustainable align with real-world practices.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2023

Integer Sequences from Configurations in the Hausdorff Metric Geometry via Edge Covers of Bipartite Graphs

The Hausdorff metric provides a way to measure the distance between nonempty compact sets in $\mathbb{R}^N$, from which we can build a geometry of sets. This geometry is very different than the standard Euclidean geometry and provides many interesting results. In this paper we focus on line segments in this geometry, where pairs of disjoint sets $A$ and $B$ satisfying certain distance conditions have the property that there are exactly $m$ different sets on the line segment $\overline{AB}$ at every distance from $A$, where $m$ can assume many values different than one. We provide new families of sets that generate previously unrecorded integer sequences via these values of $m$ by connecting the values of $m$ to the number of edge coverings of a graph corresponding to the sets $A$ and $B$.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Polyolefin plastic waste hydroconversion to fuels, lubricants, and waxes: a comparative study

Hydroconversion technologies have surged to the forefront of deconstructing plastic waste. Recent studies have been performed over several catalysts with varying conditions and plastics that make comparisons difficult. We compile and compare data from the literature by introducing various metrics and perform a simple energy analysis. We draw mechanistic similarities to and differences from the past literature on small alkane hydroconversion and leverage the former to propose standard approaches to tune product selectivity. We exemplify the plastics materials gap and the challenges it creates. Lastly, we discuss the current limitations and suggest future work.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

Velocity-space Origins of the Pressure–Strain Interaction in Multipopulation Distributions and Its Application to Magnetic Reconnection

A forefront research question is how energy evolves in weakly collisional plasmas for which departures from local thermodynamic equilibrium (LTE) are significant. The standard approach is studying the terms in the non-LTE energy evolution equation derived by taking the second moment of the Boltzmann equation, but the resultant fluid metrics do not retain information about which particles at which velocities drive energy evolution. A widely studied channel for internal energy density evolution is the pressure–strain interaction. Here, we employ the kinetic pressure–strain, a phase-space diagnostic whose velocity-space integral recovers the pressure–strain interaction to disambiguate the contributions to the pressure–strain interaction from disparate particle populations in composite phase-space densities. We develop phase-space analogs of the pressure–strain interaction decompositions to provide the phase-space origins of normal versus sheared flow. We introduce the “kinetic strain-rate” tensor, the phase-space analog of the strain-rate tensor, which we argue is needed to interpret the phase-space origins of the pressure–strain interaction. To demonstrate the utility of these quantities, we investigate them for composite electron distributions near the electron diffusion region in two-dimensional particle-in-cell simulations of antiparallel symmetric magnetic reconnection. We find that the phase-space-based diagnostics isolate the roles of distinct populations. These results contribute to a growing body of work providing new methods for quantifying phase-space energy evolution for a broad array of processes, from magnetic reconnection to collisionless shocks and turbulence, opening new pathways for answering longstanding problems of particle energization in weakly collisional plasmas.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

A Large-Scale Analysis to Optimize the Control and V2V Communication Protocols for CDA Agreement-Seeking Cooperation

Cooperative driving automation (CDA) Class C, agreement-seeking cooperation, is an innovative and practical solution that can promote cooperation among general passenger vehicles on the road. However, more comprehensive studies are needed before establishing the standard protocols of agreement-seeking cooperation, such as communication frequency and the duration of cooperation. Here, this article presents an initiative study on the impacts of communication capabilities on agreement-seeking cooperation. Through a large-scale analysis by regulating vehicle-to-vehicle (V2V) communication metrics, this work suggests desirable system parameters that can maximize the benefits of cooperation and ensure reliable operability while avoiding exhaustive communication loads. As the first step, an example agreement-seeking cooperation system is created for a car-following scenario, including decision-making and control algorithms for autonomous vehicles. Then, software-in-the-loop tests explore the performance of the developed system as it encounters various communication risks, such as latency and message packet drops. The system performance metrics are evaluated from various angles, including the time consumed for the agreement-seeking process, cooperation ratio, and the ratio of faulty cooperation. Energy saving from the cooperation is assessed by using simulation software that can run multiple high-fidelity vehicle models simultaneously. Based on the analyses, this article suggests the V2V communication requirements for the reliable operation of CDA agreement-seeking, which can be referred to when developing the standard protocols of agreement-seeking cooperation.

42 ENGINEERING↗
DOE OSTI2023

Reference data, predictors, and probability grids for forest degradation classes in three sites in the Brazilian Amazon

Forest degradation by fires and selective logging is widespread in the Amazon region. We implemented a gradient boosted classification modeling framework to classify intact, logged, and burned forests at three Amazonian sites: Feliz Natal Municipality and Xingu Indigenous Territory in Mato Grosso State, and Saracá-Taquera National Forest in Pará State. We used forest degradation history from Landsat time-series as reference data and textural metrics derived from PlanetScope images as predictors. Textural metrics were computed using the Gray-Level Co-Occurrence Matrix (GLCM) textural technique. Included in the attached zip file are ten files: - a shapefile containing the reference data (fire and selective logging polygons and year of event) for each site; - a multiband tif file containing the 8 GLCM metrics used as predictors (Mean, Variance, Homogeneity, Contrast, Dissimilarity, Entropy, Angular Second Moment, Correlation) at the original PlanetScope resolution (3.125m) for each site; - a multiband tif file containing the 72 aggregated GLCM metrics used as predictors (Mean, Variance, Homogeneity, Contrast, Dissimilarity, Entropy, Angular Second Moment, and Correlation aggregated using the mean, first quartile, third quartile, maximum, median, minimum, root mean square, standard deviation, and skewness statistics) at 562m resolution for each site; - a multiband tif file containing the 3 probability grids for either intact, logged, or burned forests at the aggregation resolution (562m) for each site.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

4.2.1.31 Integrated Life Cycle Sustainability Analysis

This project provides the Department of Energy's Bioenergy Technologies Office (BETO) with strategic decision-support for the evaluation of its R&D portfolio by developing, validating, and applying a coherent methodology and consistent model framework to quantify the net effects of an expanding US bioeconomy. The framework fills an analysis gap previously identified by Peer Review and supports a related milestone in BETO's Multi-Year Program Plan. The framework was scoped with inputs from practitioners in academia, national laboratories, and federal agencies. The model is a top-down, economy-wide framework using a coherent methodology to compute environmental and socio-economic metrics. It is purposefully complementary to existing bottom-up, process-based techno-economic and life cycle assessment BETO tools and uses their data as inputs. Presently, the model covers several commercial and near-commercial biofuel routes and an emerging pathway for plastics upcycling. It covers temporal detail across four time-steps and is currently being expanded with a prospective modeling capability. The model has provided analyses for the Third Triennial Report to Congress (RtC3) on the environmental impacts of the Renewable Fuel Standard (RFS2), among others. As part of this project, NREL also provides scientific support to BETO in the International Energy Agency's Technology Collaboration Program on Bioenergy (IEA Bioenergy) Task 45 on Sustainability. Here, NREL evaluates and synthesizes activities that develop, compare, or apply metrics, methods, and tools to quantify sustainability effects of bioeconomy products. NREL also coordinates related national lab involvement and a BETO Working Group on Sustainable Land Management.

bioeconomy↗
DOE OSTI2025

A Simple Data-Centric Methodology for Producible Geothermal Well Determinations: Preprint

The Bureau of Land Management (BLM) has traditionally lacked a standardized methodology for determining if a newly drilled geothermal well is "producible," a designation essential for deciding whether a lease should be "held by production." This is a straightforward problem to solve in oil and gas: Demonstrate that a well is economically viable, meaning it produces sufficient oil or gas to exceed direct operating costs and lease-related expenses, such as rentals or minimum royalties. In geothermal, the problem is more complex: Geothermal wells are tightly coupled with the downstream infrastructure - specifically, the power plant, which is often not designed until well after a lease is deemed as "held by production." Although this designation is critical for advancing geothermal power plant development on BLM-managed lands, current geothermal well assessments often rely on ad hoc approaches that can be complex, operator-biased, and heavy in assumptions related to economic viability. To address this, we have developed two complementary methodologies: a minimum power requirement-based approach and a productivity index (PI)-based approach. These methods leverage key flow test data - pressure, temperature, flow rate, and specific enthalpy - to provide reliable and standardized producible well determinations. The minimum power requirement-based approach evaluates wells against specific power output thresholds informed by reservoir experts and the associated temperature requirements. The PI-based approach assesses well productivity using widely accepted reservoir engineering metrics, proposing a threshold of 2.5 kg/s/bar. Both methods are data-driven and grounded in empirical production data from operational geothermal wells, avoiding uncertain economic assumptions while maintaining decision-making accuracy. Wells falling below key performance thresholds (i.e., PI, specific power) are deemed non-producible. These methodologies aim to streamline BLM's decision-making process, reduce nontechnical barriers to geothermal energy adoption, and enable regulatory expansion into states lacking geothermal expertise. Preliminary results indicate clear trends and thresholds in production data that provide actionable insights for evaluating well producibility. Validation using well completion report (WCR) data is ongoing, with promising results demonstrating the potential for these standardized methodologies to impact geothermal development significantly.

15 GEOTHERMAL ENERGY↗
DOE OSTI2023

Standard Model in conformal geometry: Local vs gauged scale invariance

Here, we discuss comparatively local versus gauged Weyl symmetry beyond Standard Model (SM) and Einstein gravity and their geometric interpretation. The SM and Einstein gravity admit a natural embedding in Weyl integrable geometry which is a special limit of Weyl conformal (non-metric) geometry. The theory has a local Weyl scale symmetry but no associated gauge boson. Unlike previous models with such symmetry, this embedding is truly minimal i.e. with no additional fields beyond SM and underlying geometry. This theory is compared to a similar minimal embedding of SM and Einstein gravity in Weyl conformal geometry (SMW) which has a full gauged scale invariance, with an associated Weyl gauge boson. At large field values, both theories give realistic, Starobinsky–Higgs like inflation. The broken phase of the current model is the decoupling limit of the massive Weyl gauge boson of the broken phase of SMW, while the local scale symmetry of the current model is part of the larger gauged scale symmetry of SMW. Hence, the current theory has a gauge embedding in SMW. Unlike in the SMW, we note that in models with local scale symmetry the associated current is trivial, which is a concern for the physical meaning of this symmetry. Therefore, the SMW is a more fundamental UV completion of SM in a full gauge theory of scale invariance that generates Einstein gravity in the (spontaneously) broken phase, as an effective theory.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2024

Anchoring

This software provides methods and functions for training deep image classification models based on the principle of anchoring. It features a user-friendly PyTorch wrapper that facilitates the easy conversion of any model into an anchored model. The software supports various standard datasets and includes scripts for conducting evaluations. Developed with PyTorch, it is compatible with common neural network architectures used for image data. Additionally, it offers tools for computing evaluation metrics to assess model performance.

Narayanaswamy, Vivek Sivaraman↗
DOE OSTI2025

HALLUFIELD: DETECTING LLM HALLUCINATIONS VIA FIELD-THEORETIC MODELING

A research-focused Python package that implements our hallucination-detection method for large language models(LLMs). The code computes stability signals from LLM predictions across various hyperparameters sweeps and combines free-energy/entropy–style metrics to flag likely hallucinations, with tunable thresholds for batch scoring. The repo includes evaluation scripts, config files, and example notebooks to reproduce benchmark results and ablations; it depends on standard open-source libraries (PyTorch, Hugging Face) and runs on CPU/GPU. The repository examples only use public models/datasets only.

Bhattarai, Manish [Los Alamos National Labs]↗
DOE OSTI2023

Acute stress reduces population-level metabolic and proteomic variation

Abstract Background Variation in omics data due to intrinsic biological stochasticity is often viewed as a challenging and undesirable feature of complex systems analyses. In fact, numerous statistical methods are utilized to minimize the variation among biological replicates. Results We demonstrate that the common statistics relative standard deviation (RSD) and coefficient of variation (CV), which are often used for quality control or part of a larger pipeline in omics analyses, can also be used as a metric of a physiological stress response. Using an approach we term Replicate Variation Analysis (RVA), we demonstrate that acute physiological stress leads to feature-wide canalization of CV profiles of metabolomes and proteomes across biological replicates. Canalization is the repression of variation between replicates, which increases phenotypic similarity. Multiple in-house mass spectrometry omics datasets in addition to publicly available data were analyzed to assess changes in CV profiles in plants, animals, and microorganisms. In addition, proteomics data sets were evaluated utilizing RVA to identify functionality of reduced CV proteins. Conclusions RVA provides a foundation for understanding omics level shifts that occur in response to cellular stress. This approach to data analysis helps characterize stress response and recovery, and could be deployed to detect populations under stress, monitor health status, and conduct environmental monitoring.

59 BASIC BIOLOGICAL SCIENCES↗