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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 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Measuring the Energy Consumption and Efficiency of Deep Neural Networks: An Empirical Analysis and Design Recommendations

Addressing the "Red-AI" trend of rising energy consumption by large-scale neural networks, this study investigates the measured energy consumption of training various fully connected neural network architectures. We introduce the BUTTER-E dataset, an augmentation to the BUTTER Empirical Deep Learning dataset, containing energy consumption and performance data from 41,129 individual experimental runs spanning 30,582 distinct configurations: 13 datasets, 20 sizes (trainable parameters), 8 "shapes", and 14 depths on both CPUs and GPUs using node-level watt-meters. This dataset reveals the complex relationship between dataset size, network structure, and energy use. Our analysis uncovers a surprising, hardware-mediated non-linear relationship between energy efficiency and network design, challenging the assumption that reducing the number of parameters or FLOPs is the best way to achieve greater energy efficiency. We propose a straightforward and effective energy model that accounts for network size, computing, and memory hierarchy. Highlighting the need for cache-considerate algorithm development, we suggest a codesign approach to energy efficient network, algorithm, and hardware design. This work contributes to the fields of sustainable computing and Green AI, offering practical guidance for creating more energy-efficient neural networks and promoting sustainable AI.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Decision Points and Practical Considerations for AI Projects

In this presentation, I will present business-relevant decisions, risks, and considerations for practical implementations of AI projects. I will use energy efficiency and renewable energy AI projects at NREL as examples and case-studies highlighting the journey from concept to implementation. First, I present challenges, questions, and trade-offs related to system inputs: the data. Next, I will examine issues with system behavior and trust, presenting examples, risks, and mitigation strategies. Finally, I will discuss challenges to effective widespread deployment of AI systems including energy, compute, and time requirements.

AI↗
DOE OSTI2025

Solvent Screening for Separation Processes Using Machine Learning and High-Throughput Technologies

As the chemical industry shifts toward sustainable practices, there is a growing initiative to replace conventional fossil-derived solvents with environmentally friendly alternatives such as ionic liquids (ILs) and deep eutectic solvents (DESs). Artificial intelligence (AI) plays a key role in the discovery and design of novel solvents and the development of green processes. This review explores the latest advancements in AI-assisted solvent screening with a specific focus on machine learning (ML) models for physicochemical property prediction and separation process design. Additionally, this paper highlights recent progress in the development of automated high-throughput (HT) platforms for solvent screening. Finally, this paper discusses the challenges and prospects of ML-driven HT strategies for green solvent design and optimization. To this end, this review provides key insights to advance solvent screening strategies for future chemical and separation processes.

Artificial intelligence↗
NASA NTRS1985

High Resolution Spectrometry of Leaf and Canopy Chemistry for Biochemical Cycling

High-resolution laboratory spectrophotometer and Airborne Imaging Spectrometer (AIS) data were used to analyze forest leaf and canopy chemistry. Fundamental stretching frequencies of organic bonds in the visible, near infrared and short-wave infrared are indicative of concentrations and total content of nitrogen, phosphorous, starch and sugar. Laboratory spectrophotometer measurements showed very strong negative correlations with nitrogen (measured using wet chemistry) in the visible wavelengths. Strong correlations with green wet canopy weight in the atmospheric water absorption windows were observed in the AIS data. A fairly strong negative correlation between the AIS data at 1500 nm and total nitrogen and nitrogen concentration was evident. This relationship corresponds very closely to protein absorption features near 1500 nm.

Spanner, M. A.↗
NASA NTRSDate not supplied

Crew Time Requirements in Future Space Greenhouses - What Can We Infer from Current Analog and Space Missions?

Efficient crop production will be required to advance humanity’s presence in space, and for this, accurate predictions of crew time in future space greenhouse modules will be crucial to design and operate these plant growth systems, and schedule crop production. Crew time estimates will also be critical for deciding priorities of automating different aspects of space crop production. Because it is difficult to capture in operational environments, crew time for plant cultivation has only been sporadically recorded in past analog and space missions. We propose a methodology for efficient categorizing and reporting of crew time in space plant growth systems: first identify the different tasks needed to operate the greenhouse module, second define a representative time period for data collection, third accurately report crew time for individual tasks - and their occurrence, and fourth use collected data to improve greenhouse modules and plant growth system designs. Using data from various analog facilities and from the Veggie hardware on ISS, and assumptions for different mission scenarios, we discuss how crew time for plant cultivation can be reduced with adequate choices of crops, automation, artificial intelligence (AI) and virtual assistants, and sufficient crew training. This has major implications for the design of future space greenhouse modules. For example, missions on future space stations or during interplanetary travel would save significant crew time by including leafy greens and microgreens for astronaut’s diet supplement, with automated watering, health and environmental checks, as well as AI managing maintenance schedules, and a virtual assistant for repair activities. This work was funded by NASA Space Biology through NASA postdoctoral program / USRA, by NASA’s Space Biology and Human Research Programs, and by the European Union Horizon 2020 program via the COMPET-07-2014 - Space exploration – Life-support subprogram (reference number: 636501).

Lucie Poulet↗
NASA NTRSDate not supplied

Crew Time Requirements in Future Space Greenhouses: What Can We Infer from Current Analog and Space Missions?

Efficient crop production will be required to advance humanity’s presence in space, and for this, accurate predictions of crew time in future space greenhouse modules will be crucial to design and operate these plant growth systems, and schedule crop production. Crew time estimates will also be critical for deciding priorities of automating different aspects of space crop production. Because it is difficult to capture in operational environments, crew time for plant cultivation has only been sporadically recorded in past analog and space missions. We propose a methodology for efficient categorizing and reporting of crew time in space plant growth systems: first identify the different tasks needed to operate the greenhouse module, second define a representative time period for data collection, third accurately report crew time for individual tasks - and their occurrence, and fourth use collected data to improve greenhouse modules and plant growth system designs. Using data from various analog facilities and from the Veggie hardware on ISS, and assumptions for different mission scenarios, we discuss how crew time for plant cultivation can be reduced with adequate choices of crops, automation, artificial intelligence (AI) and virtual assistants, and sufficient crew training. This has major implications for the design of future space greenhouse modules. For example, missions on future space stations or during interplanetary travel would save significant crew time by including leafy greens and microgreens for astronaut’s diet supplement, with automated watering, health and environmental checks, as well as AI managing maintenance schedules, and a virtual assistant for repair activities. This work was funded by NASA Space Biology through NASA postdoctoral program / USRA, by NASA’s Space Biology and Human Research Programs, and by the European Union Horizon 2020 program via the COMPET-07-2014 - Space exploration – Life-support subprogram (reference number: 636501).

Veggie↗
NASA NTRS1987

AIS-2 spectra of California wetland vegetation

Spectral data gathered by Airborne Imaging Spectrometers-2 from wetlands were analyzed. Spectra representing stands of green Salicornia virginica, green Sesuvium verrucosum, senescing Distichlis spicata, a mixture of senescing Scirpus acutus and Scirpus californicus, senescing Scirpus paludosus, senescent S. paludosus, mowed senescent S. paludosus, and soil were isolated. No difference among narrowband spectral reflectance of the cover types was apparent between 0.8 to 1.6 micron. There were, however, broadband differences in brightness. These differences were sufficient to permit a fairly accurate decomposition of the image into its major cover type components using a procedure that assumes an additive linear mixture of surface spectra.

Gross, Michael F.↗
NASA NTRSDate not supplied

Designing and Evaluating NASA SPoRT Center’s DustTracker-AI Model for Detecting Dust in NASA/NOAA Geostationary Satellite Imagery

- Near real-time identification of airborne dust in satellite imagery is important for mitigating the adverse effects of dust storms on human activities. - False color Red-Green-Blue (RGB) imagery has been used for dust detection, but it has limitations and can be difficult to interpret. - The NASA Short-term Research and Transition (SPoRT) center has developed a night-time dust detection random forest (NT-DustTracker-AI, Berndt et al. 2021) model using NASA/NOAA Geostationary Operational Environmental Satellite-16 (GOES-16) Advanced Baseline Imager (ABI) infrared imagery as inputs. - The SPoRT center has partnered with the NOAA National Weather Service to evaluate the model for use in weather forecasting operations, and preliminary results have been positive. - The SPoRT center has expanded the model to cover both day and night, continuing to use infrared imagery as inputs. This new model, known as DustTracker-AI, has shown good agreement with available dust observations

Robert A. Junod↗
NASA NTRSDate not supplied

NASA POWER: Providing Analysis-Ready, Cloud-Optimized Data for AI /ML Training and Applications in Earth Science

As global demand for sustainable development grows, the integration of Earth Observation (EO) data into decision making frameworks has become a primary objective for the scientific community. The NASA Prediction of Worldwide Energy Resources (POWER) project serves as a bridge between NASA EO data and the specialized needs of the renewable energy, sustainable infrastructure and agroclimatology communities. In this poster presentation we will present an overview of POWER data products and services along with its use in diverse research to decision-making workflows. By providing over 40 years of high-resolution historical, hourly and daily solar and meteorological data, POWER transforms satellite observations and global model reanalysis into actionable, Analysis-Ready Dataset (ARD). Currently, the project delivers over 250 industry-friendly parameters to the users from different NASA datasets like CERES SYN1Deg, MERRA-2, and IMERG alongside downscaled CMIP6 climate model data, fulfilling over 16 million requests from 50,000 unique users monthly. To ensure data quality and traceability, these parameters are rigorously validated against the ground-based observations from the Baseline Surface Radiation Network (BSRN) and the Global Surface Summary of the Day (GSOD) – these results will be discussed in the presentation. A newly introduced web-based PaRameter Uncertainty ViEwer (PRUVE) tool will be presented that provides an online validation platform to the users that benchmarks satellite-based and assimilation data products against these surface measurements. To reduce technical barriers to data adoption, POWER data is accessible through RESTful APIs, ESRI ArcGIS Image Services, a web-based Data Access Viewer tool, allowing users to visualize, validate and apply the dataset. For efficient data delivery POWER data is cloud-optimized into Zarr datastore accessible through NASA managed Amazon S3 ensures high-performance allowing users to integrate EO directly into operational pipelines. These customized services will be presented. Use cases from application will be presented from the energy sector - such as for design of generation systems, performance monitoring of solar power plants, in infrastructure sector- optimizing building energy efficiency and thermal comfort, in agriculture – such as driving crop simulation and yield forecasting models to enable climate resilient farming. Furthermore, the shift toward machine learning (ML) in EO research that has positioned POWER as a key provider for training datasets which will be discussed. Use-cases will be presented to showcase how NASA data is enabling the development of predictive tools for climate variability and resource management. The poster will present POWER’s future plans including technology development to enhance data traceability and reproducibility and improving I/O performance to support the rapid integration of new EO products, ensuring that POWER remains a robust scalable backend for the evolving landscape of AI-driven Earth Science. Additionally, POWER is developing an AI Agent and an MCP-Server to enable industry AI-Agentic workflows.

Neha Khadka↗
NASA NTRS1992

A linear spectral matching technique for retrieving equivalent water thickness and biochemical constituents of green vegetation

Over the last decade, technological advances in airborne imaging spectrometers, having spectral resolution comparable with laboratory spectrometers, have made it possible to estimate biochemical constituents of vegetation canopies. Wessman estimated lignin concentration from data acquired with NASA's Airborne Imaging Spectrometer (AIS) over Blackhawk Island in Wisconsin. A stepwise linear regression technique was used to determine the single spectral channel or channels in the AIS data that best correlated with measured lignin contents using chemical methods. The regression technique does not take advantage of the spectral shape of the lignin reflectance feature as a diagnostic tool nor the increased discrimination among other leaf components with overlapping spectral features. A nonlinear least squares spectral matching technique was recently reported for deriving both the equivalent water thicknesses of surface vegetation and the amounts of water vapor in the atmosphere from contiguous spectra measured with the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS). The same technique was applied to a laboratory reflectance spectrum of fresh, green leaves. The result demonstrates that the fresh leaf spectrum in the 1.0-2.5 microns region consists of spectral components of dry leaves and the spectral component of liquid water. A linear least squares spectral matching technique for retrieving equivalent water thickness and biochemical components of green vegetation is described.

Gao, Bo-Cai↗
NASA NTRS2009

Hot Subdwarf Stars Among the Objects Rejected from the PG Catalog: a First Assessment Using GALEX Photometry

The hot subdwarf (sd) stars in the Palomar Green (PG) catalog of ultraviolet excess (UVX) objects play a key role in investigations of the frequency and types of binary companions and the distribution of orbital periods. These are important for establishing whether and by which channels the sd stars arise from interactions in close binary systems. It has been suggested that the list of PG sd stars is biased by the exclusion of many stars in binaries, whose spectra show the Ca I1 K line in absorption. A total of 1125 objects that were photometrically selected as candidates were ultimately rejected from the final PG catalog using this K-line criterion. We study 88 of these 'PG-Rejects' (PGRs), to assess whether there are significant numbers of unrecognized sd stars in binaries among the PGR objects. The presence of a sd should cause a large UVX, compared with the cool K-line star. We assemble GALEX, Johnson V, and 2MASS photometry and compare the colors of these PGR objects with those of known sd stars, cool single stars, and hot+cool binaries. Sixteen PGRs were detected in both the far- and near-ultraviolet GALEX passbands. Eleven of these, plus the 72 cases with only an upper limit in the far-ultraviolet band, are interpreted as single cool stars, appropriately rejected by the PG spectroscopy. Of the remaining five stars, three are consistent with being sd stars paired with a cool main sequence companion, while two may be single stars or composite systems of another type. We discuss the implications of these findings for the 1125 PGR objects as a whole. An enlarged study is desirable to increase confidence in these first results and to identify individual sd+cool binaries or other composites for follow-up study. The GALEX AIS data have sufficient sensitivity to carry out this larger study.

Wade, Richard A.↗
DOE OSTI2025

Autonomous Synthesis and Inverse Design of Electrochromic Polymers with High Efficiency and Accuracy

Here, the design and synthesis of functional polymers, aimed at targeted properties through specific structures, have long been challenged by their complex and often nonlinear structure–property relationships. Key processes, including knowledge accumulation for predictive design and experimental refinement and validation, are traditionally labor-insensitive and time-consuming, making it difficult to balance accuracy and efficiency. Here, we introduce an accelerated, autonomous system for the on-demand synthesis of electronic polymers that achieves the desired electrochromic functionality with high accuracy and efficiency. Our approach leverages large language model-assisted data mining, a physics-informed copolymer machine learning model, and an AI-driven autonomous robotic workflow in the Polybot lab. Within 72 h, Polybot autonomously synthesized electrochromic polymers (ECPs) with targeted, previously-unreported color values, including green polymers with specific absorption profiles, precisely fine-tuning copolymer structures with a 5% step size in comonomer composition within a three-monomer system. A publicly accessible ECP informatics database has also been created to foster knowledge exchange.

AI-driven Robotic Lab↗
NASA NTRS1986

Soil types and forest canopy structures in southern Missouri: A first look with AIS data

Spectral reflectance properties of deciduous oak-hickory forests covering the eastern half of the Rolla Quadrangle were examined using Thematic Mapper (TM) data acquired in August and December, 1982 and Airborne Imaging Spectrometer (AIS) data acquired in August, 1985. For the TM data distinctly high relative reflectance values (greater than 0.3) in the near infrared (Band 4, 0.73 to 0.94 micrometers) correspond to regions characterized by xeric (dry) forests that overlie soils with low water retention capacities. These soils are derived primarily from rhyolites. More mesic forests characterized by lower TM band 4 relative reflectances are associated with soils of higher retention capacities derived predominately from non-cherty carbonates. The major factors affecting canopy reflectance appear to be the leaf area index (LAI) and leaf optical properties. The Suits canopy reflectance model predicts the relative reflectance values for the xeric canopies. The mesic canopy reflectance is less well matched and incorporation of canopy shadowing caused by the irregular nature of the mesic canopy may be necessary. Preliminary examination of high spectral resolution AIS data acquired in August of 1985 reveals no more information than found in the broad band TM data.

Green, G. M.↗
NASA NTRS1986

Airborne spectroradiometry: The application of AIS data to detecting subtle mineral absorption features

Analysis of Airborne Imaging Spectrometer (AIS) data acquired in Australia has revealed a number of operational problems. Horizontal striping in AIS imagery and spectral distortions due to order overlap were investigated. Horizontal striping, caused by grating position errors can be removed with little or no effect on spectral details. Order overlap remains a problem that seriously compromises identification of subtle mineral absorption features within AIS spectra. A spectrometric model of the AIS was developed to assist in identifying spurious spectral features, and will be used in efforts to restore the spectral integrity of the data.

Cocks, T. D.↗
NASA NTRS1986

Preliminary geological investigation of AIS data at Mary Kathleen, Queensland, Australia

The Airborne Imaging Spectrometer (AIS) was flown over granitic, volcanic, and calc-silicate terrain around the Mary Kathleen Uranium Mine in Queensland, in a test of its mineralocial mapping capabilities. An analysis strategy and restoration and enhancement techniques were developed to process the 128 band AIS data. A preliminary analysis of one of three AIS flight lines shows that the data contains considerable spectral variation but that it is also contaminated by second-order leakage of radiation from the near-infrared region. This makes the recognition of expected spectral absorption shapes very difficult. The effect appears worst in terrains containing considerable vegetation. Techniques that try to predict this supplementary radiation coupled with the log residual analytical technique show that expected mineral absorption spectra can be derived. The techniques suggest that with additional refinement correction procedures, the Australian AIS data may be revised. Application of the log residual analysis method has proved very successful on the cuprite, Nevada data set, and for highlighting the alunite, linite, and SiOH mineralogy.

Huntington, J. F.↗
NASA NTRS2011

Global Precipitation Measurement, Validation, and Applications Integrated Hydrologic Validation to Improve Physical Precipitation Retrievals for GPM

Land surface modeling and data assimilation can provide dynamic land surface state variables necessary to support physical precipitation retrieval algorithms over land. It is well-known that surface emission, particularly over the range of frequencies to be included in the Global Precipitation Measurement Mission (GPM), is sensitive to land surface states, including soil properties, vegetation type and greenness, soil moisture, surface temperature, and snow cover, density, and grain size. In order to investigate the robustness of both the land surface model states and the microwave emissivity and forward radiative transfer models, we have undertaken a multi-site investigation as part of the NASA Precipitation Measurement Missions (PMM) Land Surface Characterization Working Group. Specifically, we will demonstrate the performance of the Land Information System (LIS; http://lis.gsfc.nasa.gov; Peters-Lidard et aI., 2007; Kumar et al., 2006) coupled to the Joint Center for Satellite Data Assimilation (JCSDA's) Community Radiative Transfer Model (CRTM; Weng, 2007; van Deist, 2009). The land surface is characterized by complex physical/chemical constituents and creates temporally and spatially heterogeneous surface properties in response to microwave radiation scattering. The uncertainties in surface microwave emission (both surface radiative temperature and emissivity) and very low polarization ratio are linked to difficulties in rainfall detection using low-frequency passive microwave sensors (e.g.,Kummerow et al. 2001). Therefore, addressing these issues is of utmost importance for the GPM mission. There are many approaches to parameterizing land surface emission and radiative transfer, some of which have been customized for snow (e.g., the Helsinki University of Technology or HUT radiative transfer model;) and soil moisture (e.g., the Land Surface Microwave Emission Model or LSMEM).

Peters-Lidar, Christa D.↗
DOE OSTI2025

The AI-Base NDA Catalog

The project objective was to create a catalog of NDA capabilities so that as future challenges emerge, potential solutions can be quickly identified. The initial direction was to create a custom database where we would manually add various NDA technologies. During the year, LANL leadership heavily embraced AI tools and established LANL’s Enterprise ChatGPT license. To align with this vision, instead of a database we created a custom GPT to achieve the same capability. We provided a series of references on NDA technologies, the most comprehensive of which is the 2024 “PANDA manual” which is a 700-page textbook. A custom GPT is a semi-isolated version of OpenAI’s GPT model that can be tailored to specific tasks with context documents and instructions. Anyone with a LANL ChatGPT Enterprise account can access the NDA Catalog. Since the GPT exists on the green network, only non-sensitive questions may be asked. The Catalog can be used by simply asking questions in conversational English. We have found the Catalog to be quite accurate, even for heavily technical, complex queries. The Catalog allows the user to describe a measurement problem and it will respond with potential technology solutions.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗