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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 253 records · Page 14
All sourcesNASA NTRSDOE OSTI
NASA NTRS2002

Use of Satellite Data Assimilation to Infer Land Surface Thermal Inertia

There are two important but observationally uncertain parameters in the grid averaged surface energy budgets of mesoscale models - surface moisture availability and thermal heat capacity. A technique has been successfully developed for assimilating Geostationary Operational Environmental Satellite (GOES) skin temperature tendencies during the mid-morning time frame to improve specification of surface moisture. In a new application of the technique, the use of satellite skin temperature tendencies in early evening is explored to improve specification of the surface thermal heat capacity. Together, these two satellite assimilation constraints have been shown to significantly improve the characterization of the surface energy budget of a mesoscale model on fine spatial scales. The GOES assimilation without the adjusted heat capacity was run operationally during the International H2O Project on a 12-km grid. This paper presents the results obtained when using both the moisture availability and heat capacity retrievals in concert. Preliminary results indicate that retrieved moisture availability alone improved the verification statistics of 2-meter temperature and dew point forecasts. Results from the 1.5 month long study period using the bulk heat capacity will be presented at the meeting.

Lapenta, William↗
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 NTRS1992

Convection of tin in a Bridgman system. I - Flow characterization by effective diffusivity measurements

An electrochemical titration method was used to investigate the dynamic states in a cylindrical layer of convecting tin. The liquid tin was contained in a cell, with curved boundaries made of quartz and flat boundaries made of a solid state electrolyte - yttria-stabilized zirconia (YSZ). The electrolyte acted as a window through which a trace amount of oxygen could be pumped in or out by the application of a constant voltage. The concentration at the YSZ interface was monitored by operating the electrochemical cell in the galvanic mode. Experimentally determined effective diffusivities of oxygen were compared with the molecular diffusivity. Dynamic states in the convective flow were thus inferred. Temperature measurements were simultaneously made in order to identify the onset of oscillations from a steady convective regime. The experiments were conducted for two different aspect ratios for various imposed temperature gradients and two different orientations with respect to gravity. Transcritical states were identified and comparison to two-dimensional numerical models were made.

Sears, B.↗
NASA NTRS1994

The SeaWinds Scatterometer Instrument

The SeaWinds scatterometer instrument is currently being developed by NASA/JPL, as part of the NASA EOS Program, for flight on the Hapanese ADEOS II mission in 1999. This Ku-band radar scatterometer will infer surface wind speed and direction by measuring the radar normalized backscatter cross-section over several different azimuth angles. This paper presents the design characteristics of and operational approach to the instrument itself.

SeaWinds scatterometer EOS program ADEOS II Ku-ban↗
NASA NTRS2008

Material Units, Structures/Landforms, and Stratigraphy for the Global Geologic Map of Ganymede (1:15M)

In the coming year a global geological map of Ganymede will be completed that represents the most recent understanding of the satellite on the basis of Galileo mission results. This contribution builds on important previous accomplishments in the study of Ganymede utilizing Voyager data and incorporates the many new discoveries that were brought about by examination of Galileo data. Material units have been defined, structural landforms have been identified, and an approximate stratigraphy has been determined utilizing a global mosaic of the surface with a nominal resolution of 1 km/pixel assembled by the USGS. This mosaic incorporates the best available Voyager and Galileo regional coverage and high resolution imagery (100-200 m/pixel) of characteristic features and terrain types obtained by the Galileo spacecraft. This map has given us a more complete understanding of: 1) the major geological processes operating on Ganymede, 2) the characteristics of the geological units making up its surface, 3) the stratigraphic relationships of geological units and structures, and 4) the geological history inferred from these relationships. A summary of these efforts is provided here.

Patterson, G. Wesley↗
NASA NTRS2012

HyDE Framework for Stochastic and Hybrid Model-Based Diagnosis

Hybrid Diagnosis Engine (HyDE) is a general framework for stochastic and hybrid model-based diagnosis that offers flexibility to the diagnosis application designer. The HyDE architecture supports the use of multiple modeling paradigms at the component and system level. Several alternative algorithms are available for the various steps in diagnostic reasoning. This approach is extensible, with support for the addition of new modeling paradigms as well as diagnostic reasoning algorithms for existing or new modeling paradigms. HyDE is a general framework for stochastic hybrid model-based diagnosis of discrete faults; that is, spontaneous changes in operating modes of components. HyDE combines ideas from consistency-based and stochastic approaches to model- based diagnosis using discrete and continuous models to create a flexible and extensible architecture for stochastic and hybrid diagnosis. HyDE supports the use of multiple paradigms and is extensible to support new paradigms. HyDE generates candidate diagnoses and checks them for consistency with the observations. It uses hybrid models built by the users and sensor data from the system to deduce the state of the system over time, including changes in state indicative of faults. At each time step when observations are available, HyDE checks each existing candidate for continued consistency with the new observations. If the candidate is consistent, it continues to remain in the candidate set. If it is not consistent, then the information about the inconsistency is used to generate successor candidates while discarding the candidate that was inconsistent. The models used by HyDE are similar to simulation models. They describe the expected behavior of the system under nominal and fault conditions. The model can be constructed in modular and hierarchical fashion by building component/subsystem models (which may themselves contain component/ subsystem models) and linking them through shared variables/parameters. The component model is expressed as operating modes of the component and conditions for transitions between these various modes. Faults are modeled as transitions whose conditions for transitions are unknown (and have to be inferred through the reasoning process). Finally, the behavior of the components is expressed as a set of variables/ parameters and relations governing the interaction between the variables. The hybrid nature of the systems being modeled is captured by a combination of the above transitional model and behavioral model. Stochasticity is captured as probabilities associated with transitions (indicating the likelihood of that transition being taken), as well as noise on the sensed variables.

Narasimhan, Sriram↗
NASA NTRS2022

Vertical Structure in Phytoplankton Growth and Productivity Inferred From Biogeochemical-Argo Floats and the Carbon-Based Productivity Model

Estimates of marine net primary production (NPP) commonly rely on limited in situ 14C incubations or satellite-based algorithms mainly constrained to the surface ocean. Here we combine data from biogeochemical Argo floats with a carbon-based productivity model (CbPM) to compute vertically-resolved estimates of NPP. Inferred NPP profiles derived by informing the CbPM with float-based, depth-resolved, bio-optical data are able to qualitatively reproduce the vertical structure in NPP inferred from in situ 14C incubations at various ocean regions. At station ALOHA, float-based estimates agree within uncertainty with productivity observations at depth, but underestimate surface NPP. We test the ability of the CbPM to infer the depth-resolved structure in NPP from bio-optical properties in the mixed layer, in similar fashion as how remote sensing algorithms of ocean productivity operate. In Southern Ocean waters, the depth-reconstructing implementation of the CbPM overestimates phytoplankton division rates and Chl:C below the mixed layer, resulting in artificially high subsurface NPP when compared with the fully float-informed implementation of the model. The CbPM subsurface extrapolation of phytoplankton Chl, Chl:C division rates, and NPP improves by accounting for deep nutrient (iron) stress impacts on photoacclimation in the Southern Ocean. This improvement is also observed in vertically- integrated NPP, where the mean bias between model implementations in depth-integrated productivity south of 30◦S is reduced by 62 % when account for deep iron limitation. Our results demonstrate that profiling data from biogeochemical Argo floats can serve to inform regional adjustments that lead to the improvement of marine productivity algorithms.

NPP↗
NASA NTRS2007

Using Remotely-Sensed Estimates of Soil Moisture to Infer Soil Texture and Hydraulic Properties across a Semi-arid Watershed

Near-surface soil moisture is a critical component of land surface energy and water balance studies encompassing a wide range of disciplines. However, the processes of infiltration, runoff, and evapotranspiration in the vadose zone of the soil are not easy to quantify or predict because of the difficulty in accurately representing soil texture and hydraulic properties in land surface models. This study approaches the problem of parameterizing soils from a unique perspective based on components originally developed for operational estimation of soil moisture for mobility assessments. Estimates of near-surface soil moisture derived from passive (L-band) microwave remote sensing were acquired on six dates during the Monsoon '90 experiment in southeastern Arizona, and used to calibrate hydraulic properties in an offline land surface model and infer information on the soil conditions of the region. Specifically, a robust parameter estimation tool (PEST) was used to calibrate the Noah land surface model and run at very high spatial resolution across the Walnut Gulch Experimental Watershed. Errors in simulated versus observed soil moisture were minimized by adjusting the soil texture, which in turn controls the hydraulic properties through the use of pedotransfer functions. By estimating a continuous range of widely applicable soil properties such as sand, silt, and clay percentages rather than applying rigid soil texture classes, lookup tables, or large parameter sets as in previous studies, the physical accuracy and consistency of the resulting soils could then be assessed. In addition, the sensitivity of this calibration method to the number and timing of microwave retrievals is determined in relation to the temporal patterns in precipitation and soil drying. The resultant soil properties were applied to an extended time period demonstrating the improvement in simulated soil moisture over that using default or county-level soil parameters. The methodology is also applied to an independent case at Walnut Gulch using a new soil moisture product from active (C-band) radar imagery with much lower spatial and temporal resolution. Overall, results demonstrate the potential to gain physically meaningful soils information using simple parameter estimation with few but appropriately timed remote sensing retrievals.

Santanello, Joseph A.↗
DOE OSTI2026

Agentic framework for programmatic crystal structure generation using a fine-tuned worker–supervisor large language model

Platinum group metals (PGMs) underpin many catalytic technologies but face severe supply constraints, motivating the search for alternative materials and computational methods to accelerate discovery. While atomistic simulation tools such as Pymatgen and ASE have streamlined structure manipulation, they require detailed inputs, limiting accessibility for experimentalists and slowing early-stage exploration. Here, in this study, we present an AI-driven agentic framework that orchestrates worker–supervisor large language models (LLMs). The worker translates natural-language prompts of varying abstraction into valid crystallographic structures using a compact LLM fine-tuned with low-rank adaptation on a curated text–code–CIF dataset, emphasizing energy-efficient training. Benchmarking against the baseline CodeGen-350M-mono model shows that fine-tuning reduces hallucination rates from 100% to as low as 5% and improves structural match accuracy to up to 82% for fully specified inputs. Accuracy declines with decreasing prompt detail but remains nontrivial even when only stoichiometry and space group are provided, underscoring the LLM’s capacity for crystallographic inference. The supervisor Claude LLM evaluates the outputs and triggers iterative refinement through the worker’s built-in structure manipulation capabilities (e.g., supercell scaling, strain, vacancy, and substitution operations). We further demonstrate use cases for technologically relevant catalysts, including IrO 2 , pyrochlore Pb 2 Ir 2 O 7 , Ni 2 FeO 4 , and Ni 3 Mo, where the framework generates physically consistent structures that can be refined via geometry optimization. This work introduces a low-energy, language-driven pathway for integrating human and machine intelligence in materials design, paving the way for AI-assisted synthesis planning and high-throughput screening of complex oxides.

AI agent↗
DOE OSTI2025

HEPCloud Operations at Fermilab—The First Five Years

The HEPCloud Facility at Fermilab has now been in production operation for five years. This facility is a unified provisioning gateway to US high performance computing centers, including NERSC, OLCF, and ALCF, other large supercomputers run by the NSF, and commercial clouds. HEPCloud delivers hundreds of millions of core-hours yearly for CMS. HEPCloud also serves other Fermilab experiments including DUNE, Mu2e, Muon g-2, and NOvA. In this paper we present the practical considerations of operating a distributed facility such as HEPCloud. We also mention some of the interesting research and development that HEPCloud has been used for including GPU-based machine learning inference servers, and tests of Quantum Computing.

Timm, Steven [Fermilab]↗
NASA NTRS1983

Landsat-4 on-board ephemeris determination utilizing the Global Positioning System

The Global Positioning System (GPS) is a worldwide system of navigation data satellites currently being deployed by the United States for land, sea, air and space-borne users. Currently several development satellites have been launched for system evaluation. Eventually the system will include 18 operational satellites. The GPS system utilizes precision clocks to allow users to measure the one-way propagation delay of navigational signals from the satellites, so that users can infer their position by decoding the navigation signal and observing the delay from several satellites. Landsat-4 is the first spacecraft to carry a GPS navigation system into orbit. This paper describes the Landsat-4 GPS experiment, Landsat-4 requirements for ephemeris data and the in-flight performance of the system.

Korenstein, D. A.↗
NASA NTRS1993

A comparison of several techniques to assign heights to cloud tracers

Experimental results are presented which suggest that the water-vapor technique of radiance measurement is a viable alternative to the CO2 technique for inferring the height of semitransparent cloud elements. Future environmental satellites will rely on H2O-derived cloud-height assignments in the wind-field determinations with the next operational geostationary satellite. On a given day, the heights from the H2O and CO2 approaches compare to within 60-110 hPa rms.

Nieman, Steven J.↗
NASA NTRS2000

Two Fiber Optical Fiber Thermometry

An optical fiber thermometer consists of an optical fiber whose sensing tip is given a metallic coating. The sensing tip of the fiber is essentially an isothermal cavity, so the emission from this cavity will be approximately equal to the emission from a blackbody. Temperature readings are obtained by measuring the spectral radiative heat flux at the end of the fiber at two wavelengths. The ratio of these measurements and Planck's Law are used to infer the temperature at the sensing tip. Optical fiber thermometers have high accuracy, excellent long-term stability and are immune to electromagnetic interference. In addition, they can be operated for extended periods without requiring re-calibration. For these reasons. it is desirable to use optical fiber thermometers in environments such as the International Space Station. However, it has recently been shown that temperature readings are corrupted by emission from the fiber when extended portions of the probe are exposed to elevated temperatures. This paper will describe several ways in which the reading from a second fiber can be used to correct the corrupted temperature measurements. The accuracy and sensitivity to measurement uncertainty will be presented for each method.

Jones, Mathew R.↗
NASA NTRS2002

Determining the Compositions of Extraterrestrial Lava Flows

The primary purpose of this research project has been to develop techniques that allow the emplacement conditions of volcanic landforms on other planets to be related to attributes that can be remotely detected with available instrumentation. The underlying assumption of our work is that the appearance of a volcano, lava flow, debris avalanche, or exhumed magmatic intrusion can provide clues about the conditions operating when that feature was first emplaced. Magma composition, amount of crustal heat flow, state of tectonic stress, and climatic conditions are among the important variables that can be inferred from the morphology and texture of an igneous body.

Fink, Jonathan H.↗
NASA NTRS1991

The Au Cathode in the System Li2CO3-CO2-CO at 800 to 900 C

Gold is one of several metals being evaluated at NASA Lewis Research Center as positive electrode catalysts for an alkali metal/molten alkali metal carbonate/carbon dioxide electrochemical cell. Such a cell is proposed for CO2-rich planetary atmospheres such as those of Mars and Venus. Its application could be as a primary power supply, as a secondary power supply recharged either 'chemically' by replenishment of the alkali metal or electrochemically from a central station power source, or as a converter of carbon dioxide to oxygen via a complete electrochemical cycle. For the work being reported, lithium was assumed to be the alkali metal of choice for the negative electrode of the cell, and therefore molten lithium carbonate was the electrolyte used in the Au electrode experiments. Cathodic linear sweep voltammetry (LSV) was the primary analytical technique for evaluating the performance of the Au cathode. interest comprised the cell temperature and the total pressure and composition of the reactant gas. In the absence of operational difficulties, the effect of bubbling the reactant gas through the melt was also determined. On the basis of the variation of electrode performance with changes in these parameters, inferences have been made concerning the electrochemical and chemical processes at and near the electrode. The results of post-test micrographic analyses of the Au cathode are also presented. An attempt is then made to project from the experimental results to some relevant conclusions pertaining to a gold cathode in a practical alkali metal - carbon dioxide cell.

Hagedorn, Norman H.↗
NASA NTRS2011

A Systematic Approach for Engagement Analysis Under Multitasking Environments

An overload condition can lead to high stress for an operator and further cause substantial drops in performance. On the other extreme, in automated systems, an operator may become underloaded; in which case, it is difficult for the operator to maintain sustained attention. When an unexpected event occurs, either internal or external to the automated system, a disengaged operation may neglect, misunderstand, or respond slowly/inappropriately to the situation. In this paper, we discuss a systematic approach monitor for extremes of cognitive workload and engagement in multitasking environments. Inferences of cognitive workload ar engagement are based on subjective evaluations, objective performance measures, physiological signals, and task analysis results. The systematic approach developed In this paper aggregates these types of information collected under the multitasking environment and can provide a real-time assessment or engagement.

Zhang, Guangfan↗
DOE OSTI2025

A resolution independent neural operator

The Deep operator network (DeepONet) is a powerful yet simple neural operator architecture that utilizes two deep neural networks to learn mappings between infinite-dimensional function spaces. This architecture is highly flexible, allowing the evaluation of the solution field at any location within the desired domain. However, it imposes a strict constraint on the input space, requiring all input functions to be discretized at the same locations; this limits its practical applications. Here, in this work, we introduce a general framework for operator learning from input–output data with arbitrary number and locations of sensors. This begins by introducing a resolution-independent DeepONet (RI-DeepONet), enabling it to handle input functions that are arbitrarily, but sufficiently finely, discretized. To this end, we propose two dictionary learning algorithms to adaptively learn a set of appropriate continuous basis functions, parameterized as implicit neural representations (INRs), from correlated signals defined on arbitrary point cloud data. These basis functions are then used to project arbitrary input function data as a point cloud onto an embedding space (i.e., a vector space of finite dimensions) with dimensionality equal to the dictionary size, which can be directly used by DeepONet without any architectural changes. In particular, we utilize sinusoidal representation networks (SIRENs) as trainable INR basis functions. The introduced dictionary learning algorithms are then used in a similar way to learn an appropriate dictionary of basis functions for the output function data, which defines a new neural operator architecture referred to as the R esolution I ndependent N eural O perator (RINO). In the RINO, the operator learning task simplifies to learning a mapping from the coefficients of input basis functions to the coefficients of output basis functions. We demonstrate the robustness and applicability of RINO in handling arbitrarily (but sufficiently richly) sampled input and output functions during both training and inference through several numerical examples.

Deep operator network (DeepONet)↗