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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 1,369 records · Page 76
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Storing Affordability: Battery Storage as an Asset to Reduce Data Center Cost Shifts

This report examines how battery energy storage systems (BESS) can help utilities accommodate large load growth while protecting affordability for existing ratepayers. Rapid growth in electricity demand from artificial intelligence (AI) data centers is straining the U.S. grid. Furthermore, many new data centers are entering rural markets, which could offer economic benefits but may also pose implementation challenges for smaller utilities. At the same time, retail electricity prices are increasing faster than inflation, elevating customer affordability as a key challenge. While data centers have not been the primary driver of increases in residential prices to date, they have pushed wholesale energy and capacity prices higher in several markets. Fundamental utility cost-allocation principles show that data center growth can be rate-positive for existing customers only if new peak demand grows faster than the costs a utility must incur to serve it. Several factors, including a utility’s degree of wholesale market exposure, forecast uncertainty and stranded-asset risk, and tariff design can determine the outcome of load growth on retail rates. Energy storage can make several affordability contributions in the face of this landscape of uncertainty and market volatility, including deferral of higher-cost grid investments through improved utilization of existing assets and flexibility of new large loads, insulation from volatile wholesale prices through peak shaving, and reliability support to address grid risks stemming from the behavior of AI data center loads. Different potential BESS deployment pathways—utility-scale front-of-the-meter systems, aggregated small-scale storage installations, and data center-sited behind-the-meter storage—are compared against each other and against conventional capacity alternatives. This framework is intended as a conceptual resource to utilities, particularly smaller public utilities with rural service territories, who may be considering the role that energy storage can play in insulating existing ratepayers from data center cost shifts.

25 ENERGY STORAGE↗
NASA NTRS1972

Double floating probe measurements on S-cubed A

Results of plasmapause measurements made onboard S cubed A (explorer 45) satellite are presented. Data cover model calculations made to explain the observed probe potential behavior, comparison of satellite data with VLF data to establish that certain effects seen are interpretable as the plasmapause, and plasmapause behavior during the December 17, 1971 magnetic storm.

Maynard, N. C.↗
NASA NTRS1985

Three-dimensional airflow and hydrometeor trajectory calculation with applications

Numerical codes developed to model potential flows about three-dimensional bodies and applied to examining the flowfields around aircraft nose probes for gathering hydrometeor data are described. The potential flow code accounts for lifting or nonlifting surfaces, the effects of lift vorticity, inlet flows, and compression effects. Particle trajectories are calculated using particle equations of motion, with note taken of conditions in both the far- and near-fields of the flow. Graphic projections of the trajectories can be made from any view angle using another code. The codes also contain routines for handling flow velocities around arrays of spatial locations, trajectory arrays, and the effects of flow distortion on the particle flux.

Norment, H. G.↗
NASA NTRS1998

Estimation of Soil Moisture Profile using a Simple Hydrology Model and Passive Microwave Remote Sensing

Soil moisture is an important component of analysis in many Earth science disciplines. Soil moisture information can be obtained either by using microwave remote sensing or by using a hydrologic model. In this study, we combined these two approaches to increase the accuracy of profile soil moisture estimation. A hydrologic model was used to analyze the errors in the estimation of soil moisture using the data collected during Huntsville '96 microwave remote sensing experiment in Huntsville, Alabama. Root mean square errors (RMSE) in soil moisture estimation increase by 22% with increase in the model input interval from 6 hr to 12 hr for the grass-covered plot. RMSEs were reduced for given model time step by 20-50% when model soil moisture estimates were updated using remotely-sensed data. This methodology has a potential to be employed in soil moisture estimation using rainfall data collected by a space-borne sensor, such as the Tropical Rainfall Measuring Mission (TRMM) satellite, if remotely-sensed data are available to update the model estimates.

Soman, Vishwas V.↗
NASA NTRS2001

Integrated Operations Architecture Technology Assessment Study

As part of NASA's Integrated Operations Architecture (IOA) Baseline, NASA will consolidate all communications operations. including ground-based, near-earth, and deep-space communications, into a single integrated network. This network will make maximum use of commercial equipment, services and standards. It will be an Internet Protocol (IP) based network. This study supports technology development planning for the IOA. The technical problems that may arise when LEO mission spacecraft interoperate with commercial satellite services were investigated. Commercial technology and services that could support the IOA were surveyed, and gaps in the capability of existing technology and techniques were identified. Recommendations were made on which gaps should be closed by means of NASA research and development funding. Several findings emerged from the interoperability assessment: in the NASA mission set, there is a preponderance of small. inexpensive, low data rate science missions; proposed commercial satellite communications services could potentially provide TDRSS-like data relay functions; and. IP and related protocols, such as TCP, require augmentation to operate in the mobile networking environment required by the space-to-ground portion of the IOA. Five case studies were performed in the technology assessment. Each case represented a realistic implementation of the near-earth portion of the IOA. The cases included the use of frequencies at L-band, Ka-band and the optical spectrum. The cases also represented both space relay architectures and direct-to-ground architectures. Some of the main recommendations resulting from the case studies are: select an architecture for the LEO/MEO communications network; pursue the development of a Ka-band space-qualified transmitter (and possibly a receiver), and a low-cost Ka-band ground terminal for a direct-to-ground network, pursue the development of an Inmarsat (L-band) space-qualified transceiver to implement a global, low data rate network for LEO/MEO, mission spacecraft; and, pursue developmental research for a miniaturized, high data rate optical transceiver.

Source record↗
NASA NTRSDate not supplied

Balloon Data Readout via Free-Space Optical Communications

Free-space optical communications is a mature technology, using eye-safe beams and small-terminals, that has the potential to increase balloon data readout rates by orders of magnitude. Large commercial memory buffers paired with incredibly high-rate bursted communications – 100 Gbps has already been demonstrated with higher rates possible with existing commercial technologies – could revolutionize how science missions are designed and run. We will give an overview of the technology, show some examples of how it could already be useful to missions, and propose next steps to make affordable and robust systems be available to the balloon community.

Don Boroson↗
DOE OSTI2021

A systematic approach to generating accurate neural network potentials: the case of carbon

Abstract Availability of affordable and widely applicable interatomic potentials is the key needed to unlock the riches of modern materials modeling. Artificial neural network-based approaches for generating potentials are promising; however, neural network training requires large amounts of data, sampled adequately from an often unknown potential energy surface. Here we propose a self-consistent approach that is based on crystal structure prediction formalism and is guided by unsupervised data analysis, to construct an accurate, inexpensive, and transferable artificial neural network potential. Using this approach, we construct an interatomic potential for carbon and demonstrate its ability to reproduce first principles results on elastic and vibrational properties for diamond, graphite, and graphene, as well as energy ordering and structural properties of a wide range of crystalline and amorphous phases.

Chemistry↗
NASA NTRS2001

Analysis and visualization of single-trial event-related potentials

In this study, a linear decomposition technique, independent component analysis (ICA), is applied to single-trial multichannel EEG data from event-related potential (ERP) experiments. Spatial filters derived by ICA blindly separate the input data into a sum of temporally independent and spatially fixed components arising from distinct or overlapping brain or extra-brain sources. Both the data and their decomposition are displayed using a new visualization tool, the "ERP image," that can clearly characterize single-trial variations in the amplitudes and latencies of evoked responses, particularly when sorted by a relevant behavioral or physiological variable. These tools were used to analyze data from a visual selective attention experiment on 28 control subjects plus 22 neurological patients whose EEG records were heavily contaminated with blink and other eye-movement artifacts. Results show that ICA can separate artifactual, stimulus-locked, response-locked, and non-event-related background EEG activities into separate components, a taxonomy not obtained from conventional signal averaging approaches. This method allows: (1) removal of pervasive artifacts of all types from single-trial EEG records, (2) identification and segregation of stimulus- and response-locked EEG components, (3) examination of differences in single-trial responses, and (4) separation of temporally distinct but spatially overlapping EEG oscillatory activities with distinct relationships to task events. The proposed methods also allow the interaction between ERPs and the ongoing EEG to be investigated directly. We studied the between-subject component stability of ICA decomposition of single-trial EEG epochs by clustering components with similar scalp maps and activation power spectra. Components accounting for blinks, eye movements, temporal muscle activity, event-related potentials, and event-modulated alpha activities were largely replicated across subjects. Applying ICA and ERP image visualization to the analysis of sets of single trials from event-related EEG (or MEG) experiments can increase the information available from ERP (or ERF) data. Copyright 2001 Wiley-Liss, Inc.

Non-NASA Center↗
DOE OSTI2025

Data for Identifying the best high-biomass sorghum hybrids based on biomass yield potential and feedstock quality affected by nitrogen fertility management under various environments

Data were collected from agronomy fields in Urbana and Ewing, IL, during the 2022 and 2023 growing seasons. The dataset includes dry biomass yield, nitrogen, phosphorus, and potassium concentrations and removals, and chemical composition elements (cellulose, hemicellulose, lignin, and soluble fractions) for 13 high-biomass sorghum hybrids. data_sharing.xlsx contains 20 columns and 104 rows. Below is the explanation of all variables in the file: Year: 2022; 2023 Location: Urbana, IL; Ewing, IL N rate (kg-N/ha): 0; 112 Hybrid #: H1-H13 Pedigree: Pedigree for 13 hybrids Dry biomass yield (Mg/ha): Aboveground dry biomass yield N (g/kg): Nitrogen concentration in plant tissue P (g/kg): Phosphorus concentration in plant tissue K (g/kg): Potassium concentration in plant tissue N (kg/ha): Nitrogen removal by aboveground biomass P (kg/ha): Phosphorus removal by aboveground biomass K (kg/ha): Potassium removal by aboveground biomass Cellulose (g/kg): Cellulose concentration in plant tissue Hemicellulose (g/kg): Hemicellulose concentration in plant tissue Lignin (g/kg): Lignin concentration in plant tissue Soluble (g/kg): Soluble concentration in plant tissue Cellulose (Mg/ha): Cellulose content in aboveground biomass Hemicellulose (Mg/ha): Hemicellulose content in aboveground biomass Lignin (Mg/ha): Lignin content in aboveground biomass Soluble (Mg/ha): Soluble content in aboveground biomass

environmental adaptability↗
NASA NTRS1996

Pioneer Venus Data Analysis

Analysis and interpretation of data from the Orbiter Retarding Potential Analyzer (ORPA) onboard the Pioneer Venus Orbiter is reported. By comparing ORPA data to proton data from the Orbiter Plasma Analyzer (OPA), it was found that the ORPA suprathermal electron densities taken outside the Venusian ionopause represent solar wind electron densities, thus allowing the high resolution study of Venus bow shocks using both magnetic field and solar wind electron data. A preliminary analysis of 366 bow shock penetrations was completed using the solar wind electron data as determined from ORPA suprathermal electron densities and temperatures, resulting in an estimate of the extent to which mass loading pickup of O+ (UV ionized O atoms flowing out of the Venus atmosphere) upstream of the Venus obstacle occurred. The pickup of O+ averaged 9.95%, ranging from 0.78% to 23.63%. Detailed results are reported in two attached theses: (1) Comparison of ORPA Suprathermal Electron and OPA Solar Wind Proton Data from the Pioneer Venus Orbiter and (2) Pioneer Venus Orbiter Retarding Potential Analyzer Observations of the Electron Component of the Solar Wind, and of the Venus Bow Shock and Magnetosheath.

Jones, Douglas E.↗
DOE OSTI2019

Interatomic Potential Model Development: Finite-Temperature Dynamics Machine Learning

Developing an accurate interatomic potential model is a prerequisite for achieving reliable results from classical molecular dynamics (CMD) simulations; however, most of the potentials are biased as specific simulation purposes or conditions are considered in the parameterization. Here, for developing an unbiased potential, a finite-temperature dynamics machine learning (FTD-ML) approach is proposed, and its processes and feasibility are demonstrated using the Buckingham potential model and aluminum (Al) as an example. Compared with conventional machine learning approaches, FTD-ML exhibits three distinguished features: 1) FTD-ML intrinsically incorporates more extensive configurational and conditional space for enhancing the transferability of developed potentials; 2) FTD-ML employs various properties calculated directly from CMD, for ML model training and prediction validation against experimental data instead of first-principles data; 3) FTD-ML is much more computationally cost effective than first-principles simulations, especially when the system size increases over 10 3 atoms as employed in this research for ensuring reliable training data. The Al Buckingham potential developed by the FTD-ML approach exhibits good performance for general simulation purposes. Thus, the FTD-ML approach is expected to contribute to a fast development of interatomic potential model suitable for various simulation purposes and conditions, without limitation of model type, while maintaining experimental-level accuracy.

42 ENGINEERING↗
DOE OSTI2017

Data driven modeling of plastic deformation

In this paper the application of machine learning techniques for the development of constitutive material models is being investigated. A flow stress model, for strain rates ranging from 10 –4 to 10 12 (quasi-static to highly dynamic), and temperatures ranging from room temperature to over 1000 K, is obtained by beginning directly with experimental stress-strain data for Copper. An incrementally objective and fully implicit time integration scheme is employed to integrate the hypo-elastic constitutive model, which is then implemented into a finite element code for evaluation. Accuracy and performance of the flow stress models derived from symbolic regression are assessed by comparison to Taylor anvil impact data. The results obtained with the free-form constitutive material model are compared to well-established strength models such as the Preston-Tonks-Wallace (PTW) model and the Mechanical Threshold Stress (MTS) model. Here, preliminary results show candidate free-form models comparing well with data in regions of stress-strain space with sufficient experimental data, pointing to a potential means for both rapid prototyping in future model development, as well as the use of machine learning in capturing more data as a guide for more advanced model development.

36 MATERIALS SCIENCE↗
DOE OSTI2015

Temperature dependent van der Pauw-Hall measurements on sodium doped single crystalline cadmium telluride

In this report, results of the temperature dependent electrical conductivity measurements conducted on single crystalline cadmium telluride (CdTe), containing sodium (Na) impurities are presented and discussed. The electrical conductivity measurements were conducted using an apparatus that allowed the implementation of a standard van der Pauw-Hall effect technique through which the electrical resistivity, concentration of majority carriers, as well as the carrier mobility were determined for temperatures ranging between 24 K and 350 K. Over this temperature range, the electrical resistivity was observed to change by 7 orders of magnitude. Hall measurements showed that the hole concentration at 300 K was ∼3 × 10{sup 15 }cm{sup –3} and the hole mobility at the same temperature was ∼80 cm{sup 2}/V s. Measuring the concentration of holes as a function of the sample temperature enabled the estimation of the acceptor energy level with respect to the valence band maximum to be ∼60 meV. The same data also revealed the potential presence of a compensating donor level. Furthermore, the hole mobility was also analyzed over the entire temperature range and the data revealed that above 100 K, the carrier mobility was dominated by the scattering of holes from lattice vibrations.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2022

200-IA-1 Operable Unit Ecological Risk Screen

The purpose of this environmental calculation file (ECF) is to document the assumptions, inputs, methodology, equations, and results of a screening level ecological risk assessment (SLERA) conducted for eight of 12 representative waste sites within the newly formed 200 IA-1 Operable Unit (OU); the remaining four waste sites lacked soil data within the shallow vadose zone and were not further evaluated. The SLERA was conducted to evaluate ecological receptor exposure and potential risk using soil data for radionuclides and nonradionuclides occurring within the shallow vadose zone (0 to 15 ft) soil of the OU. The representative Hanford ecological receptors evaluated are: plants, soil invertebrates, California quail (Callipepla californica), Meadowlark (Sturnella neglecta), Killdeer (Charadrius vociferus), Red-tailed hawk (Buteo jamaicensis), Great Basin pocket mouse (Perognathus parvus), Deer mouse (Peromyscus maniculatus), Grasshopper mouse (Onychomys leucogaster), and badger (Taxidea taxus). During the SLERA, detected soil sample results from the shallow vadose zone were used to calculate Hazard Quotients (HQs) for nonradionuclides and Sum of Fractions (SOF) for radionuclides. The results of this SLERA support the preparation of DOE/RL-2020-51, 200-IA-1 Operable Unit Focused Feasibility Study.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
NASA NTRS1987

Detection of soil erosion with Thematic Mapper (TM) satellite data within Pinyon-Juniper woodlands

Pinyon-Juniper woodlands dominate approximately 24.3 million hectares (60 million acres) in the western United States. The overall objective was to test the sensitivity of the LANDSAT Thematic Mapper (TM) spectral data for detecting varying degrees of soil erosion within the Pinyon-Juniper woodlands. A second objective was to assess the potential of the spectral data for assigning the Universal Soil Loss Equation (USLE) crop management (C) factor values to varying cover types within the woodland. Thematic Mapper digital data for June 2, 1984 on channels 2, 3, 4, and 5 were used. Digital data analysis was performed using the ELAS software package. Best results were achieved using CLUS, an unsupervised clustering algorithm. Fifteen of the 40 Pinyon-Juniper signatures were identified as being relatively pure Pinyon-Juniper woodland. Final analysis resulted in the grouping of the 15 signatures into three major groups. Ten study sites were selected from each of the three groups and located on the ground. At each site the following field measurements were taken: percent tree canopy and percent understory cover, soil texture, total soil loss, and soil erosion rate estimates. A technique for measuring soil erosion within Pinyon-Juniper woodlands was developed. A theoretical model of site degradation after Pinyon-Juniper invasion is presented.

Price, Kevin Paul↗
DOE OSTI2023

Data efficiency and extrapolation trends in neural network interatomic potentials

Abstract Recently, key architectural advances have been proposed for neural network interatomic potentials (NNIPs), such as incorporating message-passing networks, equivariance, or many-body expansion terms. Although modern NNIP models exhibit small differences in test accuracy, this metric is still considered the main target when developing new NNIP architectures. In this work, we show how architectural and optimization choices influence the generalization of NNIPs, revealing trends in molecular dynamics (MD) stability, data efficiency, and loss landscapes. Using the 3BPA dataset, we uncover trends in NNIP errors and robustness to noise, showing these metrics are insufficient to predict MD stability in the high-accuracy regime. With a large-scale study on NequIP, MACE, and their optimizers, we show that our metric of loss entropy predicts out-of-distribution error and data efficiency despite being computed only on the training set. This work provides a deep learning justification for probing extrapolation and can inform the development of next-generation NNIPs.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Benchmark calculations of pure neutron matter with realistic nucleon-nucleon interactions

Here, we report benchmark calculations of the energy per particle of pure neutron matter as a function of the baryon density using three independent many-body methods: Brueckner–Bethe–Goldstone, Fermi hypernetted chain/single-operator chain, and auxiliary-field diffusion Monte Carlo. Significant technical improvements are implemented in the latter two methods. The calculations are made for two distinct families of realistic coordinate-space nucleon-nucleon potentials fit to scattering data, including the standard Argonne v 18 interaction and two of its simplified versions, and four of the new Norfolk Δ -full chiral effective field theory potentials. Primarily because of the advancements in the auxiliary-field diffusion Monte Carlo, we observe good agreement among the three many-body techniques up to nuclear saturation density—the maximum difference in the energy per particle is within 1.5 MeV for all the potentials we consider. At higher densities, the divergences become more important, and are mainly driven by the Fermi hypernetted chain/single-operator calculations. We also study the connection between nucleon-nucleon scattering data and the energy per particle of pure neutron matter. Our results suggest that fitting to higher-energy nucleon-nucleon scattering helps reduce the spread of energies among the models.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2016

A comparison of Redlich-Kister polynomial and cubic spline representations of the chemical potential in phase field computations

Free energies play a central role in many descriptions of equilibrium and non-equilibrium properties of solids. Continuum partial differential equations (PDEs) of atomic transport, phase transformations and mechanics often rely on first and second derivatives of a free energy function. The stability, accuracy and robustness of numerical methods to solve these PDEs are sensitive to the particular functional representations of the free energy. In this communication we investigate the influence of different representations of thermodynamic data on phase field computations of diffusion and two-phase reactions in the solid state. First-principles statistical mechanics methods were used to generate realistic free energy data for HCP titanium with interstitially dissolved oxygen. While Redlich-Kister polynomials have formed the mainstay of thermodynamic descriptions of multi-component solids, they require high order terms to fit oscillations in chemical potentials around phase transitions. Here, we demonstrate that high fidelity fits to rapidly fluctuating free energy functions are obtained with spline functions. As a result, spline functions that are many degrees lower than Redlich-Kister polynomials provide equal or superior fits to chemical potential data and, when used in phase field computations, result in solution times approaching an order of magnitude speed up relative to the use of Redlich-Kister polynomials.

36 MATERIALS SCIENCE↗