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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 901 records · Page 50
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Numerical Modeling & Optimization of the iProTech Pitching Inertial Pump (PIP) Wave Energy Converter (WEC) (CRADA Final Report)

This project represents a continuation of the collaboration between iProTech and NLR to simulate, optimize and design the iProTech Pitching Inertial Pump (PIP) device. The objectives of this TEAMER project are twofold: 1. Refining the physical characteristics of the existing iProTech PIP WEC-Sim model to enhance the model’s fidelity and include controllable components. Key model enhancements target the inclusion of Coulomb friction, the introduction of a controllable bypass valve, and the replacement of traditional check valves with advanced motorized ones. 2. Exploring traditional and advanced control algorithms. From traditional methods like latching control to cutting-edge reinforcement learning (RL) algorithms, the goal is to ensure the PIP device's adaptability and optimal performance across a range of ocean conditions. NLR is tasked with augmenting the WEC-Sim model and implementing the control algorithms, culminating in performance comparison analyses. iProTech will update their existing 3D models, advise on model improvements, and determine crucial system metrics. WEC-Sim, developed in MATLAB/SIMULINK with Simscape Multibody, is the main piece of software that will be used in this project. Coupled with the MATLAB RL Toolbox, it offers a robust platform for in-depth simulation and optimization of the iProTech PIP device. Building on previous work to explore the PIP design space and optimize its geometry, mass distribution, center of gravity and other key parameters, this project aims to refine iProTech’s existing numerical models and develop effective control algorithms that can seamlessly integrate into their future hardware testing campaigns.

16 TIDAL AND WAVE POWER↗
DOE OSTI2014

FOAM (Functional Ontology Assignments for Metagenomes): A Hidden Markov Model (HMM) database with environmental focus

A new functional gene database, FOAM (Functional Ontology Assignments for Metagenomes), was developed to screen environmental metagenomic sequence datasets. FOAM provides a new functional ontology dedicated to classify gene functions relevant to environmental microorganisms based on Hidden Markov Models (HMMs). Sets of aligned protein sequences (i.e. ‘profiles’) were tailored to a large group of target KEGG Orthologs (KOs) from which HMMs were trained. The alignments were checked and curated to make them specific to the targeted KO. Within this process, sequence profiles were enriched with the most abundant sequences available to maximize the yield of accurate classifier models. An associated functional ontology was built to describe the functional groups and hierarchy. FOAM allows the user to select the target search space before HMM-based comparison steps and to easily organize the results into different functional categories and subcategories. FOAM is publicly available at http://portal.nersc.gov/project/m1317/FOAM/.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1977

Thermal radiation from Titan's atmosphere

The temperature-inversion model of Titan's atmosphere is modified to include the thermal emission from a plausible amount of C2H2. Infrared photometry of Titan from 8 to 14 microns agrees with model calculations for 0.5 cm-atm C2H6 and 1.0 cm-atm C2H2 and an optically thin dust layer in a temperature-inversion region at 160 K. The random band approximation used to calculate C2H6 opacity has been verified by laboratory transmission measurements. Measurements of the C2H2 emission at 13.7 microns may provide an indirect check on the presence of H2 on Titan. Existing observations of Titan appear to be fully consistent with the inversion model if the surface radius equals 2700 km, the atmospheric radius equals 2900 km, and the surface temperature equals 78 K. The energy balance depends sensitively on these three parameters.

Caldwell, J.↗
DOE OSTI2015

Analytical evaluation of atomic form factors: Application to Rayleigh scattering

Atomic form factors are widely used for the characterization of targets and specimens, from crystallography to biology. By using recent mathematical results, here we derive an analytical expression for the atomic form factor within the independent particle model constructed from nonrelativistic screened hydrogenic wave functions. The range of validity of this analytical expression is checked by comparing the analytically obtained form factors with the ones obtained within the Hartee-Fock method. As an example, we apply our analytical expression for the atomic form factor to evaluate the differential cross section for Rayleigh scattering off neutral atoms.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2025

FY25 Theory and Simulation Performance Target: Development of an integrated modeling framework for fusion reactor design and assessment (Final Report)

This report documents the FY25 Theory and Simulation Performance Target (TSPT) of developing an integrated modeling framework for fusion reactor design and assessment (FREDA). Over Q1-Q4, new capabilities were developed across both plasma and engineering domains and demonstrated on an example representation of a Compact Advanced Tokamak with a Dual Cooled Lead Lithium blanket. This represents a first-of-a-kind demonstration of coupled core-to-wall-to-engineering for a reactor. Self-consistent CESOL workflows were applied to provide core, pedestal, and SOL prediction; new modules were developed for energetic particle stability (FAR3D) and transport (TGLF-EP) analysis; and boundary plasma modeling (SOLPS-ITER, BOUT++/Hermes-3) was expanded to evaluate wall and divertor heat fluxes and interface with engineering thermal analysis. A parameterized CAD tool, TRACER, was expanded to generate medium-fidelity divertor, blanket, and coil geometries; OpenFOAM and Diablo workflows were applied for first-wall and divertor thermal analyses with helium cooling; and reduced-order models were created for high-mass-flux divertor cooling. Magnet multiphysics capabilities were verified between Elmer, Diablo, and a new MFEM-based solver, and workflows enable stress, thermal, and neutron-fluence analysis of TF coils with neutronics-driven heating. Nuclear and blanket analysis workflows were demonstrated, including tritium breeding, transport, and CFD-informed thermo-mechanical assessment. Preliminary multi-fidelity uncertainty quantification workflows were applied to boundary modeling codes and shown to achieve variance reductions with fewer high-fidelity boundary simulations. Key findings highlight the challenges of resolving the ITEP gap to find suitable balance between wall and divertor loads, neutron heating, and practical limits of PFC cooling. Next step priorities are to develop automated workflows to check boundary code convergence and detachment, implement tighter physics-engineering CAD provenance tracking, and inclusion of plasma-material interface models for SLAG and tungsten cracking behavior. Collectively, these developments establish sophisticated capabilities for predictive, multi-fidelity, whole-device modeling that integrates plasma physics, materials, magnets, and nuclear engineering to guide pathways to viable Fusion Pilot Plant design points.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2026

Arm and shoulder muscle segmentation in axial MRI with UNet deep learning model

Quantifying individual upper-limb muscle volumes from MRI provides key insight into muscle-specific strength, deficits, and adaptations. Manual delineation is the gold standard but time‑intensive, and the performance of current deep learning approaches, particularly for small or anatomically complex muscles, remains incompletely characterized. We evaluated a state‑of‑the‑art deep learning framework across the entire upper limb and analyzed factors governing segmentation performance, with attention to the forearm. Three previously published MRI datasets (1.5 T, 3D GRE T1‑weighted; total n = 39) spanning young, middle‑aged, and older adults were curated and quality‑checked, including expert manual segmentations for 31 muscles. Following multiclass mask reconstruction, we trained three 3D nnU‑Net multiclass models matched to the muscle subsets present across datasets, using five‑fold cross‑validation and a composite Dice Similarity Coefficient (DSC) + cross entropy loss. Segmentation accuracy was assessed with DSC. Performance varied across muscles (mean DSC = 0.806 ± 0.098), ranging from 0.920 (Deltoid) to 0.461 (Extensor pollicis brevis). In uncertainty‑weighted regressions, muscle volume was positively associated with DSC (R2 = 0.36, p < 0.001), whereas training segmentation count and muscle orientation showed negligible associations (R2 ≤ 0.06). A weighted mixed‑effects model identified volume as the strongest evaluated predictor, explaining 23.9% of variance in DSC; orientation and training count each contributed <1%, leaving 61.5% unexplained. These results indicate that deep learning–based segmentation can accurately quantify muscle volume for many upper‑limb muscles but remains constrained for small, low‑contrast forearm muscles.

Gillespie, Samuel↗
DOE OSTI2024

How Do Climate Model Resolution and Atmospheric Moisture Affect the Simulation of Unprecedented Extreme Events Like the 2021 Western North American Heat Wave?

Abstract Although the 2021 Western North America (WNA) heat wave was predicted by weather forecast models, questions remain about whether such strong events can be simulated by global climate models (GCMs) at different model resolutions. Here, we analyze sets of GCM simulations including historical and future periods to check for the occurrence of similar events. High‐ and low‐resolution simulations both encounter challenges in reproducing events as extreme as the observed one, particularly under the present climate. Relatively stronger amplitudes are observed during the future periods. Furthermore, high‐ and low‐resolution short initialized GCM simulations are both able to reasonably predict such strong events and their associated high‐pressure ridge over the WNA with a 1 week forecast lead time. Moisture sensitivity experiments further indicate a drier atmospheric moisture condition results in substantially higher near‐surface temperatures in the simulated heat events.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

PolyID™ (Polymer Inverse Design) [SWR-21-98]

PolyID™ provides a framework for building, training, and predicting polymer properties using graph neural networks. The codes leverages Neural Fingerprint (nfp) (See DOE Code ID 22185) for building tensorflow-based message-passing neural networks, and Monomers to Polymers (m2p) (See DOE Code ID 44795) for building polymer structures. To understand and use the pipeline, the following examples have been provided: Building polymer structures: examples/example_generate_polymer_structures.ipynb Checking domain of validity: examples/example_determine_domain-of-validity.ipynb Training a graph neural network: examples/example_generate_and_train_models.ipynb Predicting with the trained model: examples/example_predict_with_trained_models.ipynb

Wilson, Nolan↗
DOE OSTI2016

Aerodynamic Drag Reduction Technologies Testing of Heavy-Duty Vocational Vehicles and a Dry Van Trailer

This study focused on two accepted methods for quantifying the benefit of aerodynamic improvement technologies on vocational vehicles: the coastdown technique, and on-road constant speed fuel economy measurements. Both techniques have their advantages. Coastdown tests are conducted over a wide range in speed and allow the rolling resistance and aerodynamic components of road load force to be separated. This in turn allows for the change in road load and fuel economy to be estimated at any speed, as well as over transient cycles. The on-road fuel economy measurements only supply one lumped result, applicable at the specific test speed, but are a direct measurement of fuel usage and are therefore used in this study as a check on the observed coastdown results. Resulting coefficients were then used to populate a vehicle model and simulate expected annual fuel savings over real-world vocational drive cycles.

30 DIRECT ENERGY CONVERSION↗
NASA NTRS1976

A low speed self streamlining wind tunnel

A two dimensional test section in a low speed wind tunnel is producing flow conditions free from wall interference. The test section has flexible top and bottom walls, and rigid sidewalls from which the models are mounted spanning the tunnel. All walls are unperforated, and the flexible walls are positioned by screw jacks. To eliminate wall interference, the wind tunnel itself supplies the information required in this streamlining process, when run with the model present. Measurements taken at the flexible walls are used by the tunnel computer to check wall contours. When the static pressure distribution in the test section along a contoured flexible wall matches that computed for an imaginary flow field passing over the outside of the same contour, the wall is a streamline in an infinite flow field and the test section flow is free from wall interference. A series of iterations brings the walls from straight to streamlines. Illustrative aerodynamic data is presented, taken on a bluff body and a lifting wing.

Goodyer, M. J.↗
NASA NTRS1987

Equatorial ozone profiles from the ground to 52 km during the Southern Hemisphere autumn

The results of an ozone measurement campaign conducted at Natal, Brazil from March 25 to April 15, 1985 are reported. Complete profiles of ozone, pressure, and temperature were obtained from the ground to 52 km, and all of the profiles correspond with site overpasses by ozone instruments on NASA and NOAA satellites. The profiles from this measurement series show reasonable agreement with established satellite climatologies. Stratospheric ozone variability was 2 percent or less during the 3 weeks of the measurement campaign, with stratospheric temperature and pressure variabilities half that amount. Low variability at a single location for this period implies comparable uniformity for ozone profiles over a large area around the measurement site. The auxiliary pressure and temperature profiles presented here allow the use of this data set as a transfer standard between satellite instruments with different fundamental ozone measurements. Finally, the low ozone variability in the stratosphere at Natal during this measurement series should provide an opportunity for high-quality intercomparisons of measured and modeled ozone concentrations in the equatorial stratosphere and lower mesosphere as well as a consistency check among satellite ozone measurements.

Barnes, Robert A.↗
NASA NTRS1998

The Conference Proceedings of the 1998 Air Transport Research Group (ATRG) of the WCTR Society

Contents include the following: Airport choice in a multiple airport region: an empirical analysis for the San Francisco bay area. Liberalization of the westeuropian aviation: choice of a new hub airport for an airline. Austin Bergstrom airport traffic control tower establishment of a major activity level tower. A study to optimize the environmental capacity of Amsterdam airport schiphol.Airport performance in stakeholder involvement and communication strategies: a comparison of major Australian and North American air carrier and general aviation airports. Airport planning and location.Location of international airport and regional development. A simulation technique for analysis of Brasilian airport passanger terminal building.Multimodal airport access in Japan. Planning surface access provision at major airports Airline economics and the inclusion of environmental costs on airport hub pricing: a theoretical analysis. Airport financing and user charge systems in the USA. Optimal demand for operating lease of aircraft. Aircraft leasing industry and social welfare.The development of performance indicators for airports: a management perspective. Study about operational effect of the "security check-in" implantation in Brasilian international airports.Austin Bergstrom west loop cable system.and Optimal resource allocation model for airport passanger terminals.

Oum, Tae Hoon↗
NASA NTRS2002

Coronal Element Abundances of the Post-Common Envelope Binary V471 Tauri with ASCA

We report on ASCA observations of the coronally active companion star in the post-common envelope binary V471 Tau. While it would be prudent to check the following results with grating spectroscopy, we find that a single-temperature plasma model does not fit the data. Two temperature models with variable abundances indicate that Fe is underabundant compared to the Hyades photospheric mean, whereas, the high first ionization potential element Ne is overabundant. This is indicative of the inverse first ionization effect, believed to result from the fractionation of ionized material by the magnetic field in the upper atmosphere of the star. Evolutionary calculations indicate that there should be no peculiar abundances on the companion star resulting from the common envelope epoch. Indeed, we find no evidence for peculiar abundances, although uncertainties are high.

Still, Martin↗
NASA NTRS2001

Consequences of contamination of the spacecraft environment: immunologic consequences

Long-term space voyages pose numerous known and unknown health hazards, to the human immune system. Well-studied clinical examples of secondary immunodeficiencies created on Earth, lead one to predict that the conditions of prolonged space flight would weaken the human immune responses that normally hold infection and cancer in check. From evidence gathered from humans flown for prolonged periods in space and from human models of space flight studied on Earth it is reasonable to suspect that space travelers to the planet Mars would experience a weakening of immunity. Subtle defects of immune cell structure and function have been observed in astronauts, such as weakening of specific T-lymphocyte recall of specific antigens. Ground-based models also have demonstrated alterations of immune function, such as the elevation of neuroendocrine immune system messengers, interleukin-6, and soluble tumor necrosis factor-alpha receptor in sleep deprivation. Since severe immune compromise the clinical consequences of reactivation of latent virus infections and the development of cancer, has yet to be seen in space flight or in the Earth models, it is extremely important to begin to quantify early changes in immunity to predict the development of immune system collapse with poor clinical outcomes. This approach is designed to validate a number of surrogate markers that will predict trouble ahead. Inherent in this research is the development of countermeasures to reduce the risks of infection and cancer in the first humans going to Mars.

Non-NASA Center↗
DOE OSTI2014

Estimation of k-ε parameters using surrogate models and jet-in-crossflow data

We demonstrate a Bayesian method that can be used to calibrate computationally expensive 3D RANS (Reynolds Av- eraged Navier Stokes) models with complex response surfaces. Such calibrations, conditioned on experimental data, can yield turbulence model parameters as probability density functions (PDF), concisely capturing the uncertainty in the parameter estimates. Methods such as Markov chain Monte Carlo (MCMC) estimate the PDF by sampling, with each sample requiring a run of the RANS model. Consequently a quick-running surrogate is used instead to the RANS simulator. The surrogate can be very difficult to design if the model's response i.e., the dependence of the calibration variable (the observable) on the parameter being estimated is complex. We show how the training data used to construct the surrogate can be employed to isolate a promising and physically realistic part of the parameter space, within which the response is well-behaved and easily modeled. We design a classifier, based on treed linear models, to model the "well-behaved region". This classifier serves as a prior in a Bayesian calibration study aimed at estimating 3 k - ε parameters ( C μ , C ε2 , C ε1 ) from experimental data of a transonic jet-in-crossflow interaction. The robustness of the calibration is investigated by checking its predictions of variables not included in the cal- ibration data. We also check the limit of applicability of the calibration by testing at off-calibration flow regimes. We find that calibration yield turbulence model parameters which predict the flowfield far better than when the nomi- nal values of the parameters are used. Substantial improvements are still obtained when we use the calibrated RANS model to predict jet-in-crossflow at Mach numbers and jet strengths quite different from those used to generate the ex- perimental (calibration) data. Thus the primary reason for poor predictive skill of RANS, when using nominal values of the turbulence model parameters, was parametric uncertainty, which was rectified by calibration. Post-calibration, the dominant contribution to model inaccuraries are due to the structural errors in RANS.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1993

Laboratory spectra of field samples as a check on two atmospheric correction methods

Atmospheric correction is the first step toward quantitative analysis of imaging spectroscopy data. Two methods, MODTRAN model and the empirical line, were used to convert AVIRIS radiance values to reflectance values. A set of laboratory spectra of field samples corresponding to AVIRIS coverage was used to assess these methods. This will also serve to select bands for future quantative analyses.

Xu, Pung↗
DOE OSTI2019

Deep‐learning‐based power distribution network switch action identification leveraging dynamic features of distributed energy resources

This study proposes a data‐driven approach for identifying switch actions in power distribution networks. Simulated micro‐phasor measurement unit data is utilised to train a convolutional neural network (CNN) model. The trained CNN model can identify multi‐phase multi‐switch actions. Instead of working as a blackbox, the proposed approach extracts the features from the hidden layers of the trained CNN for engineering interpretation and error check. In addition, a random‐forest‐based feature ranking algorithm is proposed to identify the most important features. The proposed approach is validated on the IEEE 123‐node feeder modelled in GridLAB‐D. The CNN model is built and trained using TensorFlow. The proposed approach achieves 96.57% identification accuracy.

42 ENGINEERING↗
NASA NTRS1947

Flight Tests of Rocket-Powered "Tin-Can" Models of AAF Project MX-800

Flight tests were made of six noninstrumented rocket-powered "Tin Can" models of AAF Project MX-800. Velocity and drag data were obtained by use of CU Doppler radar. The existence of stability and adequate structural strength for flight near zero lift was checked by visual and photographic observation. Drag data obtained during the tests agreed reasonably well with estimates based on experimental data from NACA RM-2 rocket-powered drag research models.

Purser, Paul E.↗