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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 199 records · Page 11
All sourcesNASA NTRSDOE OSTI
NASA NTRS2005

Dynamic Weakening (Extinction) of Simple Hydrocarbon-air Counterflow Diffusion Flames by Oscillatory Inflows

This study of laminar non-premixed HC-air flames used an Oscillatory-input Opposed Jet Burner (OOJB) system developed from a previously well-characterized 7.2-mm Pyrex-nozzle OJB system. Over 600 dynamic Flame Strength (FS) measurements were obtained on unanchored (free-floating) laminar Counterflow Diffusion Flames (CFDF's). Flames were stabilized using plug inflows having steady-plus-sinusoidal axial velocities of varied magnitude, frequency, f, up to 1600 Hz, and phase angle from 0 (most data) to 360 degrees. Dynamic FS is defined as the maximum average air input velocity (U(sub air), at nozzle exit) a CFDF can sustain before strain-induced extinction occurs due to prescribed oscillatory peak-to-peak velocity inputs superimposed on steady inputs. Initially, dynamic flame extinction data were obtained at low f, and were supported by 25-120 Hz Hot-Wire cold-flow velocity data at nozzle exits. Later, expanded extinction data were supported by 4-1600 Hz Probe Microphone (PM) pk/pk P data at nozzle exits. The PM data were first obtained without flows, and later with cold stagnating flows, which better represent speaker-diaphragm dynamics during runs. The PM approach enabled characterizations of Dynamic Flame Weakening (DFW) of CFDF's from 8 to 1600 Hz. DFW was defined as % decrease in FS per Pascal of pk/pk P oscillation, namely, DFW = - 100 d(U(sub air) / U(sub air),0Hz) / d(pkpk P). The linear normalization with respect to acoustic pressure magnitude (and steady state (SS) FS) led to a DFW unaffected by strong internal resonances. For the C2H4/N2-air system, from 8 to 20 Hz, DFW is constant at 8.52 plus or minus 0.20 (% weakening)/Pa. This reflects a quasi-steady flame response to an acoustically induced dU(sub air)/dP. Also, it is surprisingly independent of C2H4/N2 mole fraction due to normalization by SS FS. From 20 to approximately 150 Hz, the C2H4/N2 air-flames weakened progressively less, with an inflection at approximately 70 Hz, and became asymptotically insensitive (DFW approximately 0) at approximately 300 Hz, which continued to 1600 Hz. The DFW of CH4-air flames followed a similar pattern, but showed much greater weakening than C2H4/N2-air flames; i.e., the quasi-steady DFW (8 to approximately 15 Hz) was 44.3 %/Pa, or approximately 5x larger, even though the 0 Hz (SS) FS was only 3.0 x smaller. The quasi-steady DFW's of C3H8-air and C2H6-air were intermediate at 34.8 and 20.9 %Pa, respectively. The DFW profiles of all four fuels, at various frequencies, correlated well but non-linearly with respective SS FS's. Notably, the DFW profile for C3H8 air fell more rapidly in the range greater than 15 to 60 Hz, compared with the 1- and 2-carbon fuels. This may indicate a shift in chemical kinetics, and/or O2 transport to a flame that moved closer to the fuel-side. In conclusion, Dynamic Flame Weakening limits appear significant and unique for each fuel, and correlate closely, but non-linearly, with Steady-State Flame Strengths at any given frequency. For reasons unknown, the dynamic flames didn't weaken more at intermediate frequencies (e.g., at 20-50 Hz) than they did at low frequencies (less than 15 Hz), where quasi-steady weakening appears to dominate. Quasi-steady flame weakening ostensibly represents a transient input strain rate maximum that just exceeds the steady-state strain-rate-limited extinction limit for a few cycles. Clearly, further detailed mechanistic understanding is needed in the fall-off region.

Pellett, G.↗
DOE OSTI2025

Confusion-Driven Machine Learning of Structural Phases of a Flexible, Magnetic Stockmayer Polymer

We use a semisupervised, neural-network-based machine learning technique, the confusion method, to investigate structural transitions in magnetic polymers, which we model as chains of magnetic colloidal nanoparticles characterized by dipole–dipole and Lennard-Jones interactions. As input for the neural network, we use the particle positions and magnetic dipole moments of equilibrium polymer configurations, which we generate via replica-exchange Wang–Landau simulations. We demonstrate that by measuring the classification accuracy of neural networks, we can effectively identify transition points between multiple structural phases without any prior knowledge of their existence or location. We corroborate our findings by investigating relevant conventional order parameters. Our study furthermore examines previously unexplored low-temperature regions of the phase diagram, where we find new structural transitions between highly ordered helicoidal polymer configurations.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Chemical signature characterization with hyperspectral imagery: novel deep learning model architectures and physically-motivated data augmentation techniques

The high spectral resolution afforded by Hyperspectral Imaging (HSI) sensors is poised to bring unprecedented advancements to signature characterization applications. Thus far, much of the research in the machine learning field devoted to HSI applications has focused on a few specific tasks like land-use land-cover classification. In land classification tasks, spatial information is very important, and model architectures are often designed to leverage spatial contexts. However, it is unclear how well these spatially-tuned models will translate to tasks where spectral information is critical, like the detection and characterization of chemicals. In this work, we compare spectral models (inputs are 1D spectra) and spatial-spectral models (inputs are 3D cubes) in the context of predicting chemical concentration maps. We find that spatial-spectral models perform the best, though we find a wide range in performance across the different architectures tested. Additionally, we find that model performance is impacted by the availability of training data, particularly in scenarios where the training data doesn't fully capture the true variance of real-world conditions. We find that data augmentation can help mitigate sparse coverage of observed parameter space (e.g., seasonal or geographic variability in ground cover), and present augmentation strategies that are tailored to hyperspectral data.

• Artificial intelligence (AI) / machine learning ↗
DOE OSTI2024

Polarimetry-Enhanced Imaging towards Autonomous Solar Field and Receiver Inspections

During the typical operation of a Concentrating Solar Power (CSP) plant, a large portion of the energy (~45%) can be lost due to various imperfect conditions, such as blocking, shading, mirror soiling, tracking and canting errors, etc. It is necessary to develop efficient and effective field inspection technology to optically survey and characterize a CSP field, which can be used as an input for autonomous control, maintenance scheduling and spot repair whenever necessary to maximize the overall efficiency of the plant. In this project, We aim to apply polarimetric imaging for CSP collector and receiver inspection and develop polarimetric drone cameras (via integrating polarimetric imagers onto drones) for autonomous field inspection in CSP plants.

14 SOLAR ENERGY↗
NASA NTRS1979

A multiloop generalization of the circle criterion for stability margin analysis

In order to provide a theoretical tool suited for characterizing the stability margins of multiloop feedback systems, multiloop input-output stability results generalizing the circle stability criterion are considered. Generalized conic sectors with 'centers' and 'radii' determined by linear dynamical operators are employed to specify the stability margins as a frequency dependent convex set of modeling errors (including nonlinearities, gain variations and phase variations) which the system must be able to tolerate in each feedback loop without instability. The resulting stability criterion gives sufficient conditions for closed loop stability in the presence of frequency dependent modeling errors, even when the modeling errors occur simultaneously in all loops. The stability conditions yield an easily interpreted scalar measure of the amount by which a multiloop system exceeds, or falls short of, its stability margin specifications.

Safonov, M. G.↗
NASA NTRS1984

The thermosphere of Titan

The vertical structure of Titan's thermosphere is calculated down to the mesopause as a function of local time based on Voyager 1 occultation data. The thermal time scales that characterize the diurnal behavior of the thermosphere are discussed, the input model atmosphere used to calculate the temperature profile is presented, and the dominant heating and cooling mechanisms in the thermosphere are examined. The temperature profiles obtained by integrating the heat transfer equation with and without electron heating are presented and discussed. The implications that derived exospheric temperatures have for the neutral hydrogen torus are investigated. The diurnal exospheric temperature is unlikely to exceed 225 K, averages between 187 and 197 K, and has a variational amplitude of 28 K or less. The vertical extent of the hydrogen cloud is too large to be explained in terms of simple thermal escape of hydrogen from the exosphere.

Friedson, A. J.↗
NASA NTRS1985

Sensory and motor properties of the cerebellar uvula and modulus

The uvula and nodulus (vermal lobules 9 and 10) of the vestibulocerebellum are implicated by behavioral evidence in the control of eye and head movements and in the production of motion sickness. The uvula and nodulus could play a role in these functions through known output pathways. Purkinje cells in both structures project via the fastigial and vestibular nuceli to the ventral horn of the cervical spin cord, to oculomotor neurons, and to the emetic region of the reticular formation (ablation of which abolishes susceptability to motion sickness). Uvula and nodulus Purkinje cells will be analyzed in cats trained to make controlled head movements. The activity of these neurons is expected to modulate well during head and/or eye movements because the uvula and nodulus receive heavy projections from sources of visual, vestibular and neck proprioceptive information. How neuron activity contributes to movement and how different sensory inputs converge to influence this contribution may be determined by characterizing movement related properties of these neurons. A population of neurons that modulates powerfully to the conflict between different head movement signals that can cause motion sickness may be identified.

Robinson, F. R.↗
NASA NTRS1989

On eigensystem assignment with output feedback

A new approach for the eigenvalue assignment of linear, first-order, time-invariant systems using output feedback is developed. The approach can assign the maximum allowable number of closed-loop eigenvalues through output feedback provided that the system is fully controllable and observable, and both the input influence and output influence matrices are full rank. First, a collection of bases for the space of attainable closed-loop eigenvectors is generated using the Singular Value Decomposition or QR Decomposition techniques. Then, an algorithm based on subspace intersections is developed and used to compute the corresponding coefficients of the bases, and the required output feedback gain matrix. Moreover, the additional freedom provided by the multi-inputs and multi-outputs beyond the eigenvalue assignment is characterized for possible exploitation. A numerical example is given to demonstrate the viability of the proposed approach.

Maghami, Peiman G.↗
NASA NTRS1994

Active antenna

An antenna, which may be a search coil, is connected to an operational amplifier circuit which provides negative impedances, each of which is in the order of magnitude of the positive impedances which characterize the antenna. The antenna is connected to the inverting input of the operational amplifier; a resistor is connected between the inverting input and the output of the operational amplifier; a capacitor-resistor network, in parallel, is connected between the output and the noninverting input of the operational amplifier; and a resistor is connected from the noninverting input and the circuit common. While this circuit provides a negative resistance and a negative inductance, in series, which appear, looking into the noninverting input of the operational amplifier, in parallel with the antenna, these negative impedances appear in a series loop with the antenna positive impedances, so as to algebraically add. This circuit is tuned by varying the various circuit components so that the negative impedances are very close, but somewhat less, in magnitude, to the antenna impedances. The result is to increase the sensitivity of the antenna by lowering its effective impedance. This, in turn, increases the effective area of the antenna, which may be broadband.

Sutton, John F.↗
NASA NTRS2011

Inflatable Re-entry Vehicle Experiment (IRVE-4) Overview

The suite of Inflatable Re-Entry Vehicle Experiments (IRVE) is designed to further our knowledge and understanding of Hypersonic Inflatable Aerodynamic Decelerators (HIADs). Before infusion into a future mission, three challenges need to be addressed: surviving the heat pulse during re-entry, demonstrating system performance at relevant scales, and demonstrating controllability in the atmosphere. IRVE-4 will contribute to a better understanding of controllability by characterizing how a HIAD responds to a set of controlled inputs. The ability to control a HIAD is vital for missions that are g-limited, require precision targeting and guidance for aerocapture or entry, descent, and landing. The IRVE-4 flight test will focus on taking a first look into controlling a HIAD. This paper will give an overview of the IRVE-4 mission including the control response portion of the flight test sequence, and will provide a review of the mission s development.

Litton, Daniel K.↗
NASA NTRS2019

Anthropogenic Nitrogen Inputs and Impacts on Oceanic N2O Fluxes in the Northern Indian Ocean: the Need for an Integrated Observation and Modelling Approach

Anthropogenically-derived nitrogen input to the northern Indian Ocean has increased significantly in recent decades, based on both observational and model derived estimates This external nutrient source is supplied by atmospheric deposition and riverine fluxes, and has the potential to affect the vulnerable biogeochemical systems of the Arabian Sea and Bay of Bengal, influencing productivity and oceanic production of the greenhouse-gas nitrous-oxide (N2O). We summarize current estimates of this external nitrogen source to the northern Indian Ocean from observations and models, highlight implications for regional marine N2O emissions using model-based analyses, and make recommendations for measurement and model needs to improve current estimates and future predictions of this impact. Current observationally-derived estimates of deposition and riverine nitrogen inputs are limited by sparse measurements and uncertainties on accurate characterization of nitrogen species composition. Ocean model assessments of the impact of external nitrogen sources on regional marine N2O production in the northern Indian Ocean estimate potentially significant changes but also have large associated uncertainties. We recommend an integrated program of basin-wide measurements combined with high-resolution modeling and more detailed characterization of nitrogen-cycle process to address these uncertainties and improve current estimates and predictions.

Parvadha Suntharalingam↗
NASA NTRS2019

AladynPi – Adaptive Neural Network Molecular Dynamics Simulation Code with Physically Informed Potential: Computational Materials Mini-Application

This report provides an overview and description of commands used in the Computational Materials mini-application, AladynPi. AladynPi is an extension of a previously released mini-application, Aladyn (https://github.com/nasa/aladyn; Yamakov, V.I., and Glaessgen, E.H., NASA/TM-2018-220104). Aladyn and AladynPi are basic molecular dynamics codes written in FORTRAN 2003, which are designed to demonstrate the use of adaptive neural networks (ANNs) in atomistic simulations. The role of ANNs is to efficiently reproduce the very complex energy landscape resulting from the atomic interactions in materials with the accuracy of the more expensive quantum mechanics-based calculations. The ANN is trained on a large set of atomic structures calculated using the density functional theory method. An input for the ANN is a set of structure coefficients, characterizing the local atomic environment of each atom, for which the atomic energy is obtained in the ANN inference process. In Aladyn, the ANN gives directly the energy of interatomic interactions. In AladynPi, the ANN gives optimized parameters for a predefined empirical function, known as bond-order-potential (BOP). The parameterized BOP function is then used to calculate the energy. AladynPi code is being released to serve as a training testbed for students and professors in academia to explore possible optimization algorithms for parallel computing on multicore central processing unit (CPU) computers or computers utilizing manycore architectures based on graphic processing units (GPUs). The effort is supported by the High Performance Computing incubator (HPCi) project at NASA Langley Research Center.

Yamakov, Vesselin I.↗
NASA NTRS2019

Advancements in the Aerosol Robotic Network (AERONET) Version 3 database – automated near-real-time quality control algorithm with improved cloud screening for Sun photometer aerosol optical depth (AOD) measurements

The Aerosol Robotic Network (AERONET) has provided highly accurate, ground-truth measurements of the aerosol optical depth (AOD) using Cimel Electronique Sun–sky radiometers for more than 25 years. In Version 2 (V2) of the AERONET database, the near-real-time AOD was semiautomatically quality controlled utilizing mainly cloud-screening methodology, while additional AOD data contaminated by clouds or affected by instrument anomalies were removed manually before attaining quality-assured status (Level 2.0). The large growth in the number of AERONET sites over the past 25 years resulted in significant burden to the manual quality control of millions of measurements in a consistent manner. The AERONET Version 3 (V3) algorithm provides fully automatic cloud screening and instrument anomaly quality controls. All of these new algorithm updates apply to near-real-time data as well as post-field-deployment processed data, and AERONET reprocessed the database in 2018. A full algorithm redevelopment provided the opportunity to improve data inputs and corrections such as unique filter-specific temperature characterizations for all visible and near-infrared wavelengths, updated gaseous and water vapor absorption coefficients, and ancillary data sets. The Level 2.0 AOD quality-assured data set is now available within a month after post-field calibration, reducing the lag time from up to several months. Near-real-time estimated uncertainty is determined using data qualified as V3 Level 2.0 AOD and considering the difference between the AOD computed with the pre-field calibration and AOD computed with pre-field and post-field calibration. This assessment provides a near-real-time uncertainty estimate for which average differences of AOD suggest a +0.02 bias and one sigma uncertainty of 0.02, spectrally, but the bias and uncertainty can be significantly larger for specific instrument deployments. Long-term monthly averages analyzed for the entire V3 and V2 databases produced average differences (V3–V2) of +0.002 with a ±0.02 SD (standard deviation), yet monthly averages calculated using time-matched observations in both databases were analyzed to compute an average difference of −0.002 with a ±0.004 SD. The high statistical agreement in multiyear monthly averaged AOD validates the advanced automatic data quality control algorithms and suggests that migrating research to the V3 database will corroborate most V2 research conclusions and likely lead to more accurate results in some cases.

David M. Giles↗
NASA NTRS2011

Coherent Evaluation of Aerosol Data Products from Multiple Satellite Sensors

Aerosol retrieval from satellite has practically become routine, especially during the last decade. However, there is often disagreement between similar aerosol parameters retrieved from different sensors, thereby leaving users confused as to which sensors to trust for answering important science questions about the distribution, properties, and impacts of aerosols. As long as there is no consensus, and the inconsistencies are not well characterized and understood, there will be no way of developing reliable model inputs and climate data records from satellite aerosol measurements. Fortunately, the Aerosol Robotic Network (AERONET) is providing well-calibrated globally representative ground-based aerosol measurements corresponding to the satellite-retrieved products. Through a recently developed web-based Multi-sensor Aerosol Products Sampling System (MAPSS), we are utilizing the advantages offered by collocated AERONET and satellite products to characterize and evaluate aerosol retrieval from multiple sensors. Indeed, MAPSS and its companion statistical tool AeroStat are facilitating detailed comparative uncertainty analysis of satellite aerosol measurements from Terra-MODIS, Aqua-MODIS, Terra-MISR, Aura-OMI, Parasol-POLDER, and Calipso-CALIOP. In this presentation, we will describe the strategy of the MAPSS system, its potential advantages for the aerosol community, and the preliminary results of an integrated comparative uncertainly analysis of aerosol products from multiple satellite sensors.

Ichoku, Charles↗
NASA NTRS2006

Automated Knowledge Discovery from Simulators

In this paper, we explore one aspect of knowledge discovery from simulators, the landscape characterization problem, where the aim is to identify regions in the input/ parameter/model space that lead to a particular output behavior. Large-scale numerical simulators are in widespread use by scientists and engineers across a range of government agencies, academia, and industry; in many cases, simulators provide the only means to examine processes that are infeasible or impossible to study otherwise. However, the cost of simulation studies can be quite high, both in terms of the time and computational resources required to conduct the trials and the manpower needed to sift through the resulting output. Thus, there is strong motivation to develop automated methods that enable more efficient knowledge extraction.

landscapes↗
DOE OSTI2026

Xanthos-Lake Dataset

The Xanthos-Lake v1.0 dataset provides the input data, trained machine-learning models, and simulation outputs needed to characterize lake water balance, snow and ice conditions, and mixing-layer temperature within the Xanthos global hydrological modeling framework. The dataset supports lake representation across a wide range of lake sizes and hydroclimatic conditions by combining xLSIM, a basin-specific machine-learning emulator of lake snow, ice, ice-cover fraction, and mixing-layer temperature, with the Xanthos-Lake water-balance model. The archive contains NetCDF datasets used to train and evaluate xLSIM, trained model weights, processed meteorological and lake-property inputs, and basin- and lake-category-specific simulation outputs. These materials are organized into four primary data groups, described below. Snowice_model_inputs: Contains the NetCDF input data used to train xLSIM. The xLSIM machine-learning framework uses three lake-based datasets. The meteorological forcing dataset provides monthly relative humidity, specific humidity, surface wind speed, maximum and minimum air temperature, downward longwave and shortwave radiation, snowfall, surface air pressure, and total precipitation. Lake surface area is included as an additional static predictor. The target-state dataset provides lake ice thickness, snow depth, snow cover, and lake mixing-layer temperature, while a companion lake-surface dataset provides the lake ice-cover fraction. Before training, ice thickness and snow depth are converted from meters to centimeters, mixing-layer temperature is converted from kelvin to degrees Celsius and constrained to nonnegative values, and ice-cover fraction is converted from a fraction to a percentage. The predictor variables are normalized using statistics calculated across the selected lakes and time steps. Snowice_model_outputs: Contains the NetCDF outputs generated by xLSIM. For each basin, xLSIM produces a file containing observed and predicted lake-state variables for the training, validation, and testing periods. The modeled variables include lake ice thickness, snow depth, snow cover, mixing-layer temperature, and lake ice-cover fraction. For basins without a sufficiently persistent snow-and-ice signal, the emulator predicts only mixing-layer temperature. The outputs also include training and validation loss histories, the selected model configuration, identifiers of the lakes used in training, and SHAP-based feature-importance information at the global, lake, and seasonal-regime levels. The trained machine-learning model weights are provided separately within the dataset archive. Together, these files support model evaluation and subsequent coupling with the Xanthos-Lake water-balance framework. XanthosLAKES: Contains the NetCDF input data used by the Xanthos-Lake framework. Monthly meteorological inputs include relative and specific humidity, downward shortwave and longwave radiation, mean, maximum, and minimum air temperature, wind speed, precipitation, snowfall, and surface air pressure. Static lake-property datasets provide lake identifiers, geographic locations, surface area, volume, mean depth, elevation, drainage area, fetch, outlet-routing information, and associated Xanthos grid-cell attributes. Separate bathymetric datasets provide the coefficients of the area–depth and volume–depth relationships for each aggregated lake unit. GLEV-based records provide observed lake surface area and evaporation data used to initialize lake states, define reference conditions, and calibrate and evaluate the model. Xanthos-Lake Outputs: Contains the basin- and lake-category-specific NetCDF outputs generated by Xanthos-Lake. Monthly variables include lake surface area, storage volume, outlet discharge, evaporation rate, evaporation volume, lake–groundwater exchange, lake inflow, ice thickness, snow depth, snow-cover fraction, ice-cover fraction, and mixing-layer temperature. The files also contain lake-specific calibration and validation statistics, including normalized root-mean-square error, mean absolute error, Nash–Sutcliffe efficiency, Kling–Gupta efficiency, and percent bias. Stored calibrated and derived parameters include the weir discharge coefficient, fractional freeboard, groundwater exchange coefficient, reference water level, corresponding reference surface area and storage volume, weir-width adjustment factor, and the fraction of routed inflow entering the lake. Basin identifiers, lake category, simulation period, calibration and validation periods, and parameter-schema information are retained as NetCDF metadata.

Abeshu, Guta [Pacific Northwest National Laborator↗
NASA NTRS2015

Lower Limits on Aperture Size for an ExoEarth Detecting Coronagraphic Mission

The yield of Earth-like planets will likely be a primary science metric for future space-based missions that will drive telescope aperture size. Maximizing the exoEarth candidate yield is therefore critical to minimizing the required aperture. Here we describe a method for exoEarth candidate yield maximization that simultaneously optimizes, for the first time, the targets chosen for observation, the number of visits to each target, the delay time between visits, and the exposure time of every observation. This code calculates both the detection time and multiwavelength spectral characterization time required for planets. We also refine the astrophysical assumptions used as inputs to these calculations, relying on published estimates of planetary occurrence rates as well as theoretical and observational constraints on terrestrial planet sizes and classical habitable zones. Given these astrophysical assumptions, optimistic telescope and instrument assumptions, and our new completeness code that produces the highest yields to date, we suggest lower limits on the aperture size required to detect and characterize a statistically motivated sample of exoEarths.

Earth-like planets↗
DOE OSTI2026

Evaluation of the Radiological Characterization for Off-Site Source Recovery Program Waste Streams LA-OS-00-01.001, LA-OS-00-03, and LA-OS-00-04

The purpose of this memorandum is to satisfy the requirements of Section 4.4 of the CCP Acceptable Knowledge Documentation procedure CCP-TP-005 (Ref. 1). This evaluation is updating the previously issued memo NEN3:24-045 issued in August 2024. CCP-TP-005 requires an AK Expert and the OSRP group to evaluate the radionuclide characterization of a waste stream and prepare the NDA Memorandum (letter to CCP Records). This NDA memorandum was written with input from the OSRP group, as required by CCP-TP-005. This memo includes a discussion of the limitations for the radiological characterization and a description of the characterization method.

07 ISOTOPE AND RADIATION SOURCES↗