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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 415 records · Page 23
All sourcesNASA NTRSDOE OSTI
NASA NTRS2014

Airspace Safety Threshold Study: Status Update to SC-228 DAA Safety Sub-Group

Realization of the expected proliferation of Unmanned Aircraft System (UAS) operations in the National Airspace System (NAS) depends on the development and validation of performance standards for UAS Detect and Avoid (DAA) Systems. The RTCA Special Committee 228 is charged with leading the development of draft Minimum Operational Performance Standards (MOPS) for UAS DAA Systems. NASA, as a participating member of RTCA SC-228, is committed to supporting the development and validation of draft requirements as well as the safety substantiation and end-to-end assessment of DAA system performance. A recent study conducted using NASA's ACES (Airspace Concept Evaluation System) simulation capability begins to address questions surrounding the development of draft MOPS for DAA systems. ACES analyses were conducted to determine: 1) the rate at which IFR aircraft encounter other IFR and VFR aircraft, and 2) the rate at which UAS aircraft encounter VFR aircraft. Five different separation thresholds were used (two for encounter and one each for well-clear, near mid-air collision, and closest point of approach). The results will be used by SC228 to inform decisions about the safety aspect of UAS DAA systems and future requirements development and validation efforts.

Risk ratio↗
NASA NTRS2021

Optimal Experimental Design With Fast Neural Network Surrogate Models

Designing optimal experiments minimizes the uncertainty of results and maximizes the efficient use of resources. Herein, machine learning surrogate models and the approximate coordinate exchange (ACE) algorithm are used to determine optimum experimental designs over large or arbitrarily restrictive design spaces. Optimal experimental design is particularly salient in materials science where experiments are expensive and material properties must often be inferred indirectly. The proposed framework is demonstrated by finding optimal experiments with which the hidden constituent properties of composite materials can be most efficiently inferred from observable experimental outcomes. The optimum experimental design is given by an information-theoretic criteria, which maximizes the conditional mutual information between the hidden properties and the expected experimental outcomes. To perform tractable optimization a neural network is trained as a surrogate model to mimic a physics based simulation, which can calculate the expected experimental outcome based on a candidate experimental design and sampled constituent properties. The ACE algorithm is used to optimize over large design spaces with many tests and controlled parameters where an exhaustive search would be intractable even with the surrogate model. Using this approach, optimal experimental designs that are consistent with those produced by heuristic knowledge and established best practices are found; then optimal designs in larger design spaces where heuristic knowledge is unavailable are examined.

machine learning↗
NASA NTRS2020

Revisiting the Relationship between Atlantic Dust and Tropical Cyclone Activity using Aerosol Optical Depth Reanalyses: 2003-2018

Previous studies have noted a relationship between African dust and Atlantic tropical cyclone (TC) activity. However, due to the limitations of past dust analyses, the strength of this relationship remains uncertain. The emergence of aerosol reanalyses, including the Navy Aerosol Analysis and Prediction System (NAAPS) aerosol optical depth (AOD) reanalysis, NASA Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2), and ECMWF Copernicus Atmosphere Monitoring Service reanalysis (CAMSRA), enables an investigation of the relationship between African dust and TC activity over the tropical Atlantic and Caribbean in a consistent temporal and spatial manner for 2003–2018. Although June–July–August (JJA) 550 nm dust AOD (DAOD) from all three reanalysis products correlates significantly over the tropical Atlantic and Caribbean, the difference in DAOD magnitude between products can be as large as 60 % over the Caribbean and 20 % over the tropical North Atlantic. Based on the three individual reanalyses, we have created an aerosol multi-reanalysis consensus (MRC). The MRC presents overall better root mean square error over the tropical Atlantic and Caribbean compared to individual reanalyses when verified with ground-based AErosol RObotic NETwork (AERONET) AOD measurements. Each of the three individual reanalyses and the MRC have significant negative correlations between JJA Caribbean DAOD and seasonal Atlantic accumulated cyclone energy (ACE), while the correlation between JJA tropical North Atlantic DAOD and seasonal ACE is weaker. Possible reasons for this regional difference are provided. A composite analysis of 3 high-JJA-Caribbean-DAOD years versus 3 low-JJA-Caribbean-DAOD years reveals large differences in overall Atlantic TC activity. We also show that JJA Caribbean DAOD is significantly correlated with large-scale fields associated with variability in interannual Atlantic TC activity including zonal wind shear, mid-level moisture, and sea surface temperature (SST), as well as the El Niño–Southern Oscillation (ENSO) and the Atlantic Meridional Mode (AMM), implying confounding effects of these factors on the dust–TC relationship. We find that seasonal Atlantic DAOD and the AMM, the leading mode of coupled Atlantic variability, are inversely related and intertwined in the dust–TC relationship. Overall, DAOD in both the tropical Atlantic and Caribbean is negatively correlated with Atlantic hurricane frequency and intensity, with stronger correlations in the Caribbean than farther east in the tropical North Atlantic.

Dust particles↗
NASA NTRSDate not supplied

Building Collaborative Proving Grounds for R2O2R – NASA/CCMC - NOAA/Space Weather Prediction Testbed (SWPT) Partnership

In 2016 and 2019, Executive Orders were signed by the President to prepare the Nation for Space Weather Events. At the forefront of being able to achieve this goal is the necessity to accelerate and enhance R2O2R. To drive this initiative, the Space Weather R2O2R Framework, initially led by NOAA and NASA, was developed. At the very center of the framework is the Space Weather Proving Grounds effort that will allow various institutions to collaborate, share, and advance their space weather modeling and simulation capabilities. The first of these Proving Grounds is the Architecture for Collaborative Evaluation (ACE) environment shared between the Community Coordinated Modeling Center (CCMC) at NASA and the Space Weather Prediction Testbed (SWPT) at NOAA. Since 2019, the ACE environment has been set up on the AWS cloud where it utilizes AWS cloud services and Docker containerization technology.

T. Tsui↗
NASA NTRS2022

Regression Bias in using Solar Wind Measurements

Simultaneous solar wind measurements from the solar wind monitors, WIND and ACE, differ due to the spatial and temporal structure of the solar wind. Correlation studies that use these measurements as input may infer an incorrect correlation due to uncertainties arising from this spatial and temporal structure, especially at extreme and rare solar wind values. In particular, regression analysis will lead to a regression function whose slope is biased towards the mean value of the measurement parameter. This article demonstrates this regression bias by comparing simultaneous ACE and WIND solar wind measurements. A non-linear regression analysis between them leads to a perception of underestimation of extreme values of one measurement on average over the other. Using numerical experiments, we show that popular regression analysis techniques such as linear least-squares, orthogonal least-squares, and non-linear regression are not immune to this bias. Hence while using solar wind parameters as an independent variable in a correlation or regression analysis, random uncertainty in the independent variable can create unintended biases in the response of the dependent variable. More generally, the regression to the mean effect can impact both event-based, statistical studies of magnetospheric response to solar wind forcing.

magnetosphere↗
NASA NTRS2023

Probabilistic Forecasting of Ground Magnetic Perturbation Spikes at Mid-Latitude Stations

The prediction of large fluctuations in the ground magnetic field (dB/dt) is essential for preventing damage from Geomagnetically Induced Currents. Directly forecasting these fluctuations has proven difficult, but accurately determining the risk of extreme events can allow for the worst of the damage to be prevented. Here we trained Convolutional Neural Network models for eight mid-latitude magnetometers to predict the probability that dB/dt will exceed the 99th percentile threshold 30–60 min in the future. Two model frameworks were compared, a model trained using solar wind data from the Advanced Composition Explorer (ACE) satellite, and another model trained on both ACE and SuperMAG ground magnetometer data. The models were compared to examine if the addition of current ground magnetometer data significantly improved the forecasts of dB/dt in the future prediction window. A bootstrapping method was employed using a random split of the training and validation data to provide a measure of uncertainty in model predictions. The models were evaluated on the ground truth data during eight geomagnetic storms and a suite of evaluation metrics are presented. The models were also compared to a persistence model to ensure that the model using both datasets did not over-rely on dB/dt values in making its predictions. Overall, we find that the models using both the solar wind and ground magnetometer data had better metric scores than the solar wind only and persistence models, and was able to capture more spatially localized variations in the dB/dt threshold crossings.

Michael Coughlan↗
NASA NTRSDate not supplied

Nitric Oxide Planar Laser Induced Fluorescence Rotational Thermometry Characterization of A Hypersonic Boundary Layer

Nitric oxide (NO) planar laser-induced fluorescence (PLIF) was performed to determine rotational temperature profiles and fluctuations within a hypersonic boundary layer above the surface of a 2.75° half-angle wedge. The experiments were performed in the Texas A&M University Actively Controlled Expansion (ACE) hypersonic blow-down wind tunnel at Mach = 5.7 and 𝑅𝑒 = 6 × 10 6 /m. The NO was introduced to the flow in the settling chamber of the ACE tunnel and probed using two laser sheets near 226 nm. The resulting NO PLIF fluorescence signal was acquired using in-house software, which simultaneously recorded tunnel conditions. After a Fast Fourier Transform (FFT) blurring and statistical treatment was performed, the preshock temperature was evaluated to be 58 ± 2 K (3.5%), while the turbulent boundary layer temperature near the wall was found to be 350 K with fluctuations on the order of ±25 K (7%). The relative temperature fluctuations were determined to be 3 − 5% in the freestream and peaked at 33% in the turbulent boundary layer. The advantages and disadvantages of seeding NO in the tunnel settling chamber for thermometric PLIF measurements are discussed.

Hypersonics↗
NASA NTRSDate not supplied

NO PLIF Rotational Thermometry Characterization of a Hypersonic Boundary Layer

Nitric oxide (NO) planar laser-induced fluorescence (PLIF) was performed to determine rotational temperature profiles and fluctuations within a hypersonic boundary layer above the surface of a 2.75° half-angle wedge. The experiments were performed in the Texas A&M University Actively Controlled Expansion (ACE) hypersonic blow-down wind tunnel at Mach = 5.7 and 𝑅𝑒 = 6 × 10 6 /m. The NO was introduced to the flow in the settling chamber of the ACE tunnel and probed using two laser sheets near 226 nm. The resulting NO PLIF fluorescence signal was acquired using in-house software, which simultaneously recorded tunnel conditions. After a Fast Fourier Transform (FFT) blurring and statistical treatment was performed, the preshock temperature was evaluated to be 58 ± 2 K (3.5%), while the turbulent boundary layer temperature near the wall was found to be 350 K with fluctuations on the order of ±25 K (7%). The relative temperature fluctuations were determined to be 3 − 5% in the freestream and peaked at 33% in the turbulent boundary layer. The advantages and disadvantages of seeding NO in the tunnel settling chamber for thermometric PLIF measurements are discussed.

Hypersonics↗
NASA NTRSDate not supplied

A Path to Improving Simulated Properties of Low Clouds over the Beaufort Sea using Airborne In Situ Observations of Subgrid-Scale Variability

Arctic low clouds influence the evolution of the Arctic system through their effects on radiative fluxes, boundary layer mixing, stability, turbulence, humidity, and precipitation. Unfortunately, atmospheric models and retrospective analysis (reanalysis) products struggle to accurately simulate the occurrence and properties of low clouds in the Arctic. One of the main reasons for this problem are the possible unrealistic assumptions that models/reanalyses make about the subgrid-scale (SGS) variability of meteorological properties, as well as the relationship between SGS variability and grid-scale (GS) cloud properties. We utilize cloud and thermodynamic data of low level (primarily) liquid clouds collected from two aircraft campaigns conducted over the Beaufort Sea to better understand and characterize this problem. Examining data from the September 2014 Arctic Radiation-IceBridge Sea and Ice Experiment (ARISE) airborne campaign and the 1998 First International Satellite Cloud Climatology Project (ISCCP) Regional Experiment (FIRE)–Arctic Cloud Experiment (ACE) reveals that GS cloud water variability is closely related with SGS distribution of total water (i.e. water vapor + cloud water). We examine the influence of three assumed SGS parameters on the estimation of GS cloud water: the width of the SGS PDF of total water (ΔQT), the shape of the SGS PDF, and the critical saturation ratio (Scrit), which partitions the SGS PDF into water that is available or not for conversion to cloud water. Both Scrit and ΔQT influence predicted GS cloud water strongly. ARISE and FIRE-ACE disagree somewhat in the details, but both campaigns support the possibility that more realistic representations of these two parameters may lead to more realistic GS cloud water. In particular, both parameters are sensitive to GS relative humidity, and so we examine the effects of allowing these quantities to vary as an empirically-derived linear function of GS relative humidity. In contrast, the shape of the PDF has little effect on the predicted cloud water – a surprising result that merits additional investigation.

J Brant Dodson↗
NASA NTRS2010

Real-Time On-Board Processing Validation of MSPI Ground Camera Images

The Earth Sciences Decadal Survey identifies a multiangle, multispectral, high-accuracy polarization imager as one requirement for the Aerosol-Cloud-Ecosystem (ACE) mission. JPL has been developing a Multiangle SpectroPolarimetric Imager (MSPI) as a candidate to fill this need. A key technology development needed for MSPI is on-board signal processing to calculate polarimetry data as imaged by each of the 9 cameras forming the instrument. With funding from NASA's Advanced Information Systems Technology (AIST) Program, JPL is solving the real-time data processing requirements to demonstrate, for the first time, how signal data at 95 Mbytes/sec over 16-channels for each of the 9 multiangle cameras in the spaceborne instrument can be reduced on-board to 0.45 Mbytes/sec. This will produce the intensity and polarization data needed to characterize aerosol and cloud microphysical properties. Using the Xilinx Virtex-5 FPGA including PowerPC440 processors we have implemented a least squares fitting algorithm that extracts intensity and polarimetric parameters in real-time, thereby substantially reducing the image data volume for spacecraft downlink without loss of science information.

Earth Sciences Decadal Survey↗
NASA NTRS2012

Asymmetric Magnetosphere Deformation Driven by Hot Flow Anomaly(ies)

We present a case study of a large deformation of the magnetopause on November 26, 2008. The investigation is based on observations of five THEMIS spacecraft located at the dawn flank in the magnetosphere and magnetosheath, on Cluster measurements at the dusk magnetosheath, and is supported by ACE solar wind monitoring. The main revelation of our study is that the interaction of the IMF discontinuity with the bow shock creates either one very elongated hot flow anomaly (HFA) or a pair of them that is (are) simultaneously observed at both flanks. Whereas the dusk HFA is weak and does not cause observable deformation of the magnetopause, the pressure variations connected with the dawn HFA lead to a magnetopause displacement by approx. = 5 R(sub E) outward from its nominal position. This is followed by a rapid inward motion of the magnetopause approx. = 4 R(sub E) inward with respect to the model location. The surface deformation is so large that the outermost THEMIS spacecraft was in the magnetosphere, whereas the spacecraft located 9 R(sub E) inbound entered into the magnetosheath at the same time. The whole event lasted about 5 minutes.

hot flow anomaly↗
NASA NTRS2012

Impact of Non-Uniform Beam Filling on Spaceborne Cloud and Precipitation Radar Retrieval Algorithms

In this presentation we will discuss the performance of classification and retrieval algorithms for spaceborne cloud and precipitation radars such as the Global Precipitation Measurement mission Dual-frequency Precipitation Radar (GPM/DPR), and notional radar for the Aerosol/Clouds/Ecosystem (ACE) mission and related concepts. Spaceborne radar measurements are simulated either from Airborne Precipitation Radar 2nd Generation observations, or from atmospheric model outputs via instrument simulators contained in the NASA Earth Observing Systems Simulators Suite (NEOS(sup 3)). Both methods account for the three dimensional nature of the scattering field at resolutions smaller than that of the spaceborne radar under consideration. We will focus on the impact of non-homogeneities of the field of hydrometeors within the beam. We will discuss also the performance of methods to identify and mitigate such conditions, and the resulting improvements in retrieval accuracy. The classification and retrieval algorithms analyzed in this study are those derived from APR-2's Suite of Processing and Retrieval Algorithms (ASPRA); here generalized to operate on an arbitrary set of radar configuration parameters to study the expected performance of spaceborne cloud and precipitation radars. The presentation will highlight which findings extend to other algorithm families and which ones do not.

Aerosol/Clouds/Ecosystem (ACE)↗
NASA NTRS2015

End of Life Disposal for Three Libration Point Missions through Manipulation of the Jacobi Constant and Zero Velocity Curves

The aim of this investigation is to determine the feasibility of mission disposal by inserting the spacecraft into a heliocentric orbit along the unstable manifold and then manipulating the Jacobi constant to prevent the spacecraft from returning to the Earth-Moon system. This investigation focuses around L1 orbits representative of ACE, WIND, and SOHO. It will model the impulsive delta-V necessary to close the zero velocity curves after escape through the L1 gateway in the circular restricted three body model and also include full ephemeris force models and higher fidelity finite maneuver models for the three spacecraft.

Wind↗
NASA NTRS2015

End of Life Disposal for Three Libration Point Missions through Manipulation of the Jacobi Constant and Zero Velocity Curves

Flight Dynamics Facility (FDF) located at NASA Goddard Space Flight Center (GSFC) provides the flight dynamics expertise for three Sun-Earth Moon L1 missions. Advanced Composition Explorer (ACE) launched August 1997 Solar and Heliospheric Observatory (SOHO) launched December 1995 Global Geospace Science WIND satellite launched November 1994 entered Lagrange point orbit in 2004.

Ace↗
DOE OSTI2025

Mechanistic Insights into the Demethylation of Lignin‐Derived Structures Using Protic Ionic Liquids: A Density Functional Theory Study

Lignin valorization is restricted by the stability of its methoxy groups, creating a critical need for efficient demethylation strategies. Here, in this study, density functional theory (DFT) is employed to dissect the mechanistic pathways of demethylation in lignin model compounds, guaiacol and syringol, using protic ionic liquids (PILs) that act as both solvent and catalyst. Conductor‐like Screening Model for Real Solvents (COSMO‐RS) analysis identifies monoethanolammonium acetate ([MEOA][Ace]) as the most promising medium, attributed to its strong hydrogen bonding network and solvation ability. By integrating implicit and explicit solvation models, it is revealed that an acid‐catalyzed hydrolytic mechanism governs demethylation, with PILs stabilizing crucial transition states and intermediates. Complementary electronic structure evaluations, including highest occupied molecular orbital–lowest unoccupied molecular orbital (HOMO‐LUMO) gap analysis, charge distribution, and electrostatic potential mapping, demonstrate how PILs lower energetic barriers and enhance reactivity. To simulate realistic environments, this study is extended to lignin dimer complexes with varying water content, uncovering how water‐bridged solvation reverses demethylation preference from guaiacyl to syringyl units. This mechanistic shift aligns with experimental observations showing faster S‐unit reactivity in hydrated systems. Together, these findings provide atomic‐level insight into lignin demethylation dynamics and highlight how tuning acid concentration in PILs can accelerate kinetics, enabling a rational pathway toward next‐generation biomass conversion technologies.

COSMO-RS↗
DOE OSTI2025

An NCrystal extension for thermal neutron scattering in MCNP

A new extension for MCNP has been developed that incorporates the NCrystal thermal scattering package in the form of a Fortran to C++ wrapper, which adds the capability to describe thermal neutron scattering in the NCrystal formalism instead of the ACE tabular format. This can be used to described poly- or single-crystalline materials as well as liquids. In conclusion, the functionality of this extension to MCNP is illustrated using a layered crystal monochromator, leakage of neutrons from a moderating sphere of material, and by simulating criticality benchmarks.

MCNP↗