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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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DOE OSTI2021

A data engineering approach for sustainable chemical end-of-life management

The presence of chemicals causing significant adverse human health and environmental effects during end-of-life (EoL) stages is a challenge for implementing sustainable management efforts and transitioning towards a safer circular life cycle. Conducting chemical risk evaluation and exposure assessment of potential EoL scenarios can help understand the chemical EoL management chain for its safer utilization in a circular life-cycle environment. However, the first step is to track the chemical flows, estimate releases, and potential exposure pathways. Hence, this work proposes an EoL data engineering approach to perform chemical flow analysis and screening to support risk evaluation and exposure assessment for designing a safer circular life cycle of chemicals. This work uses publicly-available data to identify potential post-recycling scenarios (e.g., industrial processing/use operations), estimate inter-industry chemical transfers, and exposure pathways to chemicals of interest. Furthermore, a case study demonstration shows how the data engineering framework identifies, estimates, and tracks chemical flow transfers from EoL stage facilities (e.g., recycling and recovery) to upstream chemical life cycle stage facilities (e.g., manufacturing). Also, the proposed framework considers current regulatory constraints on closing the recycling loop operations and provides a range of values for the flow allocated to post-recycling uses associated with occupational exposure and fugitive air releases from EoL operations.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS1972

Polarization of solar active regions at 9.5 mm wavelength.

A study of the circular polarization structure of solar active regions has been made from data obtained at 9.5 mm. All important active regions observed at 9.5 mm are bipolar in nature, the degree of polarization is about the same for both right and left circular components, and it ranges up to about 4%. These oppositely polarized components correspond with the Mt. Wilson magnetic regions of opposite polarity; the line of zero polarization delineates clearly the neutral line between the regions of opposite polarity on magnetograms. Unipolar regions in magnetograms also show up as unipolar regions at 9.5 mm. Magnetic fields as low as 5-10 G on magnetograms manifest as distinctly polarized regions on 9.5 mm maps.

Kundu, M. R.↗

DOE OSTI2020

PV_ICE: PV in the Circular Economy, Dynamic Energy and Materials Tool (PV ICE)

This open-source tool implements Circular Economy metrics for photovoltaic (PV) materials. It can be used to quantify and assign a value framework to efforts on re-design, reduction, replacement, reuse, recycling, and lifetime and reliability increases in the PV value chain. The PV_ICE is leveraging published data from different sources on PV manufacturing and predicted technological changes. Input data is being compiled here This tool will help implement circularity metrics, quantify and assign a value framework to efforts on re-design, reduction, replacement, reusage, recycling, and lifetime and reliability increases on PV.

Ovaitt, nee Ayala Pelaez, Silvana↗

DOE OSTI2021

Direct Measurement of Helicoid Surface States in RhSi Using Nonlinear Optics

Despite the fundamental nature of the edge state in topological physics, direct measurement of electronic and optical properties of the Fermi arcs of topological semimetals has posed a significant experimental challenge, as their response is often overwhelmed by the metallic bulk. However, laser-driven currents carried by surface and bulk states can propagate in different directions in nonsymmorphic crystals, allowing for the two components to be easily separated. Motivated by a recent theoretical prediction G. Chang et al., Phys. Rev. Lett. 124, 166404 (2020), we have measured the linear and circular photogalvanic effect currents deriving from the Fermi arcs of the nonsymmorphic, chiral Weyl semimetal RhSi over the 0.45-1.1 eV incident photon energy range. Our data are in good agreement with the predicted spectral shape of the circular photogalvanic effect as a function of photon energy, although the direction of the surface photocurrent departed from the theoretical expectation over the energy range studied. In conclusion, surface currents arising from the linear photogalvanic effect were observed as well, with the unexpected result that only two of the six allowed tensor element were required to describe the measurements, suggesting an approximate emergent mirror symmetry inconsistent with the space group of the crystal.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

NASA NTRS2005

Polynomial Expressions for Estimating Elastic Constants From the Resonance of Circular Plates

Two approaches were taken to make convenient spread sheet calculations of elastic constants from resonance data and the tables in ASTM C1259 and E1876: polynomials were fit to the tables; and an automated spread sheet interpolation routine was generated. To compare the approaches, the resonant frequencies of circular plates made of glass, hardened maraging steel, alpha silicon carbide, silicon nitride, tungsten carbide, tape cast NiO-YSZ, and zinc selenide were measured. The elastic constants, as calculated via the polynomials and linear interpolation of the tabular data in ASTM C1259 and E1876, were found comparable for engineering purposes, with the differences typically being less than 0.5 percent. Calculation of additional v values at t/R between 0 and 0.2 would allow better curve fits. This is not necessary for common engineering purposes, however, it might benefit the testing of emerging thin structures such as fuel cell electrolytes, gas conversion membranes, and coatings when Poisson s ratio is less than 0.15 and high precision is needed.

Salem, Jonathan A.↗

DOE OSTI2020

Conditional Generative Adversarial Networks for Solving Heat Transfer Problems

Generative Adversarial Networks (GANs) have been used as a deep learning approach to solving physics and engineering problems. Using deep learning for these problems is attractive in that reasonably accurate models can be inferred from only raw data, eliminating the need to define the exact physical equations governing a problem. We expand on previous work using GANs to generate steady-state solutions to the two-dimensional heat equation. Using a basic conditional GAN (cGAN), we generate accurate solutions for rectangular domains conditioned on four edge boundary conditions (MAE < 0.5%). For finding steady-state solutions over arbitrary two-dimensional domains (not constrained to rectangles), we use a cGAN designed for image-to-image translation. We train this GAN on various types of geometric domains (circles, squares, triangles, shapes with one circular or rectangular hole), achieving accurate results on test data made up of geometries similar to those in training (MAE < 1%). For both of these GANs, we experiment with different loss function terms, showing that a term using the gradients of solution images significantly improves the basic cGAN but not the image-to-image GAN. Lastly, we show that the image-to-image GAN performs poorly when applied to two-dimensional geometries that vary in structure from training data (MAE < 8% for shapes with multiple holes or different shaped holes). This demonstrates the cGAN's lack of generalizability. While the cGAN is an accurate and computationally efficient method when trained and tested on similarly structured data, it is a much less reliable method when applied to data that is slightly different in structure from the training data.

97 MATHEMATICS AND COMPUTING↗

NASA NTRS1992

The Panther Mountain circular structure, a possible buried meteorite crater

Panther Mountain, located near Phoenicia, New York, is part of the Catskill Mountains, which form the eastern end of the Allegheny Plateau in New York. It is a circular mass defined physiographically by an anomalous circular drainage pattern produced by Esopus Creek and its tributary Woodland Creek. The circular valley that rings the mountain is fracture-controlled; where bedrock is exposed, it shows a joint density 5 to 10 times greater than that on either side of the valley. Where obscured by alluvial valley fill, the bedrock's low seismic velocity suggests that this anomalous fracturing is continuous in the bedrock underlying the rim valley. North-south and east-west gravity and magnetic profiles were made across the structure. Terrane-corrected, residual gravity profiles show an 18-mgal negative anomaly, and very steep gradients indicate a near-surface source. Several possible explanations of the gravity data were modeled. We conclude that the Panther Mountain circular structure is probably a buried meteorite crater that formed contemporaneously with marine or fluvial sedimentation during Silurian or Devonian time. An examination of drill core and cuttings in the region is underway to search for ejecta deposits and possible seismic and tsunami effects in the sedimentary section. Success would result in both dating the impact and furnishing a chronostratigraphic marker horizon.

Isachsen, Y. W.↗

DOE OSTI2020

The Gemini Planet Imager Exoplanet Survey: Dynamical Mass of the Exoplanet β Pictoris b from Combined Direct Imaging and Astrometry

Herein, we present new observations of the planet β Pictoris b from 2018 with the Gemini Planet Imager (GPI), the first GPI observations following conjunction. Based on these new measurements, we perform a joint orbit fit to the available relative astrometry from ground-based imaging, the Hipparcos Intermediate Astrometric Data (IAD), and the Gaia DR2 position, and demonstrate how to incorporate the IAD into direct imaging orbit fits. We find a mass consistent with predictions of hot-start evolutionary models and previous works following similar methods, though with larger uncertainties: $12.8^{+5.3}_{–3.2} M_{Jup}$. Our eccentricity determination of ${0.12}_{-0.03}^{+0.04}$ disfavors circular orbits. We consider orbit fits to several different imaging data sets, and find generally similar posteriors on the mass for each combination of imaging data. Our analysis underscores the importance of performing joint fits to the absolute and relative astrometry simultaneously, given the strong covariance between orbital elements. Time of conjunction is well-constrained within 2.8 days of 2017 September 13, with the star behind the planet's Hill sphere between 2017 April 11 and 2018 February 16 (±18 days). Following the recent radial velocity detection of a second planet in the system, β Pic c, we perform additional two-planet fits combining relative astrometry, absolute astrometry, and stellar radial velocities. These joint fits find a significantly smaller mass (8.0 ± 2.6 M Jup) for the imaged planet β Pic b, in a somewhat more circular orbit. We expect future ground-based observations to further constrain the visual orbit and mass of the planet in advance of the release of Gaia DR4.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2023

A Machine Learning–Based Approach to Time-series Wave Identification in the Solar Wind

The Wind spacecraft has yielded several decades of high-resolution magnetic field data, a large fraction of which displays small-scale structures. In particular, the solar wind is full of wavelike fluctuations that appear in both the field magnitude and its components. The nature of these fluctuations can be tied to the properties of other structures in the solar wind, such as shocks, that have implications for the time evolution of the solar wind. As such, having a large collection of wave events would facilitate further study of the effects that these fluctuations have on solar wind evolution. Given the large volume of magnetic field data available, machine learning is the most practical approach to classifying the myriad small-scale structures observed. To this end, a subset of Wind data is labeled and used as a training set for a multibranch 1D convolutional neural network aimed at classifying circularly polarized wave modes. Using this algorithm, a preliminary statistical study of 1 yr of data is performed, yielding about 300,000 wave intervals out of about 5,000,000 solar wind intervals. The wave intervals come about more often in the fast solar wind and at higher temperatures, and the number of waves per day is highly periodic. This machine learning–based approach to wave detection has the potential to be a powerful, inexpensive way to catalog waves throughout decades of spacecraft data.

79 ASTRONOMY AND ASTROPHYSICS↗

NASA NTRS2014

NASA Tech Briefs, March 2014

Topics include: Data Fusion for Global Estimation of Forest Characteristics From Sparse Lidar Data; Debris and Ice Mapping Analysis Tool - Database; Data Acquisition and Processing Software - DAPS; Metal-Assisted Fabrication of Biodegradable Porous Silicon Nanostructures; Post-Growth, In Situ Adhesion of Carbon Nanotubes to a Substrate for Robust CNT Cathodes; Integrated PEMFC Flow Field Design for Gravity-Independent Passive Water Removal; Thermal Mechanical Preparation of Glass Spheres; Mechanistic-Based Multiaxial-Stochastic-Strength Model for Transversely-Isotropic Brittle Materials; Methods for Mitigating Space Radiation Effects, Fault Detection and Correction, and Processing Sensor Data; Compact Ka-Band Antenna Feed with Double Circularly Polarized Capability; Dual-Leadframe Transient Liquid Phase Bonded Power Semiconductor Module Assembly and Bonding Process; Quad First Stage Processor: A Four-Channel Digitizer and Digital Beam-Forming Processor; Protective Sleeve for a Pyrotechnic Reefing Line Cutter; Metabolic Heat Regenerated Temperature Swing Adsorption; CubeSat Deployable Log Periodic Dipole Array; Re-entry Vehicle Shape for Enhanced Performance; NanoRacks-Scale MEMS Gas Chromatograph System; Variable Camber Aerodynamic Control Surfaces and Active Wing Shaping Control; Spacecraft Line-of-Sight Stabilization Using LWIR Earth Signature; Technique for Finding Retro-Reflectors in Flash LIDAR Imagery; Novel Hemispherical Dynamic Camera for EVAs; 360 deg Visual Detection and Object Tracking on an Autonomous Surface Vehicle; Simulation of Charge Carrier Mobility in Conducting Polymers; Observational Data Formatter Using CMOR for CMIP5; Propellant Loading Physics Model for Fault Detection Isolation and Recovery; Probabilistic Guidance for Swarms of Autonomous Agents; Reducing Drift in Stereo Visual Odometry; Future Air-Traffic Management Concepts Evaluation Tool; Examination and A Priori Analysis of a Direct Numerical Simulation Database for High-Pressure Turbulent Flows; and Resource-Constrained Application of Support Vector Machines to Imagery.

Source record↗

DOE OSTI2023

AI ensemble for signal detection of higher order gravitational wave modes of quasi-circular, spinning, non-precessing binary black hole mergers

We introduce spatiotemporal-graph models that concurrently process data from the twin advanced LIGO detectors and the advanced Virgo detector. We trained these AI classifiers with 2.4 million IMRPhenomXPHM waveforms that describe quasi-circular, spinning, non-precessing binary black hole mergers with component masses m{1,2}∈[3M⊙,50M⊙], and individual spins sz{1,2}∈[−0.9,0.9]; and which include the (ℓ,|m|)={(2,2),(2,1),(3,3),(3,2),(4,4)} modes, and mode mixing effects in the ℓ=3,|m|=2 harmonics. We trained these AI classifiers within 22 hours using distributed training over 96 NVIDIA V100 GPUs in the Summit supercomputer. We then used transfer learning to create AI predictors that estimate the total mass of potential binary black holes identified by all AI classifiers in the ensemble. We used this ensemble, 3 classifiers for signal detection and 2 total mass predictors, to process a year-long test set in which we injected 300,000 signals. This year-long test set was processed within 5.19 minutes using 1024 NVIDIA A100 GPUs in the Polaris supercomputer (for AI inference) and 128 CPU nodes in the ThetaKNL supercomputer (for post-processing of noise triggers), housed at the Argonne Leadership Computing Facility. These studies indicate that our AI ensemble provides state-of-the-art signal detection accuracy, and reports 2 misclassifications for every year of searched data. This is the first AI ensemble designed to search for and find higher order gravitational wave mode signals.

Tian, Minyang↗

DOE OSTI2024

Tracking the topology of neural manifolds across populations

Neural manifolds summarize the intrinsic structure of the information encoded by a population of neurons. Advances in experimental techniques have made simultaneous recordings from multiple brain regions increasingly commonplace, raising the possibility of studying how these manifolds relate across populations. However, when the manifolds are nonlinear and possibly code for multiple unknown variables, it is challenging to extract robust and falsifiable information about their relationships. We introduce a framework, called the method of analogous cycles, for matching topological features of neural manifolds using only observed dissimilarity matrices within and between neural populations. We demonstrate via analysis of simulations and in vivo experimental data that this method can be used to correctly identify multiple shared circular coordinate systems across both stimuli and inferred neural manifolds. Conversely, the method rejects matching features that are not intrinsic to one of the systems. Further, as this method is deterministic and does not rely on dimensionality reduction or optimization methods, it is amenable to direct mathematical investigation and interpretation in terms of the underlying neural activity. We thus propose the method of analogous cycles as a suitable foundation for a theory of cross-population analysis via neural manifolds.

97 MATHEMATICS AND COMPUTING↗

NASA NTRS1939

Radiator Design and Installation

The fundamental principles of fluid flow, pressure losses, and heat transfer have been presented and analyzed for the case of a smooth tube with fully developed turbulent flow. These equations apply to tubes with large length-diameter ratios where the f1ow is at a high Reynolds Number. The error introduced by using these equations increases as the magnitude of the tube length and the air-flow Reynolds Number approaches the values encountered in modern radiator designs. Accordingly, heat-transfer tests on radiator sections were made and the results are presented in nondimensional form to facilitate their use and for comparison with other heat-transfer data. In addition, pressure losses were measured along smooth tubes of circular, square, and rectangular cross section and the results were also correlated and are presented in nondimensional form. The problem of a radiator design for a particular installation is solved, the experimental heat-transfer and pressure-loss data being used, on a basis of power chargeable to the radiator for form drag, for propelling the weight, and for forcing the air through the radiator. The case of an installation within a wing or an engine nacelle is considered. An illustration of radiator design is carried through for an arbitrary set of conditions. Sufficient detail is given to enable the reader to reproduce the analysis for any given case.

Brevoort, M.J.↗

NASA NTRS1992

Fluid mechanics experiments in oscillatory flow. Volume 2: Tabulated data

Results of a fluid mechanics measurement program in oscillating flow within a circular duct are presented. The program began with a survey of transition behavior over a range of oscillation frequency and magnitude and continued with a detailed study at a single operating point. Such measurements were made in support of Stirling engine development. Values of three dimensionless parameters, Re sub max, Re sub w, and A sub R, embody the velocity amplitude, frequency of oscillation, and mean fluid displacement of the cycle, respectively. Measurements were first made over a range of these parameters that are representative of the heat exchanger tubes in the heater section of NASA's Stirling cycle Space Power Research Engine (SPRE). Measurements were taken of the axial and radial components of ensemble-averaged velocity and rms velocity fluctuation and the dominant Reynolds shear stress, at various radial positions for each of four axial stations. In each run, transition from laminar to turbulent flow, and its reverse, were identified and sufficient data was gathered to propose the transition mechanism. Volume 2 contains data reduction program listings and tabulated data (including its graphics).

Seume, J.↗

NASA NTRS1986

Medium and large-scale variations of dynamo-induced electric fields from AE ion drift measurements

Current models of the low latitude electric field are largely based on data from incoherent scatter radars. These observations are extended through the addition of the rather extensive high quality electric field measurements from the Ion Drift Meter (IDM) aboard the Atmosphere Explorer (AE) spacecraft. Some preliminary results obtained from the Unified Abstract files of satellite AE-E are presented. This satellite was active from the end of 1975 through June 1981 in various elliptical and circular orbits having an inclination near 20 deg. The resulting data can be examined for the variation of ion drift with latitude, longitude, season, solar cycle, altitude, and magnetic activity. The results presented deal primarily with latitudinal variations of the drift features. Diagrams of data are given and briefly interpreted. The preliminary results presented here indicate that IDM data from the AE and the more recent Dynamics Explorer B spacecraft should continue to disclose some interesting and previously unobserved dynamical features of the low latitude F region.

Coley, W. R.↗

NASA NTRS2023

A Machine Learning-Based Approach to Time-Series Wave Identification in the Solar Wind

The Wind spacecraft has yielded several decades of high-resolution magnetic field data, a large fraction of which displays small-scale structures. In particular, the solar wind is full of wavelike fluctuations that appear in both the field magnitude and its components. The nature of these fluctuations can be tied to the properties of other structures in the solar wind, such as shocks, that have implications for the time evolution of the solar wind. As such, having a large collection of wave events would facilitate further study of the effects that these fluctuations have on solar wind evolution. Given the large volume of magnetic field data available, machine learning is the most practical approach to classifying the myriad small-scale structures observed. To this end, a subset of Wind data is labeled and used as a training set for a multi-branch 1D convolutional neural network aimed at classifying circularly polarized wave modes. Using this algorithm, a preliminary statistical study of one year of data is performed, yielding about 300,000 wave intervals out of about 5,000,000 solar wind intervals. The wave intervals come about more often in the fast solar wind and at higher temperatures, and the number of waves per day is highly periodic. This machine learning-based approach to wave detection has the potential to be a powerful, inexpensive way to catalog waves throughout decades of spacecraft data.

Samuel Fordin↗

NASA NTRSDate not supplied

Enhancements to Linear Stability-Based, CFD-integrated Transition Prediction for High-Speed Flows

Combining linear stability calculations with computational fluid dynamics (CFD) simulations has great potential for the automated modeling of high-speed flows, especially when adequate information about the configuration and the disturbance environment is available. However, a significant impediment to the applicability of this technique is the lack of an efficient method to calculate the crucial amplification ratio corresponding to the onset of transition in hypersonic flows. This ratio, also known as the "transition N-factor," is dependent upon the freestream disturbance environment as well as the surface properties of the test article. In response to the need for an engineering solution to predict the transition N-factor within conventional hypersonic wind tunnels, this paper presents a data-driven correlation that expands the existing correlations from straight circular cones with a narrow range of half angles to a broader array of axisymmetric configurations. Furthermore, when tested against a chosen dataset that was not used in its calibration, the suggested correlation shows good predictive accuracy with an RMS error of only 6.9%. Although similar accuracy may also be achieved via existing correlations based on similar datasets, predictions based on the proposed correlation have the advantage of not requiring an extensive amount of configuration-specific data. Practical applications often have access to the input parameters needed for this correlation, such as the freestream disturbance intensity, Mach number, and body-based slenderness Reynolds number. Additionally, this correlation outperforms the traditional assumption of a constant N-factor, particularly for configurations with blunted nose geometries. The development of this correlation is grounded in an extensive dataset encompassing conical models with body half-angles varying between 5 degrees and 16 degrees, Mach numbers ranging from 5 to 14, and nosetip-based Reynolds numbers approaching the transition reversal limit for blunt-nosed cones.

CFD↗

NASA NTRSDate not supplied

Enhancements to Linear Stability-Based, CFD-integrated Transition Prediction for High-Speed Flows

CFD↗