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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 271 records · Page 15
All sourcesNASA NTRSDOE OSTI
NASA NTRS1985

Trajectory reconstruction and aerodynamic results from the first Discovery flight, STS-14(41-D)

Trajectory reconstruction results for the first Discovery flight are presented. Spacecraft dynamic measurements from IMU2 were utilized in conjunction with the ground based tracking data from two S-band stations, eight C-band, and five cameras at Edwards Air Force Base to determine the spacecraft trajectory from epoch through roll-out on Runway 17. Specifics as to the trajectory reconstruction are discussed in Section 1. The final inertial profile is BT14NO2/UN=169750N. Merging of this file with the final LAIRS atmosphere is discussed in Section 2. The final Extended BET is ST14BET/UN=274885C. Section 3 presents plots of relevant parameters from the AEROBET as well as aerodynamic performance comparison results. High frequency files for maneuver extraction were also generated as discussed in Section 4. Appendices are attached which contain: (1) spacecraft and physical parameters utilized, (2) final residuals obtained from the data fitting process, (3) listing of trajectory parameters, and (4) archival information.

Kelly, G. M.↗
NASA NTRS2014

A Numerical Testbed for Remote Sensing of Aerosols, and its Demonstration for Evaluating Retrieval Synergy from a Geostationary Satellite Constellation of GEO-CAPE and GOES-R

We present a numerical testbed for remote sensing of aerosols, together with a demonstration for evaluating retrieval synergy from a geostationary satellite constellation. The testbed combines inverse (optimal-estimation) software with a forward model containing linearized code for computing particle scattering (for both spherical and non-spherical particles), a kernel-based (land and ocean) surface bi-directional reflectance facility, and a linearized radiative transfer model for polarized radiance. Calculation of gas absorption spectra uses the HITRAN (HIgh-resolution TRANsmission molecular absorption) database of spectroscopic line parameters and other trace species cross-sections. The outputs of the testbed include not only the Stokes 4-vector elements and their sensitivities (Jacobians) with respect to the aerosol single scattering and physical parameters (such as size and shape parameters, refractive index, and plume height), but also DFS (Degree of Freedom for Signal) values for retrieval of these parameters. This testbed can be used as a tool to provide an objective assessment of aerosol information content that can be retrieved for any constellation of (planned or real) satellite sensors and for any combination of algorithm design factors (in terms of wavelengths, viewing angles, radiance and/or polarization to be measured or used). We summarize the components of the testbed, including the derivation and validation of analytical formulae for Jacobian calculations. Benchmark calculations from the forward model are documented. In the context of NASA's Decadal Survey Mission GEOCAPE (GEOstationary Coastal and Air Pollution Events), we demonstrate the use of the testbed to conduct a feasibility study of using polarization measurements in and around the O2 A band for the retrieval of aerosol height information from space, as well as an to assess potential improvement in the retrieval of aerosol fine and coarse mode aerosol optical depth (AOD) through the synergic use of two future geostationary satellites, GOES-R (Geostationary Operational Environmental Satellite R-series) and TEMPO (Tropospheric Emissions: Monitoring of Pollution). Strong synergy between GEOS-R and TEMPO are found especially in their characterization of surface bi-directional reflectance, and thereby, can potentially improve the AOD retrieval to the accuracy required by GEO-CAPE.

Aerosols↗
DOE OSTI2021

Measurement of the CP-violating phase ϕ s in B$_s^0$→J/ψϕ decays in ATLAS at 13 TeV

A measurement of the B$_s^0$→J/ψϕ decay parameters using 80.5 fb -1 of integrated luminosity collected with the ATLAS detector from 13 Te proton–proton collisions at the LHC is presented. The measured parameters include the CP-violating phase ϕ s , the width difference Δ Γ s between the B$_s^0$ meson mass eigenstates and the average decay width Γ s . The values measured for the physical parameters are combined with those from 19.2fb -1 of 7 and 8 Te data, leading to the following: ϕs=-0.087±0.036(stat.)±0.021(syst.)rad ΔΓ s =0.0657±0.0043(stat.)±0.0037(syst.)ps -1 Γ s =0.6703±0.0014(stat.)±0.0018(syst.)ps -1 Results for ϕ s and Δ Γ s are also presented as 68% confidence level contours in the ϕ s –Δ Γ s plane. Furthermore the transversity amplitudes and corresponding strong phases are measured. ϕ s and Δ Γ s measurements are in agreement with the Standard Model predictions.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
NASA NTRS1993

A test of the applicability of independent scattering to high albedo planetary regoliths

We show that 'independent scattering' is a useful approximation for high albedo particles whose size and packing density are similar to typical particles in the lunar regolith. Laboratory measurements of the intensity and linear polarization of light scattered from a laboratory sample of glass spheres of know size and composition are compared to radiative transfer calculations of the same observable quantities. Mie scattering is integrated over the size distribution of the particles to determine the mean phase and polarization functions, or phase matrix, of the particle. Assuming that the particles scatter independently, the 'doubling method' is used to rigorously calculate multiple scattering in an optically thick layer of these anisotropically scattering particles. All of the major features 'predicted' in the calculated intensity (double peaks at small phase angles) and polarization (negative branch at small phase angles, large positive peak near 20 degrees phase, and small polarization elsewhere) are observed in the laboratory measurements, with good quantitative agreement indicated at phase angles less than 90 degrees. Even though the particles are supported by physical contact with each other in the sample, as are the particles in planetary regoliths, the independent scattering calculation yields a good approximation to both the intensity and polarization. The physical parameters input to the calculation are only the size distribution of the particles and their complex index of refraction (composition). Significant advantages of this approach are that the phase matrix is calculated from basic physical principles and both the intensity and polarization are determined simultaneously. This model may have broad applications to the interpretation of photometry, spectroscopy, and polarimetry of the icy regoliths of high albedo satellites. The intent of this effort is to perform a controlled experiment that tests the utility of the independent scattering assumption

Goguen, J. D.↗
DOE OSTI2021

The role of toroidal rotation in the very high energy confinement quality observed in super H-mode experiments on DIII-D

In this paper, we report the key role that toroidal rotation and the related E × B shear physics played in the very high energy confinement quality (H 98y2 > 1.5) of super H-mode experiments on DIII-D. Experiments show that the energy confinement quality decreases when toroidal rotation decreases due to the decreased externally controlled torque per particle. Meanwhile, the total pedestal pressure in the experiments remains very high during the rotation and confinement quality change. TGYRO transport modeling suggests the contribution from rotation in the E × B shear is responsible for the confinement quality in excess of standard H-mode (H 98y2 ~ 1). CGYRO gyrokinetic simulations reveal the governing physics in the core plasma of super H-modes: significant up-shift of nonlinear the ITG critical gradient is observed when applying E × B shear physics in the modeling based on experimental data. Furthermore, the effects of other physical parameters and contribution from pedestal height, which may play minor roles in this study, are also discussed.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
NASA NTRS1977

Identification of state variables and dynamic inflow from rotor model dynamic tests

The paper describes methods for extracting unknown state variables and parameters from dynamic rotor model tests given transient cyclic pitch stirring inputs, blade root flap-bending measurements, and the form of the dynamic rotor equations, including a rotor dynamic inflow description, when none of the physical parameters are known. A simplified version of the maximum likelihood method seems best suited for this purpose. The measurement equation error covariance matrix is assumed constant during each iteration, but updated for the subsequent iteration. A detailed analysis of the suitability of the derived techniques for studying various rotor dynamic inflow effects is provided.

Banerjee, D.↗
NASA NTRS1994

Theoretical studies of spectroscopic problems of importance for atmospheric radiation measurements

Many of the instruments used to deduce the physical parameters of the Earth's atmosphere necessary for climate studies or for pollution monitoring (for instance, temperature versus pressure or number densities of trace molecules) rely on the existence of accurate spectroscopic data and an understanding of the physical processes responsible for the absorption or emission of radiation. During the summer, research was either continued or begun on three distinct problems: (1) an improved theoretical framework for the calculation of the far-wing absorption of allowed spectral lines; (2) a refinement of the calculation of the collision-induced fundamental spectrum of N2; and (3) an investigation of possible line-mixing effects in the fundamental spectrum of CH4. Progress in these three areas is summarized below. During the past few years, we have developed a theoretical framework for the calculation of the absorption of radiation by the far wings of spectral lines. Such absorption due to water vapor plays a crucial role in the greenhouse effect as well as limiting the retrieval of temperature profiles from satellite data. Several improvements in the theory have been made and the results are being prepared for publication. Last year we published results for the theoretical calculation of the absorption of radiation due to the dipoles induced during binary collisions of N2 molecules using independently measured molecular parameters; the results were in reasonable agreement with experimental data. However, recent measurements have revealed new fine structure that has been attributed to line-mixing effects. We do not think that this is correct, rather that the structure results from short-range anisotropic dipoles. We are in the process of including this refinement in our theoretical calculation in order to compare with the new experimental data. Subtle changes in the spectra of CH4 measured by researchers at Langley have also been attributed to line-mixing effects. By analyzing the same spectral lines we have attempted to verify or rule out possible line-mixing mechanisms. Due to the complexity and richness of the spectrum of this highly symmetric molecule, as well as the small magnitude of the effects, a detailed first-principle calculation of the mixing is a difficult problem. Before such a program is undertaken it is important to glean as much information as possible concerning the possible mechanisms by a systematic analysis of the existing data.

Tipping, Richard H.↗
DOE OSTI2022

Data-driven surrogate modeling of hPIC ion energy-angle distributions for high-dimensional sensitivity analysis of plasma parameters' uncertainty

In this work, we present a data-driven strategy for effective construction of a surrogate model in high-dimensional parameter space for the ion energy-angle distribution (IEAD) output of hPIC simulations of plasma-surface interactions. The methodology is based on a bin-by-bin least-squares fitting of the IEAD in the parameter space. The fitting is performed in a transformed coordinate system to normalize the IEAD, and it employs sparse grids for sampling the parameter space to overcome sampling challenges in high dimensions. The surrogate model is significantly cheaper computationally than direct hPIC simulations yet maintains high fidelity to them, providing a fast emulator for hPIC simulations. Sensitivity analysis based on the surrogate model is utilized to characterize the dependence of the ion impact angle and energy moments on the physical parameters.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
NASA NTRS1990

Stochastic Fermi acceleration in solar flares

Proton spectra, valid from non- to ultra-relativistic energies, resulting from stochastic Fermi acceleration in solar flares are calculated. These spectra were obtained by numerically solving the Fokker-Planck equation, in which the escape of the particles from the acceleration region is characterized by an energy-independent escape time. In addition to equilibrium spectra, time-dependent energy spectra showing the approach to equilibrium are also presented. These numerical equilibrium spectra are compared with previous results which were obtained either by Monte Carlo simulations or approximate analytical treatments. There are no analytic solutions valid in the transrelativistic regime, which is very important for the production of pions and neutrons in solar flares. The acceleration efficiency is related to physical parameters, in particular the energy density in either magnetosonic or Alfven waves, and a lower limit is placed on either of these energy densities from acceleration times implied by gamma-ray observations. Also discussed is the physical interpretation of the escape time.

Miller, James A.↗
DOE OSTI2024

Effects of Copolymer Structure on Enzyme-Catalyzed Polyester Recycling

Polyesters are an omnipresent material used for a variety of applications (e.g., bottles, packaging, textile, windshield), roughly comprising 8% of plastics produced worldwide. Enzymatic recycling is an emerging solution to deal with the increasingly diverse polyesters that are not suitable for mechanical recycling. However, enzyme activity and efficiency are still the limiting factors impeding enzymatic recycling for different plastic waste forms. The effects of thermal and structural properties (e.g., glass transition temperature, crystallinity, specific surface area), which are determined by chemical composition and preprocessing, directly influence enzyme recycling efficiency. This work investigates two extrusion methods (single screw and twin-screw extrusion) to pretreat a range of copolyesters (RPET, PETG, Ecozen, Tritan and PBT) and modify their properties (i.e., glass transition temperature (T g ), crystallinity (%), and molecular weight (M n )). A PET-specific enzyme, leaf-branch compost cutinase (LCC ICCG ), produced from a fed-batch fermentation of Escherichia coli BL21(DE3), was used for the enzymatic depolymerization of different polyesters. Several copolyesters showed improved depolymerization after pretreatment, as measured by rate and amount of monomers produced. Furthermore, those that did not depolymerize were found to have exceptionally high glass transition temperature or percent crystallinity, highlighting the importance of these physical parameters on conversion efficiency.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS1991

Nonlinear evolution of subsonic and supersonic disturbances on a compressible free shear layer

The effects of a nonlinear-nonequilibrium-viscous critical layer on the spatial evolution of subsonic and supersonic instability modes on a compressible free shear layer is considered. It is shown that the instability wave amplitude is governed by an integrodifferential equation with cubic-type nonlinearity. Numerical and asymptotic solutions to this equation show that the amplitude either ends in a singularity at a finite downstream distance or reaches an equilibrium value, depending on the Prandtl number, viscosity law, viscous parameter and a real parameter which is determined by the linear inviscid stability theory. A necessary condition for the existence of the equilibrium solution is derived, and whether or not this condition is met is determined numerically for a wide range of physical parameters including both subsonic and supersonic disturbances. it is found that no equilibrium solution exists for the subsonic modes unless the temperature ratio of the low-to-high-speed streams exceeds a critical value, while equilibrium solutions for the most rapidly growing supersonic mode exist over most of the parameter range examined.

Leib, S. J.↗
NASA NTRS1997

Potential for Remotely Sensed Soil Moisture Data in Hydrologic Modeling

Many hydrologic processes display a unique signature that is detectable with microwave remote sensing. These signatures are in the form of the spatial and temporal distributions of surface soil moisture and portray the spatial heterogeneity of hydrologic processes and properties that one encounters in drainage basins. The hydrologic processes that may be detected include ground water recharge and discharge zones, storm runoff contributing areas, regions of potential and less than potential ET, and information about the hydrologic properties of soils and heterogeneity of hydrologic parameters. Microwave remote sensing has the potential to detect these signatures within a basin in the form of volumetric soil moisture measurements in the top few cm. These signatures should provide information on how and where to apply soil physical parameters in distributed and lumped parameter models and how to subdivide drainage basins into hydrologically similar sub-basins.

Engman, Edwin T.↗
DOE OSTI2022

Accretion disc sizes from continuum reverberation mapping of AGN selected from the ZTF survey

ABSTRACT We present the accretion disc-size estimates for a sample of 19 active galactic nuclei (AGNs) using the optical g-, r-, and i-band light curves obtained from the Zwicky Transient Facility survey. All the AGNs have reliable supermassive black hole (SMBH) mass estimates based on previous reverberation mapping measurements. The multiband light curves are cross-correlated, and the reverberation lag is estimated using the Interpolated Cross-Correlation Function method and the Bayesian method using the javelin code. As expected from the disc-reprocessing arguments, the g − r band lags are shorter than the g − i band lags for this sample. The interband lags for all, but five sources, are larger than the sizes predicted from the standard Shakura Sunyaev (SS) analytical model. We fit the light curves directly using a thin disc model implemented through the javelin code to get the accretion disc sizes. The disc sizes obtained using this model are on an average 3.9 times larger than the prediction based on the SS disc model. We find a weak correlation between the disc sizes and the known physical parameters, namely the luminosity and the SMBH mass. In the near future, a large sample of AGNs covering broader ranges of luminosity and SMBH mass from large photometric surveys would be helpful in a better understanding of the structure and physics of the accretion disc.

Jha, Vivek Kumar (ORCID:0000000232776335)↗
NASA NTRS2000

Fiber Optic Sensors for Health Monitoring of Morphing Airframes: Bragg Grating Strain and Temperature Sensor - Part 1

Fiber optic sensors are being developed for health monitoring of future aircraft. Aircraft health monitoring involves the use of strain, temperature, vibration and chemical sensors to infer integrity of the aircraft structure. Part 1 of this two part series describes sensors that will measure load and temperature signatures of these structures. In some cases a single fiber may be used for measuring these parameters. Part 2 will describe techniques for using optical fibers to monitor composite cure in real time during manufacture and to monitor in-service integrity of composite structures using a single fiber optic sensor capable of measuring multiple chemical and physical parameters. The facilities for fabricating optical fiber and associated sensors and the methods of demodulating Bragg gratings for strain measurement will be described.

Wood, Karen↗
NASA NTRS2009

A Mode-Shape-Based Fault Detection Methodology for Cantilever Beams

An important goal of NASA's Internal Vehicle Health Management program (IVHM) is to develop and verify methods and technologies for fault detection in critical airframe structures. A particularly promising new technology under development at NASA Langley Research Center is distributed Bragg fiber optic strain sensors. These sensors can be embedded in, for instance, aircraft wings to continuously monitor surface strain during flight. Strain information can then be used in conjunction with well-known vibrational techniques to detect faults due to changes in the wing's physical parameters or to the presence of incipient cracks. To verify the benefits of this technology, the Formal Methods Group at NASA LaRC has proposed the use of formal verification tools such as PVS. The verification process, however, requires knowledge of the physics and mathematics of the vibrational techniques and a clear understanding of the particular fault detection methodology. This report presents a succinct review of the physical principles behind the modeling of vibrating structures such as cantilever beams (the natural model of a wing). It also reviews two different classes of fault detection techniques and proposes a particular detection method for cracks in wings, which is amenable to formal verification. A prototype implementation of these methods using Matlab scripts is also described and is related to the fundamental theoretical concepts.

Tejada, Arturo↗
NASA NTRS2018

Atmospheric Chemistry Modeling Using a Regression Forest Model

Atmospheric chemistry is central to many environmental issues such as air pollution, climate change, and stratospheric ozone loss. Chemistry Transport Models (CTM) are a central tool for understanding these issues, whether for research or for forecasting. These models split the atmosphere in a large number of grid-boxes and consider the emission of compounds into these boxes and their subsequent transport, deposition, and chemical processing. The chemistry is represented through a series of simultaneous ordinary differential equations, one for each compound. Given the difference in life-times between the chemical compounds (milli-seconds for O1D to years for CH4) these equations are numerically stiff and solving them consists of a significant fraction of the computational burden of a CTM. We have investigated a machine learning approach to solving the differential equations instead of solving them numerically. From an annual simulation of the GEOS-Chem model we have produced a training dataset consisting of the concentration of compounds before and after the differential equations are solved, together with some key physical parameters for every grid-box and time-step. From this dataset we have trained a machine learning algorithm (regression forest) to be able to predict the concentration of the compounds after the integration step based on the concentrations and physical state at the beginning of the time step. We have then included this algorithm back into the GEOS-Chem model, bypassing the need to integrate the chemistry. This machine learning approach shows many of the characteristics of the full simulation and has the potential to be substantially faster. There are a wide range of application for such an approach - generating boundary conditions, for use in air quality forecasts, chemical data assimilation systems, centennial scale climate simulations etc. We discuss our approches' speed and accuracy, and highlight some potential future directions for improving this approach.

Keller, Christoph A.↗
NASA NTRS2019

Atmospheric Chemistry Modeling Using Machine Learning

Atmospheric chemistry models are a central tool to study the impact of chemical constituents on the environment, vegetation and human health. These models split the atmosphere in a large number of grid-boxes and consider the emission of compounds into these boxes and their subsequent transport, deposition, and chemical processing. The chemistry is represented through a series of simultaneous ordinary differential equations, one for each compound. Given the difference in life-times between the chemical compounds (milli-seconds for O1D to years for CH4) these equations are numerically stiff and solving them consists of a significant fraction of the computational burden of a chemistry model. We have investigated a machine learning approach to emulate the chemistry instead of solving the differential equations numerically. From a one-month simulation of the GEOS-Chem model we have produced a training dataset consisting of the concentration of compounds before and after the differential equations are solved, together with some key physical parameters for every grid-box and time-step. From this dataset we have trained a machine learning algorithm (regression forest) to be able to predict the concentration of the compounds after the integration step based on the concentrations and physical state at the beginning of the time step. We have then included this algorithm back into the GEOS-Chem model, bypassing the need to integrate the chemistry. This machine learning approach shows many of the characteristics of the full simulation and has the potential to be substantially faster. There are a wide range of application for such an approach - generating boundary conditions, for use in air quality forecasts, chemical data assimilation systems, etc. We discuss speed and accuracy of our approach, and highlight some potential future directions for improving it.

Keller, Christoph A.↗
NASA NTRS2019

Machine Learning Application to Atmospheric Chemistry Modeling

Atmospheric chemistry models are a central tool to study the impact of chemical constituents on the environment, vegetation and human health. These models split the atmosphere in a large number of grid-boxes and consider the emission of compounds into these boxes and their subsequent transport, deposition, and chemical processing. The chemistry is represented through a series of simultaneous ordinary differential equations, one for each compound. Given the difference in life-times between the chemical compounds (milli-seconds for O (sup 1) D (Deuterium) to years for CH4) these equations are numerically stiff and solving them consists of a significant fraction of the computational burden of a chemistry model. We have investigated a machine learning approach to emulate the chemistry instead of solving the differential equations numerically. From a one-month simulation of the GEOS-Chem model we have produced a training dataset consisting of the concentration of compounds before and after the differential equations are solved, together with some key physical parameters for every grid-box and time-step. From this dataset we have trained a machine learning algorithm (regression forest) to be able to predict the concentration of the compounds after the integration step based on the concentrations and physical state at the beginning of the time step. We have then included this algorithm back into the GEOS-Chem model, bypassing the need to integrate the chemistry. This machine learning approach shows many of the characteristics of the full simulation and has the potential to be substantially faster. There are a wide range of application for such an approach - generating boundary conditions, for use in air quality forecasts, chemical data assimilation systems, etc. We discuss speed and accuracy of our approach, and highlight some potential future directions for improving it.

Keller, Christoph A.↗