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At least 1,765 records · Page 98

Second Time-Resolved Observations of Precipitation Structure and Storm Intensity With a Constellation of Smallsats (TROPICS) Mission Applications Workshop

The NASA Time-Resolved Observations of Precipitation structure and storm Intensity with a Constellation of Smallsats (TROPICS) mission is a constellation of state-of-the-science observing platforms that will measure temperature and humidity soundings and precipitation with spatial resolution comparable to current operational passive microwave (PMW) sounders but with unprecedented temporal resolution (Mission Website).1 TROPICS is a cost-capped ($30.2 million), Venture-class mission funded by the NASA Earth Science Division (ESD) and led by Principal Investigator Dr. William Blackwell from the Massachusetts Institute of Technology Lincoln Laboratory (MIT LL). The mission consists of a constellation of six, three-unit (3U) CubeSats (approximately 10×10×34 cm), each hosting a 12-channel PMW spectrometer based on the Micro-sized Microwave Atmospheric Satellite-2 (MicroMAS-2) developed at MIT LL, but with a substantially improved design. TROPICS will provide imagery near 91 and 205 GHz, temperature sounding near 118 GHz, and moisture sounding near 183 GHz. Spatial resolution at nadir will be around 27 km for temperature and 17 km for moisture and precipitation with a swath width of approximately 2,000 km from a 550-km orbit altitude. Both the spatial resolution and swath width are similar to the Advanced Technology Microwave Sounder (ATMS) that is being flown as part of the Suomi National Polar-orbiting Partnership (SNPP) and National Oceanic and Atmospheric Administration (NOAA) Joint Polar Satellite System (JPSS). In addition, TROPICS meets many of the requirements outlined in the 2007 Decadal Survey for the Precision and All-Weather Tempera-ture and Humidity (PATH) mission, which was originally envisioned as a microwave instrument in geostationary orbit. TROPICS enables temporal resolution approaching that of geostationary orbit but at a much lower cost, demonstrating a technology that could impact the design of future Earth-observing missions. The satellites for the TROPICS mission were delivered to NASA in 2019 for launches planned in the 2021–2022 timeframe. The primary mission objective of TROPICS is to relate temperature, humidity, and precipitation structure to the evolution of tropical cyclone (TC) intensity.

remote sensing↗

Scale-Resolving Simulations of a Supersonic Retro-Propulsion Concept For Mars Entry, Descent, and Landing

Supersonic retro-propulsion (SRP) is a deceleration technology that could enable largerpayloads and potentially humans to be brought from space to the surface of Mars safely.Accurate and reliable CFD predictions are needed to help design future Mars entry, descent,and landing (EDL) vehicles due to the cost and limitations of wind tunnel tests. TypicalCFD approaches struggle at points along the Mars EDL trajectory with high enough altitude(low enough ambient pressure) that the SRP motor plumes are over-expanded. This workdemonstrates how high-order scale-resolving simulations on Cartesian AMR grids can provideaccurate and reliable time-averaged aerodynamic loads on the vehicle for this challengingflow regime. Greater than second order convergence in the time-averaged integrated axialforce𝐶𝐴is obtained across the three finest mesh resolutions simulated, and the estimateduncertainty is smaller than the unsteady standard deviation – demonstrating the accuracy andreliability of the method. Although costly, this approach can yield higher confidence in𝐶𝐴thanpreviously obtained in a reasonable turnaround time and is within reach for a few points in thetrajectory (i.e. Mach 2.4, 2.52, and 2.78). Further computational performance and numericalmethods improvements are needed to reduce the cost enough to bring this method into the EDLengineering design cycle.

S&M↗

Precise Timing and Phase-resolved Spectroscopy of the Young Pulsar J1617-5055 with NuSTAR

We report on a Nuclear Spectroscopic Telescope Array (NuSTAR) observation of the young, energetic pulsar PSR J1617-5055. Parkes Observatory 3 GHz radio observations of the pulsar (taken about 7 yr before the NuSTAR observations) are also reported here. NuSTAR detected pulsations at a frequency of f ≍ 14.4 Hz (P ≍ 69.44 ms) and, in addition, the observation was long enough to measure the source's frequency derivative, ḟ ≈ -2.8 X10^(-11) Hz/s. We find that the pulsar shows one peak per period at both hard X-ray and radio wavelengths, but that the hard X-ray pulse is broader (having a duty cycle of ~0.7), than the radio pulse (having a duty cycle of ~0.08). Additionally, the radio pulse is strongly linearly polarized. J1617's phase-integrated hard X-ray spectrum is well fit by an absorbed power-law model, with a photon index Γ = 1.59 ± 0.02. The hard X-ray pulsations are well described by three Fourier harmonics, and have a pulsed fraction that increases with energy. We also fit the phase-resolved NuSTAR spectra with an absorbed power-law model in five phase bins and find that the photon index varies with phase from Γ = 1.52 ± 0.03 at phases around the flux maximum to Γ = 1.79 ± 0.06 around the flux minimum. Last, we compare our results with other pulsars whose magnetospheric emission is detected at hard X-ray energies and find that, similar to previous studies, J1617's hard X-ray properties are more similar to the MeV pulsars than the GeV pulsars.

Neutron Stars↗

Spectroscopy of the first resolved strongly lensed Type Ia supernova iPTF16geu

We report the results from spectroscopic observations of the multiple images of the strongly lensed Type Ia supernova (SN Ia), iPTF16geu, obtained with ground-based telescopes and the Hubble Space Telescope (HST). From a single epoch of slitless spectroscopy with HST, we resolve spectra of individual lensed supernova images for the first time. This allows us to perform an independent measurement of the time-delay between the two brightest images, Δt = 1.4 ± 5.0 d, which is consistent with the time-delay measured from the light curves. We also present measurements of narrow emission and absorption lines characterizing the interstellar medium in the SN Ia host galaxy at z = 0.4087, as well as in the foreground lensing galaxy at z = 0.2163. We detect strong Na ID absorption in the host galaxy, indicating that iPTF16geu belongs to a subclass of SNe Ia displaying ‘anomalously’ large Na ID column densities compared to dust extinction derived from light curves. For the lens galaxy, we refine the measurement of the velocity dispersion, σ = 129 ± 4 km/s, which significantly constrains the lens model. We use ground-based spectroscopy, boosted by a factor ∼70 from lensing magnification, to study the properties of a high-z SN Ia with unprecedented signal-to-noise ratio. The spectral properties of the supernova, such as pseudo-Equivalent widths of several absorption features and velocities of the Si II-line, indicate that iPTF16geu is a normal SN Ia. We do not detect any significant deviations of the SN spectral energy distribution from microlensing of the SN photosphere by stars and compact objects in the lensing galaxy.

J Johansson↗

Probing the High-energy Gamma-Ray Emission Mechanism in the Vela Pulsar via Phase-resolved Spectral and Energy-dependent Light-curve Modeling

Recent kinetic simulations sparked a debate regarding the emission mechanism responsible for pulsed GeV γ-ray emission from pulsars. Some models invoke curvature radiation, while other models assume synchrotron radiation in the current sheet. We interpret the curved spectrum of the Vela pulsar as seen by H.E.S.S. II (up to ∼100 GeV) and the Fermi Large Area Telescope to be the result of curvature radiation due to primary particles in the pulsar magnetosphere and current sheet. We present phase-resolved spectra and energy-dependent light curves using an extended slot gap and current-sheet model, invoking a step function for the accelerating electric field as motivated by kinetic simulations. We include a refined calculation of the curvature radius of particle trajectories in the lab frame, impacting the particle transport, predicted light curves, and spectra. Our model reproduces the decrease of the flux of the first peak relative to the second one, evolution of the bridge emission, near-constant phase positions of peaks, and narrowing of pulses with increasing energy. We can explain the first of these trends because we find that the curvature radii of the particle trajectories in regions where the second γ-ray light-curve peak originates are systematically larger than those associated with the first peak, implying that the spectral cutoff of the second peak is correspondingly larger. However, an unknown azimuthal dependence of the E field, as well as uncertainty in the precise spatial origin of the GeV emission, precludes a simplistic discrimination of emission mechanisms.

Monica Barnard↗

ArcjetCV: a new machine learning application for extracting time-resolved recession measurements from arc jet test videos

Arc jet Computer Vision (ArcjetCV) is a software application built to automate analysis of arc jet ground test video footage. This includes tracking material recession, sting arm motion, and the shock-material standoff distance. This provides a new capability to resolve and validate new physics associated with non-linear processes. This is an essential step to reduce testing, modeling, and validation uncertainties for heatshield material performance. ArcjetCV uses several types of machine learning (convolutional neural net: CNN, decision tree: DT, k-means unsupervised clustering: KM) to automate the video processing pipeline. These include inferring the start/stop of time segments of interest (1D CNN), measuring the time-dependent 2D recession of the material samples (2D CNN, DT), measuring the time-dependent shock standoff distance (2D CNN, DT), and post-processing cleaning of the recession data (KM). The software also provides a graphical user interface for ease of use. The results of using this tool on arc jet videos show non-linear time-dependent effects can be important for certain materials.

machine learning↗

Progress Toward Evaluating Roughness-Resolved Aerothermodynamic Simulations for the Mars Sample Return Earth Entry System

This work pursues the efficient Navier-Stokes simulation, using NASA’s standard LAURA and DPLR simulation tools, of the flowfield and convective heating resulting from the patterned-roughness surface of the Mars Sample Return (MSR) Earth Entry System (EES). This type of simulation has been infeasible for past NASA missions. However, for EES, the combination of a patterned roughness heatshield approximated with a sinusoidal pattern, the zero degrees angle of attack nominal flow, and the supersonic boundary layer edge Mach number, all combine to make these roughness resolved grid simulations feasible. The patterned roughness and zero degrees angle of attack nominal flow allow the simulation domain to be reduced to a thin axial flowfield slice that captures a minimum of single period of the roughness pattern. With a periodic boundary condition applied along the boundaries of this slice, simulation on the reduced domain is identical to the full domain but with significant cost saving. The supersonic boundary layer edge Mach number prevents the upstream influence, which allows the smooth stagnation region to be frozen. This freezing of the stagnation region flow is required because of the numerical instability resulting from the tight spacing of the grid in the axial direction as the stagnation point is approached. Applying the developed analysis approach to a peak heating EES case results in a roughness augmentation factor for the convective heating of 1.32, which agrees well with correlations that predict values between 1.30 and 1.37. Analysis approach developed in this work provides the framework for future studies of turbulence modeling impacts and the influence of an ablating surface.

Earth Entry System↗

Stokes resolved differential temperature: an important metric of polarimetric precision in the long-wave infrared

In remote sensing, radiometric measurements taken in the mid-wave infrared and beyond (λ >3μm) are commonly reported in units of Kelvin by utilizing Planck’s radiation law to relate measured radiance and target brightness temperature (Tb). Thus, it is desirable to match this formalism in thermal polarimetry and report the unnormalized Stokes parameters in units of K instead of radiance (Wm−2sr−1). This approach also al-lows common performance metrics in long-wave infrared (LWIR) imaging such as Noise Equivalent DifferentialTemperature (NEDT) to be modified and extended to metrics of polarimetric accuracy and precision. However, since the relationship between measured radiance and Tb are non-linear, the conversion of I, Q, and U in units of radiance to Tb,Tb,Q, and Tb,U in K is ambiguous. As a solution a metric of performance for thermal linear Stokes polarimetry, the Stokes resolved differential temperature (SRDT), is introduced.

polarimetry↗

ArcjetCV: A New Machine Learning Application for Extracting Time-Resolved Recession Measurements From Arc Jet Test Videos

Arc jet Computer Vision (ArcjetCV) is a software application built to automate analysis of arc jet ground test video footage. This includes tracking material recession, sting arm motion, and the shock-material standoff distance. This provides a new capability to resolve and validate new physics associated with non-linear processes. This is an essential step to reduce testing, modeling, and validation uncertainties for heatshield material performance. ArcjetCV uses several types of machine learning (convolutional neural net: CNN, decision tree: DT, k-means unsupervised clustering: KM) to automate the video processing pipeline. These include inferring the start/stop of time segments of interest (1D CNN), measuring the time-dependent 2D recession of the material samples (2D CNN, DT), measuring the time-dependent shock standoff distance (2D CNN, DT), and post-processing cleaning of the recession data (KM). The software also provides a graphical user interface for ease of use. The results of using this tool on arc jet videos show non-linear time-dependent effects can be important for certain materials.

machine learning↗

Spectrally Resolving High Redshift Dual AGNs with the Line Emission Mapper

The Line Emission Mapper (LEM) is an X-ray Probe-class Mission concept which combines 1-2 eV spectral resolution in the soft X-ray band (0.2-2 keV) with an effective area of ~2600 cm^2 at 1 keV, and 10’’ HPD over a large 30'x30' field of view. The mission will directly address the Astro2020 Decadal Report’s Priority Area of Unveiling the Drivers of Galaxy Growth through the study of merging galaxies, which offer one of the most dramatic channels for galaxy growth and evolution and can potentially trigger both star formation and supermassive black hole (SMBH) growth. Dual and binary AGNs in late-stage mergers are predicted to be a critical stage of merger-induced SMBH growth and coincide with the most transformative period for the host galaxies. Thus, dual and binary AGNs represent ideal laboratories for studying examples of SMBH-galaxy co-evolution. Recent discoveries of dual AGNs at redshifts of z~>2 have been made via optical observations, but such dual AGNs are inaccessible to current X-ray observatories due to a lack of instrumental capabilities, including sensitivity, spectral and spatial resolution. LEM will be uniquely suited to study AGNs at z > 3, where the intrinsic X-ray power law continuum and Iron K alpha emission line - a nearly ubiquitous signpost for AGN accretion – are redshifted into the LEM bandpass. In this poster, we present a simulation-based study on LEM’s ability to discern dual, binary, and/or clustered AGNs at high redshift. We demonstrate the unique capabilities of LEM to spectrally resolve Iron K-alpha lines emitted by distinct AGNs with sufficient line-of-sight velocity differences, even in cases of convolved point sources. Using a suite of simulations probing a range of velocity differences, line strengths, and column densities, we demonstrate LEM’s ability to constrain the spectral properties of dual AGNs at z>3 for a variety of physically realistic pairings. LEM will uniquely provide crucial constraints on dual AGN environments and activity as a function of redshift and will complement optical and infrared observations from upcoming Extremely Large Telescopes.

Ryan W Pfeifle↗

A SmallSat Concept to Resolve Diurnal and Vertical Variations of Aerosols, Clouds, and Boundary Layer Height

A SmallSat mission concept is formulated here to carry out Time-varying Optical Measurements of Clouds and Aerosol Transport (TOMCAT) from space while embracing low-cost opportunities enabled by the revolution in Earth science observation technologies. TOMCAT’s “around-the-clock” measurements will provide needed insights and strong synergy with existing Earth observation satellites to 1) statistically resolve diurnal and vertical variation of cirrus cloud properties (key to Earth’s radiation budget), 2) determine the impacts of regional and seasonal planetary boundary layer (PBL) diurnal variation on surface air quality and low-level cloud distributions, and 3) characterize smoke and dust emission processes impacting their long-range transport on the subseasonal to seasonal time scales. Clouds, aerosol particles, and the PBL play critical roles in Earth’s climate system at multiple spatiotemporal scales. Yet their vertical variations as a function of local time are poorly measured from space. Active sensors for profiling the atmosphere typically utilize sun-synchronous low-Earth orbits (LEO) with rather limited temporal and spatial coverage, inhibiting the characterization of spatiotemporal variability. Pairing compact active lidar and passive multiangle remote sensing technologies from an inclined LEO platform enables measurements of the diurnal and vertical variability of aerosols, clouds, and aerosol-mixing-layer (or PBL) height in tropical-to-midlatitude regions where most of the world’s population resides. TOMCAT is conceived to bring potential societal benefits by delivering its data products in near–real time and offering on-demand hazard-monitoring capabilities to profile fire injection of smoke particles, the frontal lofting of dust particles, and the eruptive rise of volcanic plumes.

John E. Yorks↗

ArcjetCV: A New Machine Learning Application for Extracting Time-Resolved Recession Measurements From Arc Jet Test Videos

Arc jet Computer Vision (ArcjetCV) is a software application built to automate analysis of arc jet ground test video footage. This includes tracking material recession and the shock-material standoff distance. This provides a new capability to resolve and validate new physics associated with non-linear processes. This is an essential step to reduce testing, modeling, and validation uncertainties for heatshield material performance. ArcjetCV uses several types of machine learning (convolutional neural net: CNN, decision tree: DT, k-means unsupervised clustering: KM) to automate the video processing pipeline. These include inferring the start/stop of time segments of interest (1D CNN), measuring the time-dependent 2D recession of the material samples (2D CNN, DT), measuring the time-dependent shock standoff distance (2D CNN, DT), and post-processing cleaning of the recession data (KM). The software also provides a graphical user interface for ease of use. The results of using this tool on arc jet videos show non-linear time-dependent effects can be important for certain materials and characterizing certain failure modes.

machine learning↗

arcjetCV: A New Machine Learning Application for Extracting Time-Resolved Recession Measurements From Arc Jet Test Videos

Arc jet Computer Vision (ArcjetCV) is a software application built to automate analysis of arc jet ground test video footage. This includes tracking material recession and the shock-material standoff distance. This provides a new capability to resolve and validate new physics associated with non-linear processes. This is an essential step to reduce testing, modeling, and validation uncertainties for heatshield material performance. ArcjetCV uses several types of machine learning (convolutional neural net: CNN, decision tree: DT, k-means unsupervised clustering: KM) to automate the video processing pipeline. These include inferring the start/stop of time segments of interest (1D CNN), measuring the time-dependent 2D recession of the material samples (2D CNN, DT), measuring the time-dependent shock standoff distance (2D CNN, DT), and post-processing cleaning of the recession data (KM). The software also provides a graphical user interface for ease of use. The results of using this tool on arc jet videos show non-linear time-dependent effects can be important for certain materials and characterizing certain failure modes.

machine learning↗

Insights Into the Size-Resolved Dust Emission From Field Measurements in the Moroccan Sahara

The particle size distribution (PSD) of mineral dust has a strong effect on the impacts of dust on climate. However, our understanding of the emitted dust PSD, including its variability and the fraction of super-coarse dust (diameter >10 µm), remains limited. Here, we provide new insights into the size-resolved dust emission process based on a field campaign performed in the Moroccan Sahara in September 2019 in the context of the FRontiers in dust minerAloGical coMposition and its Effects upoN climaTe (FRAGMENT) project. The obtained dust concentration and diffusive flux PSDs show significant dependencies upon the friction velocity (u*), wind direction and type of event (regular events versus haboob events). For instance, the number fraction of sub-micrometre particles increases with u*, along with a large decrease in the mass fraction of super-coarse dust. We identify dry deposition, which is modulated by u* and fetch length, as a potential cause for this PSD variability. Using a resistance model constrained with field observations to estimate the dry deposition flux and thereby also the emitted dust flux, we show that deposition could represent up to ∼90 % of the emission of super-coarse particles (>10 µm) and up to ∼65 % of the emission of particles as small as ∼5 µm in diameter. Importantly, removing the deposition component significantly reduces the variability with u* in the PSD of the emitted dust flux compared with the diffusive flux, particularly for super-coarse dust. The differences between regular and haboob event concentration and diffusive flux PSDs are suspected to result from a smaller and variable dust source fetch during the haboob events, and/or an increased resistance of soil aggregates to fragmentation associated with the observed increase in relative humidity along the haboob outflow. Finally, compared to the invariant emitted dust flux PSD estimated based on brittle fragmentation theory, we obtain a substantially higher proportion of super-micrometre particles in the dust flux. Overall, our results suggest that dry deposition needs to be adequately considered to estimate the emitted PSD, even in studies limited to the fine and coarse size ranges (<10 µm).

particle size distribution↗

Scale Resolving Simulations of Viking '75 Reentry Capsule Wake Flow

Scale resolving simulations of static wake tests of the '75 Viking capsule have been performed using two unstructured grid flow solvers, US3D and PyFR. Simulations with several resolutions have been performed at varying Mach numbers with the aim of solver validation for capsule wake flows as well as establishing simulation best practices.

Rathakrishnan Bhaskaran↗

Highly Resolved Mapping of NO 2 Vertical Column Densities From GeoTASO Measurements Over a Megacity and Industrial Area During the KORUS-AQ Campaign

The Korea–United States Air Quality (KORUS-AQ) campaign is a joint study between the United States National Aeronautics and Space Administration (NASA) and the South Korea National Institute of Environmental Research (NIER) to monitor megacity and transboundary air pollution around the Korean Peninsula using airborne and ground-based measurements. Here, tropospheric nitrogen dioxide (NO 2 ) slant column density (SCD) measurements were retrieved from Geostationary Trace and Aerosol Sensor Optimization (GeoTASO) L1B data during the KORUS-AQ campaign (2 May to 10 June 2016). The retrieved SCDs were converted to tropospheric vertical column densities using the air mass factor (AMF) obtained from a radiative transfer calculation with trace gas profiles and aerosol property inputs simulated with the Community Multiscale Air Quality (CMAQ) model and surface reflectance data obtained from the Moderate Resolution Imaging Spectroradiometer (MODIS). For the first time, we examine highly resolved (250 m × 250 m resolution) tropospheric NO 2 over the Seoul and Busan metropolitan regions and the industrial region of Anmyeon. We reveal that the maximum NO 2 vertical column densities (VCDs) were 4.94×10 16 and 1.46×10 17 molec. cm −2 at 09:00 and 15:00 LT over Seoul, respectively, 6.86×10 16 and 4.89×10 16 molec. cm −2 in the morning and afternoon over Busan, respectively, and 1.64×10 16 molec. cm −2 over Anmyeon. The VCDs retrieved from the GeoTASO airborne instrument were correlated with those obtained from the Ozone Monitoring Instrument (OMI) (r=0.48), NASA's Pandora Spectrometer System (r=0.91), and NO 2 mixing ratios obtained from in situ measurements (r=0.07 in the morning, r=0.26 in the afternoon over the Seoul, and r>0.56 over Busan). Based on our results, GeoTASO is useful for identifying NO 2 hotspots and their spatial distribution in highly populated cities and industrial areas.

Gyo-Hwang Choo↗

Scale Resolving Simulations of Contra Rotating Open Rotor Noise Prediction

The development of breakthrough open-rotor propulsion systems that meet performance, fuel efficiency, and noise requirements for commercial viability relies heavily on the availability of reliable and computationally efficient aerodynamic performance and noise prediction tools.This investigation showcases the proficiency of the structured curvilinear overset grid Computational Fluid Dynamics solver implemented in the Launch, Ascent, and Vehicle Aerodynamics framework for predicting the aerodynamic and aeroacoustic characteristics of a Contra-RotatingOpen Rotor propulsion system. Additionally, the study includes comprehensive sensitivity analyses employing a Hybrid Reynolds-Averaged Navier-Stokes/Large Eddy Simulation - based scale-resolving turbulence closure treatment. The computed results are validated through comparison with existing experimental measurements.

ARMD↗

Time-Resolved OH-PLIF Assessment of Deflagrations Levels in a CH4-O2 Rotating Detonation Rocket Combustor

The parasitic loss incurred by deflagrative pre-burning is considered to be one of the key challenges to the effective implementation of rotating detonation engine (RDE) systems. Thus far, there have been relatively limited high fidelity, spatio-temporally resolved measurements of the pre-burning process as a function of the reactant conditions and composition within an RDE. In this work, simultaneous high-repetition-rate broadband OH* chemiluminescence and OH planar laser-induced fluorescence (PLIF) imaging are employed to investigate deflagrative burning dynamics in a fully optically accessible CH4-O2 RDE as compared to a H2-air systems under the same conditions. A custom-built optical parametric oscillator (OPO) is coupled with a high-repetition-rate burst-mode laser to generate the 284 nm source for the excitation of the Q1(9) transition of the OH radical. Significant deflagrative burning is observed throughout the chamber as a consequence of the oxygen-rich environment. Trailing wave systems that consume unburned reactants in the region immediately following the primary detonation wave are observed. The formation of a product gas recirculation zone that entrains and combusts incoming reactants is observed, and quantitative analysis is performed to gain valuable insight into deflagration characteristics. At an oxidizer mass flux of Gox ≈ 350kg/m2/s, it is found that the levels of pre-wave burning in CH4-O2 are a factor of ∼2-3 times higher than in H2-air in the region directly ahead of the detonation wave at a global equivalence ratio Φ ≈ 1.0. This study highlights the key roles of ignition delay and turbulent mixing of the combustion product and reactant fields on the levels of pre-burning and establishes a methodology for further investigation, such as for the effects of various RDE inlet configurations.

Propulsion↗