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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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NASA NTRS2017

GES DISC Greenhouse Gas Data Sets and Associated Services

NASA Goddard Earth Sciences (GES) Data and Information Services Center (DISC) archives and distributes rich collections of data on atmospheric greenhouse gases from multiple missions. Hosted data include those from the Atmospheric Infrared Sounder (AIRS) mission (which has observed CO2, CH4, ozone, and water vapor since 2002); legacy water vapor and ozone retrievals from TIROS Operational Vertical Sounder (TOVS); and Upper Atmosphere Research Satellite (UARS) going back to the early 1980s. GES DISC also archives and supports data from seven projects of the Making Earth System Data Records for Use in Research Environments (MEaSUREs) program that have ozone and water vapor records. Greenhouse gases data from the A-Train satellite constellation is also available: (1) Aura-Ozone Monitoring Instrument (OMI) and Microwave Limb Sounder (MLS) ozone, nitrous oxide, and water vapor since 2004; (2) Greenhouse Gases Observing Satellite (GOSAT) CO2 observations since 2009 from the Atmospheric CO2 Observations from Space (ACOS) task; and (3) Orbiting Carbon Observatory-2 (OCO-2) CO2 data since 2014. The most recent related data set that the GES DISC archives is methane flux for North America, as part of NASAs Carbon Monitoring System (CMS) project. This dataset contains estimates of methane emission in North America based on an inversion of the GEOS-Chem chemical transport model constrained by GOSAT observations (Turner et al., 2015). Along with data stewardship, an important focus area of the GES DISC is to enhance the usability of its data and broaden its user base. Users have unrestricted access to a new user-friendly search interface, which includes many services such as variable subsetting, format conversion, quality screening, and quick browse. The majority of the GES DISC data sets are also accessible through Open-source Project for a Network Data Access Protocol (OPeNDAP) and Web Coverage Service (WCS). The latter two services provide more options for specialized subsetting, format conversion, and image viewing. Additional data exploration, data preview, and preliminary analysis capabilities are available via NASA Giovanni, which obviates the need forusers to download the data (Acker and Leptoukh, 2007). Giovanni provides a bridge between the data and science and has been very successful in extending GES DISC data to educational users and to users with limited resources.

data ordering↗
NASA NTRS2016

The NANOGrav Nine-Year Data Set: Limits on the Isotropic Stochastic Gravitational Wave Background

We compute upper limits on the nanohertz-frequency isotropic stochastic gravitational wave background (GWB) using the 9 year data set from the North American Nanohertz Observatory for Gravitational Waves (NANOGrav) collaboration. Well-tested Bayesian techniques are used to set upper limits on the dimensionless strain amplitude (at a frequency of 1 yr(exp -1) for a GWB from supermassive black hole binaries of A(sub gw) less than 1.5 x 10(exp -15). We also parameterize the GWB spectrum with a broken power-law model by placing priors on the strain amplitude derived from simulations of Sesana and McWilliams et al. Using Bayesian model selection we find that the data favor a broken power law to a pure power law with odds ratios of 2.2 and 22 to one for the Sesana and McWilliams prior models, respectively. Using the broken power-law analysis we construct posterior distributions on environmental factors that drive the binary to the GW-driven regime including the stellar mass density for stellar-scattering, mass accretion rate for circumbinary disk interaction, and orbital eccentricity for eccentric binaries, marking the first time that the shape of the GWB spectrum has been used to make astrophysical inferences. Returning to a power-law model, we place stringent limits on the energy density of relic GWs, OMEGA(sub gw) (f) h squared less than 4.2 x 10(exp -10). Our limit on the cosmic string GWB, OMEGA(sub gw) (f) h squared less than 2.2 x 10(exp -10), translates to a conservative limit on the cosmic string tension with G mu less than 3.3 x 10(exp -8), a factor of four better than the joint Planck and high-l‚ cosmic microwave background data from other experiments.

Arzoumanian, Z.↗
NASA NTRS2018

Description of Simulated Small Satellite Operation Data Sets

A set of two BP930 batteries (Identified as PK31 and PK35) were operated continuously for a simulated satellite operation profile completion for single cycle. The battery packs were charged to an initial voltage of around 8.35 V for 100% SOC before the experiment was started. This document explains the structure of the battery data sets. Please cite this paper when using this dataset: Z. Cameron, C. Kulkarni, A. Guarneros, K. Goebel, S. Poll, "A Battery Certification Testbed for Small Satellite Missions", IEEE AUTOTESTCON 2015, Nov 2-5, 2015, National Harbor, MA

Kulkarni, Chetan S.↗
NASA NTRS2018

Impact of Different Trajectory Option Set Participation Levels within an Air Traffic Management Collaborative Trajectory Option Program

This paper presents the methodology and results of a Human-In-The-Loop (HITL) simulation study conducted in the Airspace Operations Laboratory at NASA Ames Research Center. This study is a part of NASA's ongoing research into developing an Integrated Demand Management (IDM) concept, whose aim is to improve traffic flow management (TFM) by coordinating the FAA's strategic Traffic Flow Management System (TFMS) with its more tactical Time-Based Flow Management (TBFM) system. The purpose of TFM is to regulate air traffic demand so that it is delivered efficiently through constrained airspace resources without exceeding their capacity limits. The IDM concept leverages a new TFMS capability called the Collaborative Trajectory Options Program (CTOP) to strategically pre-condition traffic demand flowing into a TBFM-managed arrival environment, where TBFM is responsible for managing traffic tactically by generating precise arrival schedules. Unlike other TFM tools, CTOP gives flight operators the option of submitting a set of user-preferred alternative trajectories for each flight. CTOP can then use these trajectory option sets (or TOSs) to find user-preferred alternative routes to reduce demand on an overloaded resource. CTOP's effectiveness in redistributing demand is limited, however, by the availability of flights with alternative routes. The research presented in this paper focuses on evaluating the impact on TFM operations by varying the percentage of flights that submit a multiple-option TOS ('TOS participation levels'). Results show the impact on overall system performance and on the rerouted flights themselves. The simulation used a Newark (EWR) airport arrival scenario, with en route weather affecting traffic inbound from the west. Participants were asked to control each of the three arrival flows (north, west, and south) to meet their individual capacity constraints while simultaneously ensuring efficient utilization of the capacity at the destination airport. A large, permeable convective weather cell located southeast of Chicago severely reduced the capacity of the west flow. The study evaluated the impact of five different TOS participation levels on CTOP's ability to re-allocate traffic from the west and improve TFM performance in terms of delay assignment and traffic delivery rate to the airport. Overall, the results showed that increasing TOS submissions allowed the overall system delays to be reduced and fairly distributed among the three arrival flows, at the same time achieving the airport throughput rate. Moreover, it was found that aircraft who submitted a TOS saw a greater reduction in delay, even when they were assigned longer routes. This was particularly true when fewer aircraft submitted a TOS. The results confirm that the CTOP operations with higher TOS participation levels helped utilize the overall National Airspace System (NAS) resources as well as benefited the users who participated.

Yoo, Hyo-Sang↗
NASA NTRS2020

Impact of Bias in the Marine Air Temperature Observation Set on Atmospheric Reanalyses

Marine air temperature observations are collected by volunteer observing ships carrying automatic meteorological sensor packages. Close proximity of the package mount to a dark colored ship's deck is known to introduce a warm bias on sunny afternoons in summer under low wind conditions. This observation set is used as an influential constraint in the MERRA-2 reanalysis, as well as several other reanalyses, in the poorly sampled surface marine boundary layer. Here we explore the consequences of bias in the marine air temperature observation set during the 36 year period 1980-2015. Marine air temperature observations have mainly been collected along major shipping routes, with the densest coverage in the North Atlantic and North Pacific. In the first part of this presentation we review the historical observation coverage and compare air temperature and contemporaneous SST measurements to identify systematic differences. The comparison suggests the presence of diurnal bias in the air temperature observations peaking in subtropics and midlatitudes latitudes in mid-afternoon local time. In the second part we explore the impact of the marine air temperature observations on MERRA-2 through analysis of the terms in the temperature forecast tendency equation, the equation which describes the contributions to changes in temperature during each analysis cycle. Examination of the temperature forecast tendency equation allows us to separate systematic errors due to such processes and radiation, mixing, and advection and allows us to identify the major source of systematic forecast tendencies. The results show that the marine air temperature measurements introduce an error that can reach 0.4K/dy which also peaks in local afternoon at subtropical latitudes. This bias varies with season and has gradually reduced from the beginning of the MERRA-2 reanalysis in 1980. In the final part we conduct numerical experiments with a version of the MERRA-2 analysis system, comparing experiments when the marine air temperature observations are included or withheld from the analysis system.

Akella, Santharam↗
NASA NTRS2020

Hover Validation and Acoustic Baseline Blade Set

The Hover Validation and Acoustic Baseline (HVAB) blade set has been jointly developed by the U.S. Army Combat Capabilities Development Command Aviation & Missile Center (CCDC AvMC) and the NASA Revolutionary Vertical Lift Technology (RVLT) Project. This Mach-scale, 66.50 in radius, blade set will ultimately be tested in both hover and forward-flight to provide key data for analysis validation. This paper provides comprehensive detail of the blade geometry, instrumentation, and structure for use in future analyses.

Hover,Rotor,Helicopter,Aerodynamics,HVAB,PSP,AIAA ↗
NASA NTRSDate not supplied

Building Capacity for Policy-makers in a Virtual Setting: Providing Tools to Analyze Wildfire Smoke Plumes and Their Impacts

The NASA DEVELOP Program conducted 10-week long feasibility projects in a remote work setting, including partnering with The Nature Conservancy’s Washington Chapter and the Puget Sound Clean Air Agency to investigate wildfire smoke from 2000 - 2020 in the Pacific Northwest using satellite-derived data. The team engaged with platforms for collaboration both internally with NASA affiliates and externally with community organizations. Working from multiple states, the team members used a variety of software including Google Meet, Microsoft Teams, and Google Earth Engine to foster communication and work with data in a shared virtual environment. Throughout the project, the team learned that executing the project in a distanced work setting made it easier to reach out to scientists across the country for expertise and guidance. To study changes in air quality resulting from wildfire smoke, the team utilized data from NASA’s Fire Information from Resource Management System (FIRMS) and the ESA’s Sentinel-5 TROPOspheric Monitoring Instrument (TROPOMI). The team created a Google Earth Engine web-based tool, “Plume Hazards and Observations of Emissions by Navigating an Interactive eXplorer” (PHOENIX), to visualize changes in pollutants and aerosol optical depth after fire events. The potential relationship between plume height and fire radiative power was evaluated by using NASA’s Moderate Resolution Imaging Spectroradiometer (MODIS) aboard Aqua and Terra satellites and NASA’s Multi-angle Imaging SpectroRadiometer (MISR) aboard Terra with the MISR INteractive eXplorer (MINX). The PHOENIX smoke assessment tool and science communication infographics will be shared electronically with the partner organizations. Furthermore, the team introduced the partners to MINX and will provide a tailored tutorial that included a recorded video and a written component with a live virtual workshop. These resources build capacity for further research and education on wildfire smoke and air quality within communities.

NASA DEVELOP↗
NASA NTRS2001

InP MMIC chip set for power sources covering 86-170 GHz

We will present a Monolithic Millimeter-wave Integrated Circuit (MMIC) chip set which provides high output-power sources for driving diode frequency multipliers into the terahertz range. The chip set was fabricated at HRL Laboratories using a 0.l-μm gatelength InAI/InGaAs/InP high electron mobility transistor (HEMT) process, and features transistors with an fmax above 600 GHz. The HRL InP HEMT process has already demonstrated amplifiers in the 60-200 GHz range. In this paper, these high frequency HEMTs form the basis for power sources up to 170 GHz.

Ngo, Catherine↗
NASA NTRS2020

The NANOGrav 11 yr Data Set: Contraints on Planetary Masses Around 45 Millisecond Pulsars

We search for extrasolar planets around millisecond pulsars using pulsar timing data and seek to determine the minimum detectable planetary masses as a function of orbital period. Using the 11 yr data set from the North American Nanohertz Observatory for Gravitational Waves (NANOGrav), we look for variations from our models of pulse arrival times due to the presence of exoplanets. No planets are detected around the millisecond pulsars in the NANOGrav 11 yr data set, but taking into consideration the noise levels of each pulsar and the sampling rate of our observations, we develop limits that show we are sensitive to planetary masses as low as that of the moon. We analyzed potential planet periods, P, in the range 7 days < P < 2000 days, with somewhat smaller ranges for some binary pulsars. The planetary-mass limit for our median-sensitivity pulsar within this period range is 1 Mmoon (P 100 days)-2/3.

E A Behrens↗
NASA NTRS2019

Constraining the Neutron Star Mass–Radius Relation and Dense Matter Equation of State with NICER. I. The Millisecond Pulsar X-Ray Data Set

We present the set of deep Neutron Star Interior Composition Explorer (NICER) X-ray timing observations of the nearby rotation-powered millisecond pulsars PSRs J0437−4715, J0030+0451, J1231−1411, and J2124−3358, selected as targets for constraining the mass–radius relation of neutron stars and the dense matter equation of state (EoS) via modeling of their pulsed thermal X-ray emission. We describe the instrument, observations, and data processing/reduction procedures, as well as the series of investigations conducted to ensure that the properties of the data sets are suitable for parameter estimation analyses to produce reliable constraints on the neutron star mass–radius relation and the dense matter EoS. We find that the long-term timing and flux behavior and the Fourier domain properties of the event data do not exhibit any anomalies that could adversely affect the intended measurements. From phase-selected spectroscopy, we find that emission from the individual pulse peaks is well described by a single-temperature hydrogen atmosphere spectrum, with the exception of PSR J0437−4715, for which multiple temperatures are required.

Neutron stars↗
NASA NTRS2021

Evaluation of Global Surface Water Temperature Data Sets for use in Passive Remote Sensing of Soil Moisture

Inland open water bodies often pose a systematic error source in the passive remote sensing retrievals of soil moisture. Water temperature is a necessary variable used to compute water emissions that is required to be subtracted from satellite observation to yield actual emissions from the land portion, which in turn generates accurate soil moisture retrievals. Therefore, overestimation of soil moisture can often be corrected using concurrent water temperature data in the overall mitigation procedure. In recent years, several data sets of lake water temperature have become available, but their specifications and accuracy have rarely been investigated in the context of passive soil moisture remote sensing on a global scale. For this reason, three lake temperature products were evaluated against in-situ measurements from 2007 to 2011. The data sets include the lake surface water temperature (LSWT) from Global Observatory of Lake Responses to Environmental Change (GloboLakes), the Copernicus Global Land Operations Cryosphere and Water (C-GLOPS), as well as the lake mix-layer temperature (LMLT) from the European Centers for Medium-Range Weather Forecast (ECMWF) ERA5 Land Reanalysis. GloboLakes, C-GLOPS, and ERA5 Land have overall comparable performance with Pearson correlations (R) of 0.87, 0.92 and 0.88 in comparison with in-situ measurements. LSWT products exhibit negative median biases of −0.27 K (GloboLakes) and −0.31 K (C-GLOPS), whereas the median bias of LMLT is 1.56 K. When mapped from their respective native resolutions to a common 9 km Equal-Area Scalable Earth (EASE) Grid 2.0 projection, similar relative performance was observed. LMLT and LSWT data are closer in performance over the 9 km grid cells that exhibit a small range of lake cover fractions (0.05–0.5). Despite comparable relative performance, ERA5 Land shows great advantages in spatial coverage and temporal resolution. In summary, an integrated evaluation on data accuracy, long-term availability, global coverage, temporal resolution, and regular forward processing with modest data latency led us to conclude that LMLT from the ERA5 Land Reanalysis product represents the most optimal path for use in the development of a long-term soil moisture product.

Runze Zhang↗
NASA NTRSDate not supplied

Improving Computational Efficiency of Prognostics Algorithms in Resource-Constrained Settings

In engineering and aerospace applications, it is vital to operational success to have insight into the expected performance and health of physical systems. The field of prognostics and health management provides quantitative methods for monitoring, predicting, and managing system health. Prognostics algorithms can be employed to assess the current state of a system, propagate the system throughout time, and predict potential anomalies or failures that may occur. While they can provide accurate prediction results, effective prognostics algorithms can be challenging to use in resource-constrained settings due to computational limitations and high computational latency, leading to obsolete predictions. Thus, computationally efficient and accurate algorithms are necessary for future remaining useful life predictions. In this work, we implement new algorithmic approaches for prediction, quantitatively compare them via a battery degradation use-case, and provide recommendations of potential improvements to a prognostics framework. One approach to prediction is through sampling, whereby the current state of a physical system is sampled many times and each sample is propagated forward until failure is reached, resulting in a distribution of failure values. To improve the efficiency of this process, we implemented five new algorithmic approaches to prediction, including three distinct sampling methods (standard Monte Carlo, Quasi-Monte Carlo, and Latin Hypercube Sampling), a variable time step algorithm, and a variable sample size algorithm. To compare the algorithms, we employ a variety of metrics designed specifically to analyze both computational efficiency and model accuracy. Our metrics include accuracy to compare the average predicted value to ground truth, mean absolute deviation to illustrate dispersion, specific percentile error to describe accuracy within a user-defined risk tolerance, and code run-time. To quantitatively analyze our results, we employ a use-case of degradation of a Lithium-ion battery. We use an electrochemistry-based model to describe the current health state of the battery, and implement our prediction algorithms to propagate forward in time until end-of-discharge (EOD) is reached. Notably, through this work it was found that none of our sampling approaches had a significant impact on computational efficiency or model accuracy in predicting EOD of the battery. We find that while the sampling methods are unique, the distributions they generate are similar, ultimately producing final predictions that are nearly identical. In exploring the effect of the time step within the prediction algorithm, we found that prediction accuracy was highly dependent on the time step used, and that implementing a variable time step within a particular prediction may provide an increase in computational efficiency while also maintaining prediction accuracy. Finally, implementing a variable sample size also affected prediction, and our results show that tuning both the magnitude and timing of the sample size adjustment can result in improved computation speed and maintained prediction accuracy. Taken together, our findings highlight the challenge of performing prognostics in resource-constrained settings, and illustrate the potential of developing new prediction algorithms to improve computational efficiency.

prognostics↗
NASA NTRSDate not supplied

Improving Computational Efficiency of Prognostics Algorithms in Resource-Constrained Settings

The field of prognostics and health management provides quantitative methods for monitoring and predicting the health of physical systems. Prognostics algorithms are useful in that they can be employed to assess the current state of a system, propagate the system state throughout time, and predict potential anomalies or failures that may occur. However, effective prognosis can be challenging to achieve in resource-constrained settings due to computational limitations and high computational latency, leading to obsolete predictions. Thus, computationally efficient and accurate algorithms are necessary for some prognostics applications. In this work, we implement three new algorithmic approaches to prediction (sampling methods, variable prediction time step, variable prediction sample size) with the goal of improving computational efficiency while minimizing decrease in model accuracy. To quantitatively analyze our results, we examine a use-case of degradation of a Lithium-ion battery. Notably, through this work it was found that none of the sampling approaches had a significant impact on computational efficiency or model accuracy in predicting EOD of the battery. However, our results show that prediction accuracy is highly dependent on the time step used, and that an appropriate time step can optimize both model accuracy and simulation efficiency. Finally, implementing a variable sample size also affected prediction, and our results show that tuning both the magnitude and timing of the sample size adjustment in an application-specific manner may prove useful in some applications. Taken together, our findings highlight the challenge of performing prognostics in resource-constrained settings, and illustrate the potential of developing new prediction algorithms to improve computational efficiency of prognosis.

Prognostics↗
NASA NTRSDate not supplied

The Impact of Mood and Affect on Operationally Relevant Performance Among Astronaut-Like Individuals in Space Analog Settings

Crewmembers of future space exploration missions will face many challenges associated with living and working in extreme conditions for prolonged periods of time. Thus, it is imperative to understand how spaceflight hazards impact mood and affect, cognitive functioning, and ultimately, operational performance. The Human Factors and Behavioral Performance Exploration Measures (HFBP-EM) suite is a set of standardized measures used to assess behavioral health and performance related to future exploration class space missions. HFBP-EM includes the Profile of Mood States (POMS) as a self-report measure of psychological distress, and the Robotic On-Board Trainer (ROBoT) as an operationally relevant task that simulates the capture and grappling maneuvers required to operate the Canadarm2 on the International Space Station. Although previous space analog studies have demonstrated fluctuations in mood and affect ratings assessed by the POMS (Basner, 2014), no studies to date have examined impacts on operationally relevant tasks. Here we investigate the relationship between POMS ratings and performance on the ROBoT task in space analog settings.

S I Dev↗
NASA NTRS2022

Model Predictive Control in the Three-body Problem Using Invariant Funnels As Terminal Sets

This paper describes a method for augmenting Model Predictive Control techniques using invariant funnels computed in the Circular Restricted Three-Body Problem. We use ellipsoids that roughly approximate the boundary of the invariant funnel as convex terminal sets for a short look-ahead optimization problem at each time step. We apply this method to a hypothetical low-thrust mission to land on Jupiter’s mooon Europa and show that including the invariant funnels as terminal sets reduces the amount of control effort required by almost an order of magnitude.

Close, Sigrid↗
NASA NTRSDate not supplied

Medical Resource Set Bulky Item Trade Space Analysis for Spaceflight Medical Risk

The NASA engineering community utilizes event-driven and fault-tree probabilistic techniques to classify risks in the space environment by taking advantage of the inherent knowledge of complex spaceflight system design and testing to quantify failure risk. In harmonizing the risk of human space flight, answering the question of ‘How do we balance health, performance and resource risks with other engineering risks on long duration space missions?’ remains a deeply challenging and largely qualitative practice. The Medical Extensible Dynamic Probabilistic Risk Assessment Tool (MEDPRAT) is one aspect of the efforts by NASA’s Human Research Program (HRP) to quantitatively assess the impact of health and performance risk. One of MEDPRAT’s key features is its high degree of computational efficiency. Coupled with the HRP High Performance Compute cluster located at NASA’s Glenn Research Center, MEDPRAT runs millions of simulated missions in a matter of minutes. This degree of computational efficiency provides the novel opportunity to explore the relationship between medical set mass, volume, and medical resource size. Of particular interest for future human spaceflight missions are ‘bulky’ items, medical resources like devices, which occupy a large portion of the small, allocated mass and volume for the medical set leaving less room for other resources. This talk will present results showing the quantitative impact of forced inclusion of several bulky items across a variety of medical kit constraints, and the effect that a potential research investment into reducing the bulky item mass and volume may have on risk.

Lauren Mcintyre↗
NASA NTRS2025

Assessing the Added Value of Miniature X-Ray in the Setting of Portable Ultrasound in Spaceflight

INTRODUCTION: Point-of-care ultrasound (POCUS) has become the standard of care for imaging diagnosis and management in low-Earth Orbit (LEO) spaceflight and it has long been hypothesized that POCUS will also be the standard of care for exploration spaceflight. However, like the trajectory for which ultrasound became more portable and user-friendly, the mass, volume, and power requirements of radiography devices for both diagnostic and therapeutic applications have also been dramatically reduced. This study seeks to determine the clinical utility and added value of miniature x-ray (XR) for the diagnosis and management of each of the 119 conditions within NASA Exploration Medical Capability’s IMPACT Condition List (ICL) given that the medical system is presumed to already be carrying a handheld portable POCUS device. METHODS: For each condition, a team of reviewers performed a rapid systematic literature review seeking sensitivity and specificity data for both handheld portable ultrasound and miniature XR. When there was a paucity of data, subject matter expertise and clinical experience was added to semi-quantitatively score the added value of miniature XR, given an US was already available for both diagnosis and management. Diagnostic utility of a modality for a condition was evaluated in the setting of both the best- and worst-case scenario definitions included and defined by the ICL. Evidence tracing and quality of evidence scores were also recorded. RESULTS/DISCUSSION: Conditions for which it was determined that miniature XR added diagnostic or therapeutic value are provided in this presentation. Previously presented work by our team has demonstrated that XR provides diagnostic and management capabilities that are hypothesized to complement or surpass ultrasound for over one-third of medical conditions that may arise during exploration spaceflight (i.e., diagnosis of injuries to the axial skeleton, teeth, and lungs as well as management of orthopedic reductions, endotracheal tube placement, and drain placement confirmation). In the setting of known inclusion of a handheld portable POCUS device, there remains significant added value of portable miniature XR. Whether or not this added clinical benefit is worth the mass, volume, and power requirements of the radiography system remains yet unknown and is the focus of future work. LEARNING OBJECTIVES: 1) Understand the value of ultrasound and radiography in the diagnosis and management of medical comorbidities that may arise in exploration spaceflight; 2) Understand the medical conditions of highest concern on exploration class missions for which miniature x-ray may provide added value to portable ultrasound.

J G Steller↗
NASA NTRS2024

SOFIA/HAWC+ Far-Infrared Polarimetric Large-area CMZ Exploration Survey. III. Full Survey Data Set

We present the second data release (DR2) of the Far-InfraREd Polarimetric Large-Area CMZ Exploration (FIREPLACE) survey. This survey utilized the Stratospheric Observatory for Infrared Astronomy High-resolution Airborne Wideband Camera plus instrument at 214 μm (E band) at a resolution of 19"6 to observe thermal polarized dust emission throughout the Central Molecular Zone (CMZ). DR2 consists of observations obtained in 2022 covering the region of the CMZ extending from the Brick to the Sgr C molecular clouds (corresponding to a 1° × 0°.75 region of the sky). We combine DR2 with the first FIREPLACE data release (DR1) to obtain full coverage of the CMZ (a 1°.5 × 0°.75 region of the sky). After applying total and polarized intensity significance cuts on the full FIREPLACE data set, we obtain ∼64,000 Nyquist-sampled polarization pseudovectors. The distribution of polarization pseudovectors confirms a bimodal distribution in the CMZ magnetic field orientations, recovering field components that are oriented predominantly parallel or perpendicular to the Galactic plane. This distribution of orientations is similar to what was observed in DR1 and other studies. We also inspect the magnetic fields toward a set of prominent CMZ molecular clouds (the Brick, Three Little Pigs, 50 and 20 km s −1 clouds, circumnuclear disk, CO 0.02-0.02, and Sgr C), revealing spatially varying magnetic fields having orientations that generally trace the total intensity morphologies of the clouds. We find evidence that compression from stellar winds and shear from tidal forces are prominent mechanisms influencing the structure of the magnetic fields.

Molecular clouds↗