Results for “information processing” — Page 86

NASA NTRS2016

Thermodynamic Spectrum of Solar Flares Based on SDO/EVE Observations: Techniques and First Results

The Solar Dynamics Observatory (SDO)/EUV Variability Experiment (EVE) provides rich information on the thermodynamic processes of solar activities, particularly on solar flares. Here, we develop a method to construct thermodynamic spectrum (TDS) charts based on the EVE spectral lines. This tool could potentially be useful for extreme ultraviolet (EUV) astronomy to learn about the eruptive activities on distant astronomical objects. Through several cases, we illustrate what we can learn from the TDS charts. Furthermore, we apply the TDS method to 74 flares equal to or greater than the M5.0 class, and reach the following statistical results. First, EUV peaks are always behind the soft X-ray (SXR) peaks and stronger flares tend to have faster cooling rates. There is a power-law correlation between the peak delay times and the cooling rates, suggesting a coherent cooling process of flares from SXR to EUV emissions. Second, there are two distinct temperature drift patterns, called Type I and Type II. For Type I flares, the enhanced emission drifts from high to low temperature like a quadrilateral, whereas for Type II flares the drift pattern looks like a triangle. Statistical analysis suggests that Type II flares are more impulsive than Type I flares. Third, for late-phase flares, the peak intensity ratio of the late phase to the main phase is roughly correlated with the flare class, and the flares with a strong late phase are all confined. We believe that the re-deposition of the energy carried by a flux rope, which unsuccessfully erupts out, into thermal emissions is responsible for the strong late phase found in a confined flare. Furthermore, we show the signatures of the flare thermodynamic process in the chromosphere and transition region in the TDS charts. These results provide new clues to advance our understanding of the thermodynamic processes of solar flares and associated solar eruptions, e.g., coronal mass ejections.

Wang, Yuming↗

NASA NTRS2017

Occurrence of Radio Minihalos in a Mass-Limited Sample of Galaxy Clusters

We investigate the occurrence of radio minihalos-diffuse radio sources of unknown origin observed in the cores of some galaxy clusters-in a statistical sample of 58 clusters drawn from the Planck Sunyaev-Zeldovich cluster catalog using a mass cut (M(sub 500) greater than 6 x 10(exp 14) solar mass). We supplement our statistical sample with a similarly sized nonstatistical sample mostly consisting of clusters in the ACCEPT X-ray catalog with suitable X-ray and radio data, which includes lower-mass clusters. Where necessary (for nine clusters), we reanalyzed the Very Large Array archival radio data to determine whether a minihalo is present. Our total sample includes all 28 currently known and recently discovered radio minihalos, including six candidates. We classify clusters as cool-core or non-cool-core according to the value of the specific entropy floor in the cluster center, rederived or newly derived from the Chandra X-ray density and temperature profiles where necessary (for 27 clusters). Contrary to the common wisdom that minihalos are rare, we find that almost all cool cores-at least 12 out of 15 (80%)-in our complete sample of massive clusters exhibit minihalos. The supplementary sample shows that the occurrence of minihalos may be lower in lower-mass cool-core clusters. No minihalos are found in non-cool cores or "warm cores." These findings will help test theories of the origin of minihalos and provide information on the physical processes and energetics of the cluster cores.

catalogs↗

NASA NTRS2017

Improvement of Automated POST Case Success Rate Using Support Vector Machines

During early conceptual design of complex systems, concept down selection can have a large impact upon program life-cycle cost. Therefore, any concepts selected during early design will inherently commit program costs and affect the overall probability of program success. For this reason it is important to consider as large a design space as possible in order to better inform the down selection process. For conceptual design of launch vehicles, trajectory analysis and optimization often presents the largest obstacle to evaluating large trade spaces. This is due to the sensitivity of the trajectory discipline to changes in all other aspects of the vehicle design. Small deltas in the performance of other subsystems can result in relatively large fluctuations in the ascent trajectory because the solution space is non-linear and multi-modal. In order to help capture large design spaces for new launch vehicles, the authors have performed previous work seeking to automate the execution of the industry standard tool, Program to Optimize Simulated Trajectories (POST). This work initially focused on implementation of analyst heuristics to enable closure of cases in an automated fashion, with the goal of applying the concepts of design of experiments (DOE) and surrogate modeling to enable near instantaneous throughput of vehicle cases.3 As noted in [4] work was then completed to improve the DOE process by utilizing a graph theory based approach to connect similar design points.

Zwack, Mathew R.↗

NASA NTRS2017

Improvement of Automated POST Case Success Rate Using Support Vector Machines

During early conceptual design of complex systems, concept down selection can have a large impact upon program life-cycle cost. Therefore, any concepts selected during early design will inherently commit program costs and affect the overall probability of program success. For this reason it is important to consider as large a design space as possible in order to better inform the down selection process. For conceptual design of launch vehicles, trajectory analysis and optimization often presents the largest obstacle to evaluating large trade spaces. This is due to the sensitivity of the trajectory discipline to changes in all other aspects of the vehicle design. Small deltas in the performance of other subsystems can result in relatively large fluctuations in the ascent trajectory because the solution space is non-linear and multi-modal [1]. In order to help capture large design spaces for new launch vehicles, the authors have performed previous work seeking to automate the execution of the industry standard tool, Program to Optimize Simulated Trajectories (POST). This work initially focused on implementation of analyst heuristics to enable closure of cases in an automated fashion, with the goal of applying the concepts of design of experiments (DOE) and surrogate modeling to enable near instantaneous throughput of vehicle cases [2]. Additional work was then completed to improve the DOE process by utilizing a graph theory based approach to connect similar design points [3]. The conclusion of the previous work illustrated the utility of the graph theory approach for completing a DOE through POST. However, this approach was still dependent upon the use of random repetitions to generate seed points for the graph. As noted in [3], only 8% of these random repetitions resulted in converged trajectories. This ultimately affects the ability of the random reps method to confidently approach the global optima for a given vehicle case in a reasonable amount of time. With only an 8% pass rate, tens or hundreds of thousands of reps may be needed to be confident that the best repetition is at least close to the global optima. However, typical design study time constraints require that fewer repetitions be attempted, sometimes resulting in seed points that have only a handful of successful completions. If a small number of successful repetitions are used to generate a seed point, the graph method may inherit some inaccuracies as it chains DOE cases from the non-global-optimal seed points. This creates inherent noise in the graph data, which can limit the accuracy of the resulting surrogate models. For this reason, the goal of this work is to improve the seed point generation method and ultimately the accuracy of the resulting POST surrogate model. The work focuses on increasing the case pass rate for seed point generation.

Zwack, Matthew R.↗

NASA NTRS2018

Medical Data Architecture Project Status

The Medical Data Architecture (MDA) project supports the Exploration Medical Capability (ExMC) risk to minimize or reduce the risk of adverse health outcomes and decrements in performance due to in-flight medical capabilities on human exploration missions. To mitigate this risk, the ExMC MDA project addresses the technical limitations identified in ExMC Gap Med 07: We do not have the capability to comprehensively process medically-relevant information to support medical operations during exploration missions. This gap identifies that the current in-flight medical data management includes a combination of data collection and distribution methods that are minimally integrated with on-board medical devices and systems. Furthermore, there are a variety of data sources and methods of data collection. For an exploration mission, the seamless management of such data will enable a more medically autonomous crew than the current paradigm. The medical system requirements are being developed in parallel with the exploration mission architecture and vehicle design. ExMC has recognized that in order to make informed decisions about a medical data architecture framework, current methods for medical data management must not only be understood, but an architecture must also be identified that provides the crew with actionable insight to medical conditions. This medical data architecture will provide the necessary functionality to address the challenges of executing a self-contained medical system that approaches crew health care delivery without assistance from ground support. Hence, the products supported by current prototype development will directly inform exploration medical system requirements.

medical data architecture↗

NASA NTRS2018

Medical Data Architecture (MDA) Project Status

The Medical Data Architecture (MDA) project supports the Exploration Medical Capability (ExMC) risk to minimize or reduce the risk of adverse health outcomes and decrements in performance due to in-flight medical capabilities on human exploration missions. To mitigate this risk, the ExMC MDA project addresses the technical limitations identified in ExMC Gap Med 07: We do not have the capability to comprehensively process medically-relevant information to support medical operations during exploration missions. This gap identifies that the current in-flight medical data management includes a combination of data collection and distribution methods that are minimally integrated with on-board medical devices and systems. Furthermore, there are a variety of data sources and methods of data collection. For an exploration mission, the seamless management of such data will enable a more medically autonomous crew than the current paradigm. The medical system requirements are being developed in parallel with the exploration mission architecture and vehicle design. ExMC has recognized that in order to make informed decisions about a medical data architecture framework, current methods for medical data management must not only be understood, but an architecture must also be identified that provides the crew with actionable insight to medical conditions. This medical data architecture will provide the necessary functionality to address the challenges of executing a self-contained medical system that approaches crew health care delivery without assistance from ground support. Hence, the products supported by current prototype development will directly inform exploration medical system requirements.

medical data architecture↗

NASA NTRS2019

Thermal Moonquake Location Algorithm and Analysis

What: Thermal moonquakes are small amplitude events that are produced by diurnal temperature changes. Why: Finding the locations of thermal moonquakes will lead to information about lunar surface processes. Where: Apollo 17 Lunar Seismic Profiling Experiment (LSPE) is able to locate thermal moonquake unlike Apollo 12-16. The primary science goal was an active source experiment to study the detailed structure of the lunar crust using 8 explosive packages (EPs). The secondary science goal was to passively listen for lunar seismic activity.

Phillips, Deanna↗

NASA NTRS2019

Satellite Salinity Observing System: Recent Discoveries and the Way Forward

Advances in L-band microwave satellite radiometry in the past decade, pioneered by ESA's SMOS and NASA's Aquarius and SMAP missions, have demonstrated an unprecedented capability to observe global sea surface salinity (SSS) from space. Measurements from these missions are the only means to probe the very-near surface salinity (top cm), providing a unique monitoring capability for the interfacial exchanges of water between the atmosphere and the upper-ocean, and delivering a wealth of information on various salinity processes in the ocean, linkages with the climate and water cycle, including land-sea connections, and providing constraints for ocean prediction models. The satellite SSS data are complimentary to the existing in situ systems such as Argo that provide accurate depiction of large-scale salinity variability in the open ocean but under-sample mesoscale variability, coastal oceans and marginal seas, and energetic regions such as boundary currents and fronts. In particular, salinity remote sensing has proven valuable to systematically monitor the open oceans as well as coastal regions up to approximately 40 km from the coasts. This is critical to addressing societally relevant topics, such as land-sea linkages, coastal-open ocean exchanges, research in the carbon cycle, near-surface mixing, and air-sea exchange of gas and mass. In this paper, we provide a community perspective on the major achievements of satellite SSS for the aforementioned topics, the unique capability of satellite salinity observing system and its complementarity with other platforms, uncertainty characteristics of satellite SSS, and measurement versus sampling errors in relation to in situ salinity measurements. We also discuss the need for technological innovations to improve the accuracy, resolution, and coverage of satellite SSS, and the way forward to both continue and enhance salinity remote sensing as part of the integrated Earth Observing System in order to address societal needs.

Vinogradova, Nadya↗

NASA NTRS2019

NASA Electrified Aircraft Propulsion Efforts

NASA's broad investments in Electrified Aircraft Propulsion (EAP) are reviewed in this paper. NASA investments are guided by an assessment of potential market impacts, technical key performance parameters, and technology readiness attained through a combination of studies, enabling fundamental research, and flight research. NASA has determined that the impact of EAP varies by market and NASA is considering three markets: national/international, on-demand mobility, and short haul regional air transport. Flight research is underway to demonstrate integrated solutions and inform standards and certification processes. This paper focuses on the vehicle related activities, however there are related NASA activities in air space management and vehicle autonomy activities as well as a breakthrough technology project called the Convergent Aeronautics Solutions Project. A key finding is that sufficient technical advances in key areas have been made which indicate EAP is a viable technology for aircraft. Significant progress has been made to reduce EAP adoption barriers and further work is needed to transition the technology to a commercial product and improve the technology so it is applicable to large transonic aircraft. This paper will review the activities of the Hybrid Gas Electric Subproject of the Advanced Air Transport Technology Project, the Revolutionary Vertical Lift Technology Project, and the X-57 Flight Demonstration Project, and discuss the potential EAP benefits for commercial and military applications.

Jansen, Ralph H.↗

NASA NTRS2019

An Overview of the Volatiles Investigating Polar Exploration Rover (VIPER) Mission

A critical goal to both science and exploration is to understand the form and location of lunar polar volatiles. The lateral and vertical distributions of these volatiles inform us of the processes that control the emplacement and retention of these volatiles, as well as helping to formulate in-situ resource utilization (ISRU) architectures. While significant progress has been made from orbital observations, measurements at a range of scales from centimeters to kilometers across the lunar surface are needed to generate adequate "volatile mineral models" for use in evaluating the resource potential of volatiles at the Moon. VIPER is a solar and battery powered rover mission designed to operate over multiple lunar days, traversing several kilometers as it continuously monitors for subsurface hydrogen and other surface volatiles. In specific thermal terrain types, including permanently shadowed terrain and locales that permit near-surface ice stability, subsurface samples will be examined for volatile content using a one-meter drill. This talk will provide an overview of the VIPER mission which is scheduled for flight to the Lunar South Pole in December 2022.

Colaprete, Anthony↗

NASA NTRS2020

Quantification of the Land Surface and Brown Ocean Influence on Tropical Cyclone Intensification Over Land

An investigation of TC Kelvin in February 2018 over northeast Australia was conducted to understand the mechanisms of the Brown Ocean effect (BOE), and to develop a comprehensive analysis framework for land falling tropical cyclones (TCs) in the process. NASA’s Land Information System (LIS) coupled to the NASA Unified WRF (NU-WRF) system was employed as the numerical model framework for 12 land/soil moisture perturbation experiments. Impacts of soil moisture and surface enthalpy flux conditions on TC Kelvin were investigated by closely evaluating simulated track and intensity, midlevel atmospheric thermodynamic properties, vertical wind shear, total precipitable water (TPW), and surface moisture flux. The results suggest that there were recognized differentiations among the sensitivity simulations as a result of land surface (e.g., soil moisture and texture) conditions. However, the intensification of TC Kelvin over land was more strongly related to atmospheric moisture advection and the diurnal cycle of solar radiation (i.e., radiative cooling) than to overall soil moisture conditions or surface fluxes. The analysis framework employed here for TC Kelvin can serve as a foundation to specifically quantify the factors governing the BOE. It also demonstrates that the BOE is not a binary influence (i.e. all or nothing), but instead operates in a continuum from largely to minimally influential such that it could be utilized to help improve prediction of inland effects for all land falling TCs.

Jinwoong Yoo↗

NASA NTRS2020

Fisher's Peak Ecological Forecasting - Mapping Biomass to Inform Conservation Planning of a Future State Park in Southern Colorado

Fisher’s Peak is a 77.5 km2 property southeast of Trinidad, Colorado that is planned to become Colorado’s newest state park. The area has experienced limited anthropogenic disturbance and is home to an abundance of unique habitats and species. A rapid, approximately 900 m change in elevation over the extent of the area nurtures a variety of plants and animals, including the endangered New Mexico meadow jumping mouse. In 2019, the State of Colorado obtained Fisher’s Peak with plans to make it Colorado’s second largest state park. A diverse group of collaborators, including the Colorado State Forest Service and The Nature Conservancy, worked closely to design the state park to maximize recreation opportunity while conserving the property’s rich habitats and biodiversity. The Fisher’s Peak Ecological Forecasting Team utilized Light Detection and Ranging (LiDAR) surveys, in situ forest inventory data, and Earth observations from Landsat 8 Operational Land Imager (OLI), Sentinel-1 C-band Synthetic Aperture Radar (C-SAR), Sentinel-2 Multispectral Instrument (MSI), Advanced Land Observing Satellite 2 (ALOS-2) Phased Array type L-band Synthetic Aperture Radar (PALSAR-2) and the Shuttle Radar Topography Mission (SRTM) to quantify and map biomass over the extent of the study area. The results from modeling biomass had an out-of-bag root mean square error of 55 Mg/ha and an R2 of 12. The resulting map indicates areas where carbon storage on the property is high, informing decision-making processes for future park development. While more in situ training data may improve modeling capacity for biomass in the Fisher’s Peak area, this work represents a feasible attempt to better understand biomass distribution using earth observation

Lauren Lad↗

NASA NTRS2020

Fisher's Peak Ecological Forecasting: Mapping Biomass to Inform Conservation Planning of a Future State Park in Southern Colorado

Fisher’s Peak is a 77.5 km2 property southeast of Trinidad, Colorado that is planned to become Colorado’s newest state park. The area has experienced limited anthropogenic disturbance and is home to an abundance of unique habitats and species. A rapid, approximately 900 m change in elevation over the extent of the area nurtures a variety of plants and animals, including the endangered New Mexico meadow jumping mouse. In 2019, the State of Colorado obtained Fisher’s Peak with plans to make it Colorado’s second largest state park. A diverse group of collaborators, including the Colorado State Forest Service and The Nature Conservancy, worked closely to design the state park to maximize recreation opportunity while conserving the property’s rich habitats and biodiversity. The Fisher’s Peak Ecological Forecasting Team utilized Light Detection and Ranging (LiDAR) surveys, in situ forest inventory data, and Earth observations from Landsat8 Operational Land Imager (OLI), Sentinel-1 C-band Synthetic Aperture Radar (C-SAR), Sentinel-2 Multispectral Instrument (MSI), Advanced Land Observing Satellite 2 (ALOS-2) Phased Array type L-band Synthetic Aperture Radar (PALSAR-2) and the Shuttle Radar Topography Mission (SRTM) to quantify and map biomass over the extent of the study area. The results from modeling biomass had an out-of-bag root mean square error of 55 Mg/ha and an R2 of 12. The resulting map indicates areas where carbon storage on the property is high, informing decision-making processes for future park development. While more in situ training data may improve modeling capacity for biomass in the Fisher’s Peak area, this work represents a feasible attempt to better understand biomass distribution using earth observation.

DEVELOP Tech Paper↗

NASA NTRS2020

Quantification of the Land Surface and Brown Ocean Influence on Tropical Cyclone Intensification Over Land

An investigation of TC Kelvin in February 2018 over northeast Australia was conducted to understand the mechanisms of the Brown Ocean effect (BOE), and to develop a comprehensive analysis framework for landfalling tropical cyclones (TCs) in the process. NASA’s Land Information System (LIS) coupled to the NASA Unified WRF (NU-WRF) system was employed as the numerical model framework for 12 land/soil moisture perturbation experiments. Impacts of soil moisture and surface enthalpy flux conditions on TC Kelvin were investigated by closely evaluating simulated track and intensity, midlevel atmospheric thermodynamic properties, vertical wind shear, total precipitable water (TPW), and surface moisture flux. The results suggest that there were recognized differentiations among the sensitivity simulations as a result of land surface (e.g., soil moisture and texture) conditions. However, the intensification of TC Kelvin over land was more strongly related to atmospheric moisture advection and the diurnal cycle of solar radiation (i.e., radiative cooling) than to overall soil moisture conditions or surface fluxes. The analysis framework employed here for TC Kelvin can serve as a foundation to specifically quantify the factors governing the BOE. It also demonstrates that the BOE is not a binary influence (i.e. all or nothing), but instead operates in a continuum from largely to minimally influential such that it could be utilized to help improve prediction of inland effects for all landfalling TCs.

Brown Ocean Effect↗

NASA NTRS2021

Noise Exposure Maps of Urban Air Mobility

A noise exposure map is “a scaled geographic depiction of an airport, its noise exposure contours, noise-sensitive facilities, and land uses in the airport surrounding area” developed in accordance with the FAA’s 14 Code of Federal Regulation Part 150. This paper is the first to explore the applicability of airport noise exposure maps to Urban Air Mobility (UAM). The FAA’s airport noise compatibility planning program is first described. Then the applicability of the noise exposure map to Urban Air Mobility (UAM) is explored. Finally, new airspace infrastructure, including vertiport locations and UAM routes from NASA’s UAM engineering simulations, and local noise-sensitive facility locations and land use information were collected and processed to develop the noise exposure maps of UAM near the Dallas-Fort Worth area. The DNL noise contours resulting from a six-passenger electric quadrotor prototype vehicle are predicted using NASA’s AIRNOISEUAM software. The noise exposure maps of UAM are generated automatically using Python’s data analysis and visualization libraries. The results have applications for UAM’s noise compatibility planning, noise-reducing route planning, and vertiport location selection

Urban Air Mobility↗

NASA NTRS2021

Climate Change Impacts and Adaptation for Dryland Farming Systems in Zimbabwe: A Stakeholder-Driven Integrated Multi-Model Assessment

Decision makers need accurate information to address climate variability and change and accelerate transformation to sustainability. A stakeholder-driven, science-based multi-model approach has been developed and used by the Agricultural Model Intercomparison and Improvement Project (AgMIP) to generate actionable information for adaptation planning processes. For a range of mid-century climate projections—likely to be hotter, drier, and more variable—contrasting future socio-economic scenarios (Representative Agricultural Pathways, RAPs) were co-developed with stakeholders to portray a sustainable development scenario and a rapid economic growth pathway. The unique characteristic of this application is the integration of a multi-modeling approach with stakeholder engagement to co-develop scenarios and adaptation strategies. Distribution of outcomes were simulated with climate, crop, livestock, and economic impact assessment models for smallholder crop livestock farmers in a typical dryland agro-ecological zone in Zimbabwe, characterized by low and erratic rainfall and nutrient depleted soils. Results showed that in Nkayi District, Western Zimbabwe, climate change would threaten most of the farms, and, in particular, those with large cattle herds due to feed shortages. Adaptation strategies that showed the most promise included diversification using legume production, soil fertility improvement, and investment in conducive market environments. The switch to more legumes in the farming systems reduced the vulnerability of the very poor as well as the more resourced farmers. Overall, the sustainable development scenario consistently addressed institutional failures and motivated productivity-enhancing, environmentally sound technologies and inclusive development approaches. This yielded more favorable outcomes than investment in quick economic wins from commercializing agriculture.

Climate change adaptation↗

NASA NTRS2021

Noise Exposure Map of Urban Air Mobility

A noise exposure map is “a scaled geographic depiction of an airport, its noise exposure contours, noise-sensitive facilities, and land uses in the airport surrounding area” developed in accordance with the FAA’s 14 Code of Federal Regulation Part 150. This paper is the first to explore the applicability of airport noise exposure maps to Urban Air Mobility (UAM). The FAA’s airport noise compatibility planning program is first described. Then the applicability of the noise exposure map to Urban Air Mobility (UAM) is explored. Finally, new airspace infrastructure, including vertiport locations and UAM routes from NASA’s UAM engineering simulations, and local noise-sensitive facility locations and land use information were collected and processed to develop the noise exposure maps of UAM near the Dallas-Fort Worth area. The DNL noise contours resulting from a six-passenger electric quadrotor prototype vehicle are predicted using NASA’s AIRNOISEUAM software. The noise exposure maps of UAM are generated automatically using Python’s data analysis and visualization libraries. The results have applications for UAM’s noise compatibility planning, noise-reducing route planning, and vertiport location selection. (To hear the voice please utilize the video uploaded to the record)

Urban Air Mobility↗

NASA NTRS2021

First Demonstration of Early Warning Gravitational-wave Alerts

Gravitational-wave observations became commonplace in Advanced LIGO-Virgo's recently concluded third observing run. 56 nonretracted candidates were identified and publicly announced in near real time. Gravitational waves from binary neutron star mergers, however, remain of special interest since they can be precursors to high-energy astrophysical phenomena like γ-ray bursts and kilonovae. While late-time electromagnetic emissions provide important information about the astrophysical processes within, the prompt emission along with gravitational waves uniquely reveals the extreme matter and gravity during—and in the seconds following—merger. Rapid communication of source location and properties from the gravitational-wave data is crucial to facilitate multimessenger follow-up of such sources. This is especially enabled if the partner facilities are forewarned via an early warning (pre-merger) alert. Here we describe the commissioning and performance of such a low-latency infrastructure within LIGO-Virgo. We present results from an end-to-end mock data challenge that detects binary neutron star mergers and alerts partner facilities before merger. We set expectations for these alerts in future observing runs.

Ryan Magee↗