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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 NTRSDate not supplied

Exploring our Changing Planet through NASA’s Earth Information Center and Novel Methods for Earth Science Communication

In June 2023, NASA unveiled the Earth Information Center (EIC), an interagency initiative which invites the global community to explore how our planet is changing through interactive installations, captivating visualizations, and novel storytelling. Existing in both physical and virtual space, the EIC serves as a gateway to actionable information collected through an expanding fleet of Earth observing satellites and sensors. The EIC features data driven visualizations, near real-time information, immersive experiences, and curated stories that highlight the applications of publicly available data to address environmental challenges across nine thematic areas: agriculture, biodiversity, disasters, greenhouse gases, air quality, sea level rise, sustainable energy, water resources, and wildfires. Designed by an interdisciplinary team, EIC exhibits are developed to reach a wide base of end-users across multiple learning modalities. During the first year of operation, the inaugural location of the EIC at NASA Headquarters in Washington, DC, welcomed an estimated 24,000 visitors, hosted over 145 tours for domestic and international organizations, supported NASA’s Earth Day 2024 programming, and led six STEM programs for diverse student communities. With lessons learned from the first year of being open to the public and a variety of novel science communication tools in development, the EIC is expanding the mission’s reach by collaborating with museums and visitor centers. Through these collaborations, we aim to inform a broader demographic about the unprecedented changes observed in Earth’s climate and inspire communities to learn more about their one and only home, planet Earth.

Nicole Ramberg-Pihl↗
NASA NTRSDate not supplied

Scaling a Smart Sub-metered Electrical Data Solution for the Center

Currently, there is no single Center-level point solution that enables facility systems data to be collected and stored from buildings on-site, on-demand, and through a faceted search capability that would enable the cultivation of deeper insights into incipient facility-related issues that would otherwise be overlooked. Such insights would represent a powerful and very valuable tool to aid in data-driven decision making and informing actionable steps for remediation and resolution of these problems. Access to electrically sub-metered data via hardware upgrades is in the process of being restored for Building N232, Sustainability Base, but this is only the first step. The native software capabilities that were originally part of a proposed Agencywide Smart Center initiative project can unlock the ability to access actionable insights. This work would also nicely complement many initiatives that are actively being pursued, under consideration, or pending submission to this same call at the Center. This project is poised to support an NRSAA with Verdigris Technologies, as well as in support of an Agencywide Smart Center initiative. Monitoring electrical consumption usage at the most granular level in Bldg. N232 will be instrumental in accurately quantifying potential utility costs and investments, and monitoring usage trends, as it is being converted into hoteling spaces, to help. This will also help with determining how the capability can scale more broadly across the Center and Agency at large.

Rodney Alexander Martin↗
NASA NTRSDate not supplied

Scaling a Smart Sub-metered Electrical Data Solution for the Center

Currently, there is no single Center-level point solution that enables facility systems data to be collected and stored from buildings on-site, on-demand, and through a faceted search capability that would enable the cultivation of deeper insights into incipient facility-related issues that would otherwise be overlooked. Such insights would represent a powerful and very valuable tool to aid in data-driven decision making and informing actionable steps for remediation and resolution of these problems. Access to electrically sub-metered data via hardware upgrades is in the process of being restored for Building N232, Sustainability Base, but this is only the first step. The native software capabilities that were originally part of a proposed Agencywide Smart Center initiative project can unlock the ability to access actionable insights. This work would also nicely complement many initiatives that are actively being pursued, under consideration, or pending submission to this same call at the Center. This project is poised to support an NRSAA with Verdigris Technologies, as well as in support of an Agencywide Smart Center initiative. Monitoring electrical consumption usage at the most granular level in Bldg. N232 will be instrumental in accurately quantifying potential utility costs and investments, and monitoring usage trends, as it is being converted into hoteling spaces, to help. This will also help with determining how the capability can scale more broadly across the Center and Agency at large.

Rodney Alexander Martin↗
NASA NTRS1985

A high-speed, large-capacity, 'jukebox' optical disk system

Two optical disk 'jukebox' mass storage systems which provide access to any data in a store of 10 to the 13th bits (1250G bytes) within six seconds have been developed. The optical disk jukebox system is divided into two units, including a hardware/software controller and a disk drive. The controller provides flexibility and adaptability, through a ROM-based microcode-driven data processor and a ROM-based software-driven control processor. The cartridge storage module contains 125 optical disks housed in protective cartridges. Attention is given to a conceptual view of the disk drive unit, the NASA optical disk system, the NASA database management system configuration, the NASA optical disk system interface, and an open systems interconnect reference model.

Ammon, G. J.↗
NASA NTRS1992

Menu-Driven Program Displays Data In Real Time

JPL/VIEW is menu-driven program retrieving and displaying incoming propagation data as they reach hard disk of data-acquisition-and-storage system. Real-time display enables operator to monitor progress of events and respond swiftly to errors during experiment or trial operation. Written in Microsoft C.

Mckeeman, John C.↗
NASA NTRS2024

On the Use of SMAP Soil Moisture for Forecasting NDVI Over CONUS Cropland Regions

Vegetation health forecasting (NDVI as a proxy) informs decision-makers about the end of season crop yield productivity but is not well-documented. This study tests improvements in vegetation health forecasting by developing a data-driven Dynamic Agricultural Productivity Indicator ( DAPI ), which simultaneously incorporates satellite-based root zone soil moisture (RZSM) and satellite-based NDVI data. RZSM is estimated via data assimilation of satellite based SMAP SM dataset. We employ the proposed DAPI forecast across four cropland types in CONUS, including corn, cotton, soybeans, and wheat. Results demonstrate superior performance of the DAPI forecasts compared to climatology-based NDVI forecasts, with the largest improvements in water-limited regions. DAPI shows particularly good performance during hydrologic disturbances such as floods and droughts. To this end, the DAPI approach is useful in estimating future vegetation health for identifying potential food-insecure areas, predicting crop price changes, and projecting expected commodities market trends.

Manh Le↗
NASA NTRS2009

Towards a Framework for Evaluating and Comparing Diagnosis Algorithms

Diagnostic inference involves the detection of anomalous system behavior and the identification of its cause, possibly down to a failed unit or to a parameter of a failed unit. Traditional approaches to solving this problem include expert/rule-based, model-based, and data-driven methods. Each approach (and various techniques within each approach) use different representations of the knowledge required to perform the diagnosis. The sensor data is expected to be combined with these internal representations to produce the diagnosis result. In spite of the availability of various diagnosis technologies, there have been only minimal efforts to develop a standardized software framework to run, evaluate, and compare different diagnosis technologies on the same system. This paper presents a framework that defines a standardized representation of the system knowledge, the sensor data, and the form of the diagnosis results and provides a run-time architecture that can execute diagnosis algorithms, send sensor data to the algorithms at appropriate time steps from a variety of sources (including the actual physical system), and collect resulting diagnoses. We also define a set of metrics that can be used to evaluate and compare the performance of the algorithms, and provide software to calculate the metrics.

Kurtoglu, Tolga↗
NASA NTRS1999

SIRTF Science Operations System Design

SIRTF Science Operations System Design William B. Green Manager, SIRTF Science Center California Institute of Technology M/S 310-6 1200 E. California Blvd., Pasadena CA 91125 (626) 395 8572 Fax (626) 568 0673 bgreen@ipac.caltech.edu. The Space Infrared Telescope Facility (SIRTF) will be launched in December 2001, and perform an extended series of science observations at wavelengths ranging from 20 to 160 microns for five years or more. The California Institute of Technology has been selected as the home for the SIRTF Science Center (SSC). The SSC will be responsible for evaluating and selecting observation proposals, providing technical support to the science community, performing mission planning and science observation scheduling activities, instrument calibration during operations and instrument health monitoring, production of archival quality data products, and management of science research grants. The science payload consists of three instruments delivered by instrument Principal Investigators located at University of Arizona, Cornell, and Harvard Smithsonian Astrophysical Observatory. The SSC is responsible for design, development, and operation of the Science Operations System (SOS) which will support the functions assigned to the SSC by NASA. The SIRTF spacecraft, mission profile, and science instrument design have undergone almost ten years of refinement. SIRTF development and operations activities are highly cost constrained. The cost constraints have impacted the design of the SOS in several ways. The Science Operations System has been designed to incorporate a set of efficient, easy to use tools which will make it possible for scientists to propose observation sequences in a rapid and automated manner. The use of highly automated tools for requesting observations will simplify the long range observatory scheduling process, and the short term scheduling of science observations. Pipeline data processing will be highly automated and data-driven, utilizing a variety of tools developed at JPL, the instrument development teams, and Space Telescope Science Institute to automate processing. An incremental ground data system development approach has been adopted, featuring periodic deliveries that are validated with the flight hardware throughout the various phases of system level development and testing. This approach minimizes development time and decreases operations risk. This paper will describe the top level architecture of the SOS and the basic design concepts. A summary of the incremental development approach will be presented. Examples of the unique science user tools now under final development prior to the first proposal call scheduled for mid-2000 will be shown.

Green, William↗
NASA NTRS2016

Consistency Between Sun-Induced Chlorophyll Fluorescence and Gross Primary Production of Vegetation in North America

Accurate estimation of the gross primary production (GPP) of terrestrial ecosystems is vital for a better understanding of the spatial-temporal patterns of the global carbon cycle. In this study,we estimate GPP in North America (NA) using the satellite-based Vegetation Photosynthesis Model (VPM), MODIS (Moderate Resolution Imaging Spectrometer) images at 8-day temporal and 500 meter spatial resolutions, and NCEP-NARR (National Center for Environmental Prediction-North America Regional Reanalysis) climate data. The simulated GPP (GPP (sub VPM)) agrees well with the flux tower derived GPP (GPPEC) at 39 AmeriFlux sites (155 site-years). The GPP (sub VPM) in 2010 is spatially aggregated to 0.5 by 0.5-degree grid cells and then compared with sun-induced chlorophyll fluorescence (SIF) data from Global Ozone Monitoring Instrument 2 (GOME-2), which is directly related to vegetation photosynthesis. Spatial distribution and seasonal dynamics of GPP (sub VPM) and GOME-2 SIF show good consistency. At the biome scale, GPP (sub VPM) and SIF shows strong linear relationships (R (sup 2) is greater than 0.95) and small variations in regression slopes ((4.60-5.55 grams Carbon per square meter per day) divided by (milliwatts per square meter per nanometer per square radian)). The total annual GPP (sub VPM) in NA in 2010 is approximately 13.53 petagrams Carbon per year, which accounts for approximately 11.0 percent of the global terrestrial GPP and is within the range of annual GPP estimates from six other process-based and data-driven models (11.35-22.23 petagrams Carbon per year). Among the seven models, some models did not capture the spatial pattern of GOME-2 SIF data at annual scale, especially in Midwest cropland region. The results from this study demonstrate the reliable performance of VPM at the continental scale, and the potential of SIF data being used as a benchmark to compare with GPP models.

photosynthesis model↗
NASA NTRSDate not supplied

Flow field Reconstruction for Inhomogeneous Turbulence using Data and Physics Driven Models

A methodology combining Large Eddy Simulation (LES) trained data and a physics driven wave packet model to obtain a reduced order reconstruction for broadband, three-dimensional, temporally stationary but spatially inhomogeneous, incompressible turbulence. Wake turbulence generated by an axisymmetric dragging disk with a turbulent co-flow serves as the benchmark test case. We begin by studying the proper-orthogonal decomposition of the turbulent fluctuations taken from a high-resolution LES to first identify whether the fields demonstrate a low-rank character. It is argued that the presence of the turbulent co-flow results in a largely broadband character lacking any tonal properties. This is especially true for Strouhal numbers greater than 1 and only a small fraction of energy is contained in the leading order Kelvin-Helmholtz modes. As such reconstructions and reduced order modeling purely relying on data from LES does not appear to be a lucrative solution - contrary to problems with strongly tonal character. To supplement the missing energy from a low order truncated mode expansion, we utilize a physics based super-resolution (enrichment) algorithm that relies on spatio-temporally localized Gabor wave packets whose time evolution is described using a set of ordinary differential equations. The reconstructed flow has single- and two-point correlations that are consistent with the reference high resolution simulation data.

SLS↗
NASA NTRS1988

OPSMODEL, an or-orbit operations simulation modeling tool for Space Station

The 'OPSMODEL' operations-analysis and planning tool simulates on-orbit crew operations for the NASA Space Station, furnishing a quantitative measure of the effectiveness of crew activities in various alternative Station configurations while supporting engineering and cost analyses. OPSMODEL is entirely data-driven; the top-down modeling structure of the software allows the user to control both the content and the complexity level of model definition during data base population. Illustrative simulation samples are given.

Davis, William T.↗
NASA NTRS2016

Decision Manifold Approximation for Physics-Based Simulations

With the recent surge of success in big-data driven deep learning problems, many of these frameworks focus on the notion of architecture design and utilizing massive databases. However, in some scenarios massive sets of data may be difficult, and in some cases infeasible, to acquire. In this paper we discuss a trajectory-based framework that quickly learns the underlying decision manifold of binary simulation classifications while judiciously selecting exploratory target states to minimize the number of required simulations. Furthermore, we draw particular attention to the simulation prediction application idealized to the case where failures in simulations can be predicted and avoided, providing machine intelligence to novice analysts. We demonstrate this framework in various forms of simulations and discuss its efficacy.

Wong, Jay Ming↗
NASA NTRS2015

Taxi Time Prediction at Charlotte Airport Using Fast-Time Simulation and Machine Learning Techniques

Accurate taxi time prediction can be used for more efficient runway scheduling to increase runway throughput and reduce taxi times and fuel consumptions on the airport surface. This paper describes two different approaches to predicting taxi times, which are a data-driven analytical method using machine learning techniques and a fast-time simulation-based approach. These two taxi time prediction methods are applied to realistic flight data at Charlotte Douglas International Airport (CLT) and assessed with actual taxi time data from the human-in-the-loop simulation for CLT airport operations using various performance measurement metrics. Based on the preliminary results, we discuss how the taxi time prediction accuracy can be affected by the operational complexity at this airport and how we can improve the fast-time simulation model for implementing it with an airport scheduling algorithm in real-time operational environment.

Lee, Hanbong↗
NASA NTRS2022

A Dynamic Landslide Hazard Monitoring Framework for the Lower Mekong Region

The Lower Mekong region is one of the most landslide-prone areas of the world. Despite the need for dynamic characterization of landslide hazard zones within the region, it is largely understudied for several reasons. Dynamic and integrated understanding of landslide processes requires landslide inventories across the region, which have not been available previously. Computational limitations also hamper regional landslide hazard assessment, including accessing and processing remotely sensed information. Finally, open-source software and modelling packages are required to address regional landslide hazard analysis. Leveraging an open-source data-driven global Landslide Hazard Assessment for Situational Awareness model framework, this study develops a region-specific dynamic landslide hazard system leveraging satellite-based Earth observation data to assess landslide hazards across the lower Mekong region. A set of landslide inventories were prepared from high-resolution optical imagery using advanced image-processing techniques. Several static and dynamic explanatory variables (i.e., rainfall, soil moisture, slope, relief, distance to roads, distance to faults, distance to rivers) were considered during the model development phase. An extreme gradient boosting decision tree model was trained for the monsoon period of 2015–2019 and the model was evaluated with independent inventory information for the 2020 monsoon period. The model performance demonstrated considerable skill using receiver operating characteristic curve statistics, with Area Under the Curve values exceeding 0.95. The model architecture was designed to use near-real-time data, and it can be implemented in a cloud computing environment (i.e., Google Cloud Platform) for the routine assessment of landslide hazards in the Lower Mekong region. This work was developed in collaboration with scientists at the Asian Disaster Preparedness Center as part of the NASA SERVIR Program’s Mekong hub. The goal of this work is to develop a suite of tools and services on accessible open-source platforms that support and enable stakeholder communities to better assess landslide hazard and exposure at local to regional scales for decision making and planning.

Nishan Kumar Biswas↗
NASA NTRS2019

Cognitive Communications for NASA Space Systems

The growing complexity of spacecraft constellations, communication relay offerings, and mission architectures drives the need for the development of autonomous communication systems. NASA has traditionally launched single spacecraft missions that are served by the Space Communication and Navigation (SCaN) program. Operations on SCaN networks are typically scheduled weeks in advance, and often each asset serves a single user spacecraft at a time. Recent movement towards swarm missions could make the current approach unsustainable. Additionally, the integration of commercial communication service providers will substantially increase the data transfer options available to new missions. NASA science missions have found benefit in launching swarms of spacecraft, allowing coordinated simultaneous observations from different perspectives. Inter-spacecraft communication (mesh networking) is an enabler for this architecture, as are CubeSats that allow cost-effective provisioning of distributed mission assets. As more complex swarm missions launch, one challenge is coordinating communication within the swarm and choosing the appropriate mechanism for telemetry, tracking, control, and data services to and from Earth. Cognitive communications research conducted by SCaN aims to mitigate the increasing communication complexity for mission users by increasing the autonomy of links, networks, and service scheduling. By considering automation techniques including recent advances in artificial intelligence and machine learning, cognitive algorithms and related approaches enable increased mission science return, improved resource utilization for service provider networks, and resiliency in unpredictable or unplanned environments. The Cognitive Communications Project at the NASA Glenn Research Center develops applications of data-driven, non-deterministic methods to improve the autonomy of space communication. The project emphasizes development of decentralized space networks with artificial intelligence agents optimizing communication link throughput, data routing, and system-wide asset management. This paper discusses the objectives, approaches, and opportunities of the research to address growing needs of the space communications community.

Chelmins, David↗
NASA NTRS2019

Cognitive Communications for NASA Space Systems

The Cognitive Communications Project at the NASA Glenn Research Center develops applications of data-driven, non-deterministic methods to improve the autonomy of space communication. The project emphasizes development of decentralized space networks with artificial intelligence agents optimizing communication link throughput, data routing, and system-wide asset scheduling. This paper discusses the objectives, approaches, and opportunities of the research to address growing needs of the space communications community.

Chelmins, David↗
NASA NTRS2022

A New Machine Learning Based Analysis for Improving Satellite Retrieved Atmospheric Composition Data: OMI SO2 as an Example

Despite recent progress, satellite retrievals of anthropogenic SO2 still suffer from relatively low signal-tonoise ratios. In this study, we demonstrate a new machine learning data analysis method to improve the quality of satellite SO2 products. In the absence of large ground-truth datasets for SO2, we start from SO2 slant column densities (SCDs) retrieved from the Ozone Monitoring Instrument (OMI) using a data-driven, physically based algorithm and calculate the ratio between the SCD and the root mean square (rms) of the fitting residuals for each pixel. To build the training data, we select presumably clean pixels with small SCD / rms ratios (SRRs) and set their target SCDs to zero. For polluted pixels with relatively large SRRs, we set the target to the original retrieved SCDs. We then train neural networks (NNs) to reproduce the target SCDs using predictors including SRRs for individual pixels, solar zenith, viewing zenith and phase angles, scene reflectivity, and O3 column amounts, as well as the monthly mean SRRs. For data analysis, we employ two NNs: (1) one trained daily to produce analyzed SO2 SCDs for polluted pixels each day and (2) the other trained once every month to produce analyzed SCDs for less polluted pixels for the entire month. Test results for 2005 show that our method can significantly reduce noise and artifacts over background regions. Over polluted areas, the monthly mean NN-analyzed and original SCDs generally agree to within ±15 %, indicating that our method can retain SO2 signals in the original retrievals except for large volcanic eruptions. This is further confirmed by running both the NN-analyzed and original SCDs through a topdown emission algorithm to estimate the annual SO2 emissions for ∼ 500 anthropogenic sources, with the two datasets yielding similar results. We also explore two alternative approaches to the NN-based analysis method. In one, we employ a simple linear interpolation model to analyze the original SCD retrievals. In the other, we develop a PCA–NN algorithm that uses OMI measured radiances, transformed and dimension-reduced with a principal component analysis (PCA) technique, as inputs to NNs for SO2 SCD retrievals. While the linear model and the PCA–NN algorithm can reduce retrieval noise, they both underestimate SO2 over polluted areas. Overall, the results presented here demonstrate that our new data analysis method can significantly improve the quality of existing OMI SO2 retrievals. The method can potentially be adapted for other sensors and/or species and enhance the value of satellite data in air quality research and applications.

Can Li↗