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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 703 records · Page 39
All sourcesNASA NTRSDOE OSTI
NASA NTRS2015

Deriving Scaling Factors Using a Global Hydrological Model to Restore GRACE Total Water Storage Changes for China's Yangtze River Basin

This study used a global hydrological model (GHM), PCR-GLOBWB, which simulates surface water storage changes, natural and human induced groundwater storage changes, and the interactions between surface water and subsurface water, to generate scaling factors by mimicking low-pass filtering of GRACE signals. Signal losses in GRACE data were subsequently restored by the scaling factors from PCR-GLOBWB. Results indicate greater spatial heterogeneity in scaling factor from PCR-GLOBWB and CLM4.0 than that from GLDAS-1 Noah due to comprehensive simulation of surface and subsurface water storage changes for PCR-GLOBWB and CLM4.0. Filtered GRACE total water storage (TWS) changes applied with PCR-GLOBWB scaling factors show closer agreement with water budget estimates of TWS changes than those with scaling factors from other land surface models (LSMs) in China's Yangtze River basin. Results of this study develop a further understanding of the behavior of scaling factors from different LSMs or GHMs over hydrologically complex basins, and could be valuable in providing more accurate TWS changes for hydrological applications (e.g., monitoring drought and groundwater storage depletion) over regions where human-induced interactions between surface water and subsurface water are intensive.

water↗
NASA NTRS2016

Automated Storm Tracking and the Lightning Jump Algorithm Using GOES-R Geostationary Lightning Mapper (GLM) Proxy Data

This study develops a fully automated lightning jump system encompassing objective storm tracking, Geostationary Lightning Mapper proxy data, and the lightning jump algorithm (LJA), which are important elements in the transition of the LJA concept from a research to an operational based algorithm. Storm cluster tracking is based on a product created from the combination of a radar parameter (vertically integrated liquid, VIL), and lightning information (flash rate density). Evaluations showed that the spatial scale of tracked features or storm clusters had a large impact on the lightning jump system performance, where increasing spatial scale size resulted in decreased dynamic range of the system's performance. This framework will also serve as a means to refine the LJA itself to enhance its operational applicability. Parameters within the system are isolated and the system's performance is evaluated with adjustments to parameter sensitivity. The system's performance is evaluated using the probability of detection (POD) and false alarm ratio (FAR) statistics. Of the algorithm parameters tested, sigma-level (metric of lightning jump strength) and flash rate threshold influenced the system's performance the most. Finally, verification methodologies are investigated. It is discovered that minor changes in verification methodology can dramatically impact the evaluation of the lightning jump system.

lightning jump↗
NASA NTRS2018

A Potential Venera-D Element: SAEVe (Seismic and Atmospheric Exploration of Venus)

The Seismic and Atmospheric Exploration of Venus (SAEVe) mission concept study developed a long duration Venus lander concept that addressed high priority Venus science that includes seismology as well and long duration atmospheric measurements. SAEVe is a compact lander concept based on LLISSE (Long-Life In-situ Solar System Explorer) developments and capability.

Venus↗
NASA NTRS2019

A Robust Vision-Based Algorithm for Detecting and Classifying Small Orbital Debris Using On-Board Optical Cameras

This study develops a vision-based detection and classification algorithm to address the challenges of in-situ small orbital debris environment classification including debris observability and instrument requirements for small debris observation. The algorithm operates in near real time and is robust under difficult tasks in moving objects classification such as multiple moving objects, objects with various movement trajectories and speeds, very small or faint objects, and substantial background motion. The performance of the algorithm is optimized and validated using space image data available through simulated environments generated using NASA Marshall Space Flight Centers Dynamic Star Field Simulator of on-board optical sensors and cameras.

Zamani, Yasin↗
NASA NTRS2019

Citizen Science Twitter Data Management for Earth Science Applications

Social media data can provide useful real-time and historical information relating to the natural world, but managing this data poses challenges. Scientists at GES DISC are exploring the potential of Twitter data to augment precipitation data from the Global Precipitation Measurement (GPM) mission. However, the format of Twitter data is unconventional in the context of NASA data centers, resulting in frustration for scientists who need to work with the data. This study investigated procedures and standards needed to properly manage Twitter data to make them compatible with these data centers. After comparing databases, the study found that the MongoDB database was best suited for the storage of raw Twitter data due to its flexibility, ability to be accessed by multiple users, and querying functionality. The study used the Python package Zarr to transform processed Twitter data into a gridded format similar to that of satellite data. Each Tweet was mapped onto a time-space grid; each grid location contained information about Tweet attributes and precipitation. The study developed a pipeline for downloading, storing, and gridding Twitter data and transformed Twitter data into an understandable format for users of NASA satellite data.

Li, Rachel↗
NASA NTRS2021

Linear and Nonlinear Trend Analyzes in Global Satellite‐Based Precipitation, 1998–2017

Precipitation varies spatio‐temporally in amount, intensity, and frequency. Although, much research has been conducted on analyzing precipitation patterns and variation at the global scale, trend types have still not received much attention. This study developed a new polynomial‐based model for detecting nonlinear and linear trends in a satellite precipitation product (TRMM 3B43) for the 1998–2017 period at a near‐global scale. We used an automated trend classification method that detects significant trends and classifies them into linear and nonlinear (cubic, quadratic, and concealed) trend types in satellite‐based precipitation at near‐global, continental, and climate zone scales. We found that 12.3% of pixel‐based precipitation time series across the globe have significant trend at 0.05 significance level (50% positive and 50% negative trends). In all continents except Asia, decreasing trends were found to cover larger areas than corresponding increasing trends. Regarding climate zone and precipitation trend change, our results indicate that a linear trend is dominant in the warm temperate (77.7%) and equatorial climates (80.4%) while the least linear change was detected in the polar climate (68.9%). The combined results of continental and climate zone scales indicate significant increasing trends in Asia and arid climate over the last 20 yr. Furthermore, positive trends were found to be more significant at the continental scale, particularly, in Asia relative to the climate zone scale. Linear change in precipitation (80%) was the most dominant trend observed as opposed to nonlinear (quadratic [11%] and cubic [9%]) trend types at the global scale.

Majid Kazemzadeh↗
NASA NTRS2021

An Assurance Case with a Model at its Core

We describe our pilot study development of an Assurance Case arguing the robustness of a spacecraft demonstration of optical communication. Our Assurance Case addresses the concern that optical communication may be interrupted by the presence of cloud cover, threatening the success of the demonstration. Central to our Assurance Case is its use of a model of atmospheric attenuation to support a key portion of the robustness argument. The conclusion for the demonstration is that its schedule slack plus ability to store data for transmission later accommodates occasional weather-caused atmospheric attenuation.We indicate how the overall structure of the Assurance Case derives from the Objectives Hierarchy set forth in the NASA Reliability and Maintainability standard. We then present the portions of the Assurance Case that argue (1) the atmospheric attenuation model is sufficiently accurate, (2) application of the model shows the desired robustness of the demonstration, and (3) all the model assumptions are satisfied in its application. Lastly, we suggest how an engineering model of the demonstration system and its use could inform the development of the Assurance Case encompassing additional plausible hazards.

DiVenti, Anthony↗
NASA NTRS2022

Above-Aircraft Cirrus Cloud and Aerosol Optical Depth From Hyperspectral Irradiances Measured By a Total-Diffuse Radiometer

This study develops the use of spectral total and diffuse irradiance measurements, made from a prototype hyperspectral total-diffuse sunshine pyranometer (SPN-S), to retrieve layer fine-mode aerosol (τ aer ) and total optical depths from airborne platforms. Additionally, we use spectral analysis in an attempt to partition the total optical depth into its τ aer and cirrus cloud optical depth (τ cld ) components in the absence of coarse-mode aerosols. Two retrieval methods are developed: one leveraging information in the diffuse irradiance and the other using spectral characteristics of the transmitted direct beam, with each approach best suited for specific cloud and aerosol conditions. The SPN-S has advantages over traditional sun photometer systems, including no moving parts and a low cost. However, a significant drawback of the instrument is that it is unable to measure the direct-beam irradiance as accurately as sun photometers. To compensate for the greater measurement uncertainty in the radiometric irradiances, these retrieval techniques employ ratioed inputs or spectral information to reduce output uncertainty. This analysis uses irradiance measurements from the SPN-S and the solar spectral flux radiometer (SSFR) aboard the National Aeronautics and Space Administration’s (NASA) P-3 aircraft during the 2018 deployment of the ObseRvations of Aerosols above CLouds and their intEractionS (ORACLES) campaign and the 2019 Cloud, Aerosol and Monsoon Processes Philippines Experiment (CAMP 2 Ex) mission to quantify above-aircraft cirrus τ cld and derive vertical profiles of layer τ aer . Validation of the τ aer retrieval is accomplished by comparison with co-located measurements of direct solar irradiance made by the Sky-Scanning Sun-Tracking Atmospheric Research (4STAR) and in situ measurements of aerosol optical depth. For the aggregated 2018 ORACLES results, regression between the SPN-S-based method and sun photometer τ aer values yields a slope of 0.96 with an R 2 of 0.96, while the root mean square error (RMSE) is 3.0 x 10 -2 . When comparing the retrieved τ aer to profiles of integrated in situ measurements of optical extinction, the slope, R 2 , and RMSE values for ORACLES are 0.90, 0.96, and 3.4 x 10 -2 , and for CAMP 2 Ex they are 0.94, 0.97, and 3.4 x 10 -2 , respectively. This paper is a demonstration of methods for deriving cloud and aerosol optical properties in environments where both atmospheric constituents may be present. With improvements to the low-cost SPN-S radiometer instrument, it may be possible to extend these methods to a broader set of sampling applications, such as ground-based settings.

spectral instrument (SPN-S)↗
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 NTRS2023

Improving the MODIS LAI Compositing Using Prior Time-Series Information

The Moderate Resolution Imaging Spectroradiometer (MODIS) long-term leaf area index (LAI) products have significantly contributed to global energy fluxes, climate change, and biogeochemistry research. However, the maximum fraction of photosynthetically active radiation absorbed by vegetation (Max-FPAR) compositing strategy of the Collection 6 (C6) products dictates that the main or backup algorithm is always triggered by observations of different quality, which indirectly causes the observed instability in the LAI time-series. Based on MODIS daily LAI retrievals, this study develops a prior knowledge time-series compositing algorithm (PKA) using a linear kernel driven (LKD) model. Our results show that the newly proposed PKA can significantly improve the LAI composites compared to the Max-FPAR strategy using ground-based observations for validation. We found that the PKA performs better than Max-FPAR in various aspects (different sites, seasons, and retrieval index (RI) ranges), with R2 increasing from 0.69 to 0.76 and root means square error (RMSE) decreasing from 1.01 to 0.84 compared to GBOV ground truth. The same improvement was shown for the ground truth LAIs measured at the Honghe and Hailun sites in northeastern China, with R2 increasing from 0.23 to 0.41 and RMSE decreasing from 1.27 to 1.25. In addition, three newly proposed temporal uncertainty metrics (time-series stability, TSS and time-series anomaly, TSA and reconstruction error metric, RE (the proximity to the main RT-based retrievals)) were applied to compare the stability of LAI time-series before and after PKA implementation. We found that the time series stability of PKA LAI was improved, the time series anomalies were reduced, and the retrieval rates of the main algorithm were also greatly enhanced compared to Max-FPAR LAI. A case intercomparison for Max-FPAR-MODIS, Max-FPAR-VIIRS (Visible Infrared Imager Radiometer Suite), and PKA-MODIS LAIs in the Amazon Forest region showed that the PKA is also effective in improving the LAI retrieval over large regions with few qualified observations due to poor atmospheric conditions (RE decreased from 2.37/2.35 (Max-FPAR-MODIS/Max-FPAR-VIIRS) to 2.25 (PKA-MODIS) and RI increased from 61.94%/59.62% to 66.88%). The same improvement was seen in the BELMANIP 2.1 sites for almost all biomes except deciduous broadleaf forest, where the RE decreased from 1.85/2.13 to 1.15 overall. We note that the PKA has the potential to be easily implemented in the operational algorithms of subsequent MODIS and MODIS-like LAI Collections.

MODIS↗
NASA NTRS2022

Final Approach Navigation to Europa: Setting Up for a Successful Landing

Jupiter's moon Europa has been a prime target in the search for extraterrestrial life since the Galileo orbiter indicated that a saltwater ocean may exist beneath its icy crust. The proposed Europa Lander mission targets revolutionary science investigations on the surface of Europa with the main objective to search for evidence of life. Europa presents unique challenges to a landing mission because of its hostile radiation environment and the lack of information about its terrain. This paper provides an overview of Europa Lander Navigation-Deorbit, Descent, and Landing (DDL) technology development study, which is focused on the interface between ground navigation operations and onboard DDL behavior and the challenges associated with landing a probe on Europa.

McElrath, Tim↗
NASA NTRS2023

Statistical Analysis of Equatorial Plasma Bubbles Climatology and Multi-Day Periodicity Using GOLD Observations

This study develops a new Bubble Index to quantify the intensity of 2-D postsunset equatorial plasma bubbles (EPBs) in the American/Atlantic sector, using Global-scale Observations of the Limb and Disk (GOLD) nighttime data. A climatology and day-to-day variability analysis of EPBs is conducted based on the newly-derived Bubble Index with the following results: (a) EPBs show considerable seasonal and solar activity dependence, with stronger (weaker) intensity around December (June) solstice and high (low) solar activity years. (b) EPBs exhibit opposite geomagnetic activity dependencies during different storm phases: EPBs are intensified concurrently with an increasing Kp, but are suppressed with high Kp occurring 3–6 hr earlier. (c) For the first time, we found that EPBs' day-to-day variation exhibited quasi-3-day and quasi-6-day periods. A coordinated analysis of Ionospheric Connection Explorer (ICON) winds and ionosonde data suggests that this multi-day periodicity was related to the planetary wave modulation through the wind-driven dynamo.

Ercha Aa↗
NASA NTRSDate not supplied

Process Development and Testing for Lunar Habitats

Non-metallic composites are appealing for Lunar habitats due to their low specific weight and contribution to passive radiation shielding. This application of composite materials presents notable challenges, however: flammability risks are higher due to elevated oxygen levels expected in habitat structures and absorbed radiation can cause damage long-term. The Materials and Processes Laboratory at Marshall Space Flight Center awarded funding for an internal research and development study which included fabrication and testing of composite materials for Lunar habitats. Composite layups used carbon fiber or ultra-high molecular weight polyethylene reinforcement and a variety of polymer matrices. Pre-impregnated fabric was autoclave cured; however, novel coatings were incorporated during select layups. Samples from each panel were used for screening-level flammability testing (ASTM D 2863) and combined vacuum ultraviolet and electron irradiation followed by tension testing (ASTM D5766). Two of the tested composite materials exceeded a 37% limiting oxygen index. Material property degradation for a 10 year simulated radiation dose was minimal in most cases.

manufacturing↗
NASA NTRSDate not supplied

Exploring Flooded Fraction Prediction through Machine Learning Models Focusing on Medical Infrastructure in the Southeast U.S. Coastal Areas

Rising sea levels due to climate change increasingly threaten medical infrastructure through flooding. This study develops machine learning models to predict flood exposure for 11,508 medical facilities in the southeastern coastal regions of the United States by integrating datasets including meteorological, hydrological, topographic, and geological data, the Natural Risk Index, and historical flood records from NASA, HIFLD, and FEMA. Six regression models, namely Linear Regression, Support Vector Regression, Random Forest, k-Nearest Neighbors, XGBoost, and Artificial Neural Networks, are trained using 16 explanatory variables identified through literature review and correlation analysis. Data preprocessing employs the SMOGN for class imbalance and Winsorization for outliers. Model performance is evaluated using MAE, MSE, and RMSE, with Random Forest and XGBoost models achieving the highest performance (MSE of 2.58e-5 and 3.69e-5, respectively). This multifactorial approach allows the models to capture complex flood-influencing relationships, enhancing adaptability and performance across geographic regions. Future work focuses on expanding across the U.S. and developing a near real-time flood monitoring system.

Jihoon Chung↗
DOE OSTI2016

Argonne News Brief: First Sensors for Chicago’s “City Fitness Tracker”

This week in Chicago, the Array of Things team installed its first 50 nodes on city streets to kick off the groundbreaking urban sensing project. Array of Things is designed as a “fitness tracker” for the city, collecting new streams of data on Chicago’s environment, infrastructure, and activity. This hyper-local, open data can help researchers, city officials, and software developers study and address critical city challenges, such as preventing urban flooding, improving traffic safety and air quality, and assessing the nature and impact of climate change.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2015

Dynamic Recrystallization Model for Whisker and Hillock Growth

Tin (Sn) whiskers are not a recent development. Studies in the late 1930’s investigated thin filaments that grew spontaneously from Sn coatings used for the corrosion protection of electronic hardware. It was soon recognized that these Sn filaments, or whiskers, could create short circuits in the same electronic equipment. Figure 1a illustrates whisker growth in the hole of a printed circuit board having an immersion Sn surface finish. The engineering solution was to contaminate the Sn with > 3 wt.% of lead (Pb). The result was that whisker growth was replaced with hillock formation (Fig. 1b) that posed a minimal reliability concern to electrical circuits. Today, Pb-containing finishes are being replaced with pure Sn coatings to meet environmental restrictions on Pb use. The same short-circuit concerns have been raised, once again, with respect to Sn whiskers.

Vianco, Paul T.↗
DOE OSTI2020

Dynamic Modelling and Performance Evaluation of a Chemisorption Heat Pump for Cold Climate

Space heating is a significant contributor to the energy consumption in residential buildings, especially in cold climates where electric-driven heat pumps lose the advantage of high efficiency and conventional gas-combustion furnaces have limited efficiency. Chemical adsorption (chemisorption) heat pumps (CSHP) have the potential for producing heating with high energy efficiency under cold operating conditions. A CSHP operates intermittently between desorption and adsorption modes, and their performance is affected by system design and transient system control. This study developed a dynamic model and used it to evaluate the daily system performance under different designs and controls, particularly the buffer tank size and the controlled minimum heating power. The simulation results revealed the effects of these parameters on the system performance and helped identify the optimal design and control for the highest coefficient of performance and heating capacity.

Yang, Zhiyao↗